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@nuralogix.ai/awc-tfjs-face-tracker

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Anura Web Core TensorFlow.js Face Tracker Plugin

docs.deepaffex.ai/jsapiclient/

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/** * @license * Anura Web Core TensorFlow.js Face Tracker Plugin * Copyright Nuralogix, 2022 * https://docs.deepaffex.ai/jsapiclient/ * Version: 1.0.0 * Published on 2022-05-18, 6:57:20 a.m. (EST) */ /** * @license * Copyright 2022 Google LLC. All Rights Reserved. * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. * ============================================================================= */ export const version = "1.0.0"; export const faceTrackerName = "TFjs_WebGL_FaceTracker"; export const workerCode = "function e(e,t){return t.forEach((function(t){t&&\"string\"!=typeof t&&!Array.isArray(t)&&Object.keys(t).forEach((function(n){if(\"default\"!==n&&!(n in e)){var r=Object.getOwnPropertyDescriptor(t,n);Object.defineProperty(e,n,r.get?r:{enumerable:!0,get:function(){return t[n]}})}}))})),Object.freeze(e)}class t{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}}class n{refCount(e){return r(\"refCount\")}incRef(e){return r(\"incRef\")}timerAvailable(){return!0}time(e){return r(\"time\")}read(e){return r(\"read\")}readSync(e){return r(\"readSync\")}readToGPU(e,t){return r(\"readToGPU\")}numDataIds(){return r(\"numDataIds\")}disposeData(e,t){return r(\"disposeData\")}write(e,t,n){return r(\"write\")}move(e,t,n,a,s){return r(\"move\")}memory(){return r(\"memory\")}floatPrecision(){return r(\"floatPrecision\")}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}dispose(){return r(\"dispose\")}}function r(e){throw new Error(`'${e}' not yet implemented or not found in the registry. 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To get the original bytes, call tensor.bytes().\")}return e}async bytes(){this.throwIfDisposed();const e=await Xe().read(this.dataId);return\"string\"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Xe().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error(\"Tensor is disposed.\")}print(e=!1){return Ye.print(this,e)}clone(){return this.throwIfDisposed(),Ye.clone(this)}toString(e=!1){return ze(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ye.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Xe().makeVariable(this,e,t,n)}}Object.defineProperty(Qe,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),W(\"Tensor\",(()=>Qe));class Ze extends Qe{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!d(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Xe().disposeTensor(this),this.dataId=e.dataId,Xe().incRef(this,null)}dispose(){Xe().disposeVariable(this),this.isDisposedInternal=!0}}var Je,et,tt,nt,rt;Object.defineProperty(Ze,Symbol.hasInstance,{value:e=>e instanceof Qe&&null!=e.assign&&e.assign instanceof Function}),function(e){e.R0=\"R0\",e.R1=\"R1\",e.R2=\"R2\",e.R3=\"R3\",e.R4=\"R4\",e.R5=\"R5\",e.R6=\"R6\"}(Je||(Je={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"int32\",e.complex64=\"complex64\"}(et||(et={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"bool\",e.complex64=\"complex64\"}(tt||(tt={})),function(e){e.float32=\"float32\",e.int32=\"float32\",e.bool=\"float32\",e.complex64=\"complex64\"}(nt||(nt={})),function(e){e.float32=\"complex64\",e.int32=\"complex64\",e.bool=\"complex64\",e.complex64=\"complex64\"}(rt||(rt={}));const at={float32:nt,int32:et,bool:tt,complex64:rt};function st(e,t){if(\"string\"===e||\"string\"===t){if(\"string\"===e&&\"string\"===t)return\"string\";throw new Error(`Can not upcast ${e} with ${t}`)}return at[e][t]}function ot(e){return st(e,\"int32\")}function it(e,t){if(e.dtype===t.dtype)return[e,t];const n=st(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function ut(e){const t=[];return lt(e,t,new Set),t}function lt(e,t,n){if(null==e)return;if(e instanceof Qe)return void t.push(e);if(r=e,!Array.isArray(r)&&\"object\"!=typeof r)return;var r;const a=e;for(const e in a){const r=a[e];n.has(r)||(n.add(r),lt(r,t,n))}}function ct(e){return null!=e.kernelName}class pt{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class dt{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new pt}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(null==this.backendInstance){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(z(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(null==this.registryFactory[e])throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,null==this.registry[e]){this.backendInstance=null;const{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 Ve(this.backendInstance),!0}setupRegisteredKernels(){q(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){q(e).forEach((t=>{null!=t.disposeFunc&&t.disposeFunc(this.registry[e])}))}initializeBackend(e){const t=this.registryFactory[e];if(null==t)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const r=t.factory();if(!r||r instanceof n||\"function\"!=typeof r.then)return this.registry[e]=r,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,n=r.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,z(`Initialization of backend ${e} failed`),z(n.stack||n.message)),!1)));return this.pendingBackendInit=n,{success:n,asyncInit:!0}}}catch(t){return z(`Initialization of backend ${e} failed`),z(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&null!=this.pendingBackendInit&&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(0===Object.keys(this.registryFactory).length)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(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,r=null;if(null==t){if(\"function\"!=typeof e)throw new Error(\"Please provide a function to tidy()\");t=e}else{if(\"string\"!=typeof e&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(\"function\"!=typeof t)throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");r=e}return this.scopedRun((()=>this.startScope(r)),(()=>this.endScope(n)),(()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n)))}scopedRun(e,t,n){e();try{const e=n();return t(),e}catch(e){throw t(),e}}nextTensorId(){return dt.nextTensorId++}nextVariableId(){return dt.nextVariableId++}clone(e){const t=ft.runKernel(\"Identity\",{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e},n={dtype:\"float32\"};return ft.runKernel(\"Cast\",t,n)}})),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=j(e,this.backendName)))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){const r=this.backend.numDataIds();let a=0;n.forEach((e=>{a+=\"complex64\"===e.dtype?3:1}));const s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-a-s;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;let o,u;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=ct(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:\"\";if(ct(e)){const{kernelName:t,inputs:a,attrs:s}=e;null==this.backendName&&this.backend;const l=j(t,this.backendName);i(null!=l,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),o=()=>{const e=this.backend.numDataIds();u=l.kernelFunc({inputs:a,attrs:s,backend:this.backend});const o=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,o);const i=o.map((e=>{if(null!=e.rank)return e;const{dataId:t,shape:n,dtype:r}=e;return this.makeTensorFromDataId(t,n,r)}));if(r){const e=this.getTensorsForGradient(t,a,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,a=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};o=()=>{const e=this.backend.numDataIds();u=this.tidy((()=>t(this.backend,a)));const n=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:c,attrs:p}=e,d=ct(e)?null:e.backwardsFunc;let h;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool(\"DEBUG\")||this.state.profiling?(h=this.profiler.profileKernel(l,c,(()=>o())),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(h),t=h.outputs):t=o()})),r&&this.addTapeNode(l,c,t,d,n,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map((e=>null!=c[e]?c[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(u)?t:t[0]}saveTensorsForBackwardMode(e){const t=e.map((e=>this.keep(this.clone(e))));return t}getTensorsForGradient(e,t,n){const r=K(e);if(null!=r){const e=r.inputsToSave||[],a=r.outputsToSave||[];let s;r.saveAllInputs?(i(Array.isArray(t),(()=>\"saveAllInputs is true, expected inputs to be an array.\")),s=Object.keys(t).map((e=>t[e]))):s=e.map((e=>t[e]));const o=n.filter(((e,t)=>a[t]));return s.concat(o)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",r=r||this.backend;let a=e;\"string\"===n&&N(e[0])&&(a=e.map((e=>Pe(e))));const s=r.write(a,t,n),o=new Qe(t,n,s,this.nextTensorId());if(this.trackTensor(o,r),\"string\"===n){const e=this.state.tensorInfo.get(s),t=function(e){if(null==e)return 0;let t=0;return e.forEach((e=>t+=e.length)),t}(a);this.state.numBytes+=t-e.bytes,e.bytes=t}return o}makeTensorFromDataId(e,t,n,r){const a=new Qe(t,n=n||\"float32\",e,this.nextTensorId());return this.trackTensor(a,r),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const a=new Ze(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[a.name])throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,\"string\"===e.dtype&&this.state.numStringTensors++;let n=0;\"complex64\"!==e.dtype&&\"string\"!==e.dtype&&(n=e.size*$(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 Ze||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;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,\"string\"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),\"complex64\"!==e.dtype&&\"string\"!==e.dtype){const t=e.size*$(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const 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,null==e.reasons&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;const 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((e=>e.totalBytesSnapshot))),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const e of this.state.activeProfile.kernels)e.kernelTimeMs=await e.kernelTimeMs,e.extraInfo=await e.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,a,s){const o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},i=K(e);null!=i&&(r=i.gradFunc),null!=r&&(o.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],r=_(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e})),r(e.length>1?e:e[0],a,s))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=ut(e),n=new Set(t.map((e=>e.id)));for(let e=0;e<this.state.activeScope.track.length;e++){const t=this.state.activeScope.track[e];t.kept||n.has(t.id)||t.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach((e=>{e.kept||e.scopeId!==r.id||this.track(e)}))}gradients(e,t,n,r=!1){if(i(t.length>0,(()=>\"gradients() received an empty list of xs.\")),null!=n&&\"float32\"!==n.dtype)throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const a=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy(\"forward\",e)));i(a instanceof Qe,(()=>\"The result y returned by f() must be a tensor.\"));const s=function(e,t,n){const r={},a={};for(let e=0;e<t.length;e++)r[t[e].id]=!0;for(let n=0;n<e.length;n++){const s=e[n],o=s.inputs;for(const e in o){const n=o[e];let i=!1;for(let e=0;e<t.length;e++)if(r[n.id]){s.outputs.forEach((e=>r[e.id]=!0)),i=!0,a[s.id]=!0;break}if(i)break}}const s={};s[n.id]=!0;const o={};for(let t=e.length-1;t>=0;t--){const n=e[t],r=n.inputs;for(let e=0;e<n.outputs.length;e++)if(s[n.outputs[e].id]){for(const e in r)s[r[e].id]=!0,o[n.id]=!0;break}}const i=[];for(let t=0;t<e.length;t++){const n=e[t];if(a[n.id]&&o[n.id]){const e={};for(const t in n.inputs){const a=n.inputs[t];r[a.id]&&(e[t]=a)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,i.push(t)}}return i}(this.state.activeTape,t,a);if(!r&&0===s.length&&t.length>0)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.\");return this.tidy(\"backward\",(()=>{const e={};e[a.id]=null==n?function(e){const t=R(p(e),\"float32\");return ft.makeTensor(t,e,\"float32\")}(a.shape):n,function(e,t,n,r){for(let a=t.length-1;a>=0;a--){const s=t[a],o=[];if(s.outputs.forEach((t=>{const n=e[t.id];null!=n?o.push(n):o.push(null)})),null==s.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);const i=s.gradient(o);for(const t in s.inputs){if(!(t in i))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(i)}.`);const a=n((()=>i[t]()));if(\"float32\"!==a.dtype)throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${a.dtype}'`);const o=s.inputs[t];if(!d(a.shape,o.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${t}' has shape '${a.shape}', which does not match the shape of the input '${o.shape}'`);if(null==e[o.id])e[o.id]=a;else{const t=e[o.id];e[o.id]=r(t,a),t.dispose()}}}}(e,s,(e=>this.tidy(e)),mt);const r=t.map((t=>e[t.id]));return 0===this.state.gradientDepth&&(this.state.activeTape.forEach((e=>{for(const t of e.saved)t.dispose()})),this.state.activeTape=null),{value:a,grads:r}}))}customGrad(e){return i(C(e),(()=>\"The f passed in customGrad(f) must be a function.\")),(...t)=>{let n;i(t.every((e=>e instanceof Qe)),(()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\"));const r={};t.forEach(((e,t)=>{r[t]=e}));return this.runKernelFunc({forwardFunc:(r,a)=>(n=e(...t,a),i(n.value instanceof Qe,(()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\")),i(C(n.gradFunc),(()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\")),n.value),backwardsFunc:(e,r)=>{const a=n.gradFunc(e,r),s=Array.isArray(a)?a:[a];i(s.length===t.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(...).\")),i(s.every((e=>e instanceof Qe)),(()=>\"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 o={};return s.forEach(((e,t)=>{o[t]=()=>e})),o},inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){const t=Le(),n=await this.backend.time(e);return n.wallMs=Le()-t,n}track(e){return null!=this.state.activeScope&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new pt;for(const e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}function ht(){const e=U();if(null==e._tfengine){const t=new M(e);e._tfengine=new dt(t)}var t;return t=e._tfengine.ENV,V=t,Xe=()=>e._tfengine,e._tfengine}dt.nextTensorId=0,dt.nextVariableId=0;const ft=ht();function mt(e,t){const n={a:e,b:t};return ft.runKernel(\"Add\",n)}function gt(e){if(e||\"undefined\"!=typeof navigator&&null!=navigator){if(e||(e=navigator),\"ReactNative\"===e.product)return!0;const t=e.userAgent||e.vendor||(\"undefined\"!=typeof window?window.opera:\"\");if(!t){const t=e;return t.userAgentData&&t.userAgentData.mobile}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(t)||/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)