/**
 * @license
 * Copyright 2023 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.
 * =============================================================================
 */
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Buffer shape=${this.shape}`;throw new Error(t)}t++}let n=e[e.length-1];for(let t=0;t<e.length-1;++t)n+=this.strides[t]*e[t];return this.values[n]}locToIndex(e){if(0===this.rank)return 0;if(1===this.rank)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(0===this.rank)return[];if(1===this.rank)return[e];const 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 Dt().makeTensor(this.values,this.shape,this.dtype)}}let Dt=null,Ot=null;class Mt{constructor(e,t,n,r){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=ne(e),this.strides=he(e),this.dataId=n,this.id=r,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){const e=await this.data();return Ot.buffer(this.shape,this.dtype,e)}bufferSync(){return Ot.buffer(this.shape,this.dtype,this.dataSync())}async array(){const e=await this.data();return me(this.shape,e,"complex64"===this.dtype)}arraySync(){return me(this.shape,this.dataSync(),"complex64"===this.dtype)}async data(){this.throwIfDisposed();const e=Dt().read(this.dataId);if("string"===this.dtype){const t=await e;try{return t.map((e=>xt(e)))}catch(e){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(),Dt().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();const e=Dt().readSync(this.dataId);if("string"===this.dtype)try{return e.map((e=>xt(e)))}catch(e){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();const e=await Dt().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(this.kerasMask&&this.kerasMask.dispose(),Dt().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Ot.print(this,e)}clone(){return this.throwIfDisposed(),Ot.clone(this)}toString(e=!1){return St(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Ot.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Dt().makeVariable(this,e,t,n)}}function Ct(){return _e("Tensor",(()=>Mt))}Object.defineProperty(Mt,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),Ct();class Ft extends Mt{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 (${this.dtype}) must match`);if(!re(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Dt().disposeTensor(this),this.dataId=e.dataId,Dt().incRef(this,null)}dispose(){Dt().disposeVariable(this),this.isDisposedInternal=!0}}var Rt,zt,Vt,Lt,Bt;Object.defineProperty(Ft,Symbol.hasInstance,{value:e=>e instanceof Mt&&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"}(Rt||(Rt={})),function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"}(zt||(zt={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"}(Vt||(Vt={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(Lt||(Lt={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}(Bt||(Bt={}));const Pt={float32:Lt,int32:zt,bool:Vt,complex64:Bt};function Kt(e){return null!=e&&"object"==typeof e&&"texture"in e&&e.texture instanceof WebGLTexture}function qt(e){return"undefined"!=typeof GPUBuffer&&null!=e&&"object"==typeof e&&"buffer"in e&&e.buffer instanceof GPUBuffer}function Ut(e,t){if(e.dtype===t.dtype)return[e,t];const n=function(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return Pt[e][t]}(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function jt(e){const t=[];return Wt(e,t,new Set),t}function Wt(e,t,n){if(null==e)return;if(e instanceof Mt)return void t.push(e);if(r=e,!Array.isArray(r)&&"object"!=typeof r)return;var r;const s=e;for(const e in s){const r=s[e];n.has(r)||(n.add(r),Wt(r,t,n))}}function Gt(e){return null!=e.kernelName}class Ht{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map((e=>e.name))))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class Zt{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Ht}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then((()=>{}));if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(await this.initializeBackend(n).success)return void await this.setBackend(n)}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${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 '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(Ce(`${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 kt(this.backendInstance),!0}setupRegisteredKernels(){Le(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){Le(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 n=t.factory();if(!n||n instanceof class{refCount(e){return X("refCount")}incRef(e){return X("incRef")}timerAvailable(){return!0}time(e){return X("time")}read(e){return X("read")}readSync(e){return X("readSync")}readToGPU(e,t){return X("readToGPU")}numDataIds(){return X("numDataIds")}disposeData(e,t){return X("disposeData")}write(e,t,n){return X("write")}move(e,t,n,r,s){return X("move")}createTensorFromGPUData(e,t,n){return X("createTensorFromGPUData")}memory(){return X("memory")}floatPrecision(){return X("floatPrecision")}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}dispose(){return X("dispose")}}||"function"!=typeof n.then)return this.registry[e]=n,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,r=n.then((n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0))).catch((n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,Ce(`Initialization of backend ${e} failed`),Ce(n.stack||n.message)),!1)));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}}catch(t){return Ce(`Initialization of backend ${e} failed`),Ce(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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:s}=this.initializeBackend(n);if(s||r)return{name:n,asyncInit:s}}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,s=this.readSync(t),a=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,s,n.shape,n.dtype,a),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 Zt.nextTensorId++}nextVariableId(){return Zt.nextVariableId++}clone(e){const t=Qt.runKernel(Ie,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],(e=>({x:()=>{const t={x:e},n={dtype:"float32"};return Qt.runKernel(Ee,t,n)}})),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=ze(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 s=0;n.forEach((e=>{s+="complex64"===e.dtype?3:1}));const a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-s-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;let o,i;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const u=Gt(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(Gt(e)){const{kernelName:t,inputs:s,attrs:a}=e;null==this.backendName&&this.backend;const u=ze(t,this.backendName);J(null!=u,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),o=()=>{const e=this.backend.numDataIds();i=u.kernelFunc({inputs:s,attrs:a,backend:this.backend});const o=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,o);const p=o.map((e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e)));if(r){const e=this.getTensorsForGradient(t,s,p);n=this.saveTensorsForBackwardMode(e)}return p}}else{const{forwardFunc:t}=e,s=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};o=()=>{const e=this.backend.numDataIds();i=this.tidy((()=>t(this.backend,s)));const n=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,e,n),n}}const{inputs:p,attrs:l}=e,c=Gt(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(u,p,(()=>o())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),t=h.outputs):t=o()})),r&&this.addTapeNode(u,p,t,c,n,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(p).map((e=>null!=p[e]?p[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){const t=e.map((e=>this.keep(this.clone(e))));return t}getTensorsForGradient(e,t,n){const r=Ve(e);if(null!=r){const e=r.inputsToSave||[],s=r.outputsToSave||[];let a;r.saveAllInputs?(J(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an array.")),a=Object.keys(t).map((e=>t[e]))):a=e.map((e=>t[e]));const o=n.filter(((e,t)=>s[t]));return a.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 s=e;"string"===n&&pe(e[0])&&(s=e.map((e=>function(e,t="utf-8"){return t=t||"utf-8",ke().platform.encode(e,t)}(e))));const a=r.write(s,t,n),o=new Mt(t,n,a,this.nextTensorId());if(this.trackTensor(o,r),"string"===n){const e=this.state.tensorInfo.get(a),t=function(e){if(null==e)return 0;let t=0;return e.forEach((e=>t+=e.length)),t}(s);this.state.numBytes+=t-e.bytes,e.bytes=t}return o}makeTensorFromDataId(e,t,n,r){const s={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(s,r)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:r,dtype:s}=e,a=new Mt(r,s,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const s=new Ft(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[s.name])throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*ue(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 Ft||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*ue(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,s,a){const o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:s},i=Ve(e);null!=i&&(r=i.gradFunc),null!=r&&(o.gradient=e=>(e=e.map(((e,t)=>{if(null==e){const e=n[t],r=ye(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e})),r(e.length>1?e:e[0],s,a))),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=jt(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(J(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 s=this.scopedRun((()=>this.startTape()),(()=>this.endTape()),(()=>this.tidy("forward",e)));J(s instanceof Mt,(()=>"The result y returned by f() must be a tensor."));const a=function(e,t,n){const r={},s={};for(let e=0;e<t.length;e++)r[t[e].id]=!0;for(let n=0;n<e.length;n++){const a=e[n],o=a.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]){a.outputs.forEach((e=>r[e.id]=!0)),i=!0,s[a.id]=!0;break}if(i)break}}const a={};a[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(a[n.outputs[e].id]){for(const e in r)a[r[e].id]=!0,o[n.id]=!0;break}}const i=[];for(let t=0;t<e.length;t++){const n=e[t];if(s[n.id]&&o[n.id]){const e={};for(const t in n.inputs){const s=n.inputs[t];r[s.id]&&(e[t]=s)}const t=Object.assign({},n);t.inputs=e,t.outputs=n.outputs,i.push(t)}}return i}(this.state.activeTape,t,s);if(!r&&0===a.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[s.id]=null==n?function(e){const t=fe(ne(e),"float32");return Qt.makeTensor(t,e,"float32")}(s.shape):n,function(e,t,n,r){for(let s=t.length-1;s>=0;s--){const a=t[s],o=[];if(a.outputs.forEach((t=>{const n=e[t.id];null!=n?o.push(n):o.push(null)})),null==a.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);const i=a.gradient(o);for(const t in a.inputs){if(!(t in i))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(i)}.`);const s=n((()=>i[t]()));if("float32"!==s.dtype)throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${s.dtype}'`);const o=a.inputs[t];if(!re(s.shape,o.shape))throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${t}' has shape '${s.shape}', which does not match the shape of the input '${o.shape}'`);if(null==e[o.id])e[o.id]=s;else{const t=e[o.id];e[o.id]=r(t,s),t.dispose()}}}}(e,a,(e=>this.tidy(e)),Yt);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:s,grads:r}}))}customGrad(e){return J(ce(e),(()=>"The f passed in customGrad(f) must be a function.")),(...t)=>{let n;J(t.every((e=>e instanceof Mt)),(()=>"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,s)=>(n=e(...t,s),J(n.value instanceof Mt,(()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor")),J(ce(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 s=n.gradFunc(e,r),a=Array.isArray(s)?s:[s];J(a.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(...).")),J(a.every((e=>e instanceof Mt)),(()=>"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 a.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=bt(),n=await this.backend.time(e);return n.wallMs=bt()-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 Ht;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}}Zt.nextTensorId=0,Zt.nextVariableId=0;const Qt=function(){const e=Se();if(null==e._tfengine){const t=new Ne(e);e._tfengine=new Zt(t)}var t;return t=e._tfengine.ENV,ve=t,Dt=()=>e._tfengine,e._tfengine}();function Yt(e,t){const n={a:e,b:t};return Qt.runKernel("Add",n)}function Xt(e,t){let n=e;if(Nt(e))return"string"===t?[]:[e.length];if(Kt(e)){const t=e.channels||"RGBA";return[e.height,e.width*t.length]}if(qt(e))return[e.buffer.size/(null==t?4:ue(t))];if(!Array.isArray(e))return[];const r=[];for(;Array.isArray(n)||Nt(n)&&"string"!==t;)r.push(n.length),n=n[0];return Array.isArray(e)&&ke().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Jt(e,r,[]),r}function Jt(e,t,n){if(n=n||[],!Array.isArray(e)&&!Nt(e))return void J(0===t.length,(()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`));J(t.length>0,(()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`)),J(e.length===t[0],(()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`));const r=t.slice(1);for(let t=0;t<e.length;++t)Jt(e[t],r,n.concat(t))}function en(e,t,n,r){if("string_or_numeric"!==e){if(null==e)throw new Error("Expected dtype cannot be null.");if("numeric"!==e&&e!==t||"numeric"===e&&"string"===t)throw new Error(`Argument '${n}' passed to '${r}' must be ${e} tensor, but got ${t} tensor`)}}function tn(e,t,n,r="numeric"){if(e instanceof Ct())return en(r,e.dtype,t,n),e;let s=le(e);if("string"!==s&&["bool","int32","float32"].indexOf(r)>=0&&(s=r),en(r,s,t,n),null==e||!Nt(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e){const r=null==e?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${r}'`)}const a=Xt(e,s);Nt(e)||Array.isArray(e)||(e=[e]);const o="string"!==s?gt(e,s):wt(e,[],!0);return Qt.makeTensor(o,a,s)}function nn(e,t,n,r="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map(((e,s)=>tn(e,`${t}[${s}]`,n,r)))}const rn="__op";function sn(e){const t=Object.keys(e);if(1!==t.length)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. 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Qt.runKernel("Asin",t)}});const yn=sn({asinh_:function(e){const t={x:tn(e,"x","asinh")};return Qt.runKernel("Asinh",t)}});const gn=sn({atan_:function(e){const t={x:tn(e,"x","atan")};return Qt.runKernel("Atan",t)}});const bn=sn({atan2_:function(e,t){let n=tn(e,"a","atan2"),r=tn(t,"b","atan2");[n,r]=Ut(n,r);const s={a:n,b:r};return Qt.runKernel("Atan2",s)}});const xn=sn({atanh_:function(e){const t={x:tn(e,"x","atanh")};return Qt.runKernel("Atanh",t)}});const Nn=sn({cast_:function(e,t){const n=tn(e,"x","cast");if(!function(e){return"bool"===e||"complex64"===e||"float32"===e||"int32"===e||"string"===e}(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if("string"===t&&"string"!==n.dtype||"string"!==t&&"string"===n.dtype)throw new Error("Only strings can be casted to strings");const r={x:n},s={dtype:t};return Qt.runKernel(Ee,r,s)}});function wn(e,t,n,r,s,a,o=!1,i="channelsLast"){let[u,p,l,c]=[-1,-1,-1,-1];if("channelsLast"===i)[u,p,l,c]=e;else{if("channelsFirst"!==i)throw new Error(`Unknown dataFormat ${i}`);[u,c,p,l]=e}const[h,d,,m]=t,[f,y]=kn(n),[g,b]=kn(r),x=Tn(h,g),N=Tn(d,b),{padInfo:w,outHeight:k,outWidth:T}=function(e,t,n,r,s,a,o,i,u){let p,l,c;if("number"==typeof e){p={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};const s=function(e,t,n,r,s){null==r&&(r=function(e,t,n,r=1){const s=Tn(t,r);return Math.floor((e[0]*(n-1)-n+s)/2)}(e,t,n));const a=e[0],o=e[1],i=vn((a-t+2*r)/n+1,s),u=vn((o-t+2*r)/n+1,s);return[i,u]}([t,n],a,r,e,i);l=s[0],c=s[1]}else if("same"===e){l=Math.ceil(t/r),c=Math.ceil(n/s);const e=Math.max(0,(l-1)*r+a-t),i=Math.max(0,(c-1)*s+o-n),u=Math.floor(e/2),h=e-u,d=Math.floor(i/2);p={top:u,bottom:h,left:d,right:i-d,type:"SAME"}}else if("valid"===e)p={top:0,bottom:0,left:0,right:0,type:"VALID"},l=Math.ceil((t-a+1)/r),c=Math.ceil((n-o+1)/s);else{if("object"!=typeof e)throw Error(`Unknown padding parameter: ${e}`);{const h="channelsLast"===u?e[1][0]:e[2][0],d="channelsLast"===u?e[1][1]:e[2][1],m="channelsLast"===u?e[2][0]:e[3][0],f="channelsLast"===u?e[2][1]:e[3][1];p={top:h,bottom:d,left:m,right:f,type:0===h&&0===d&&0===m&&0===f?"VALID":"EXPLICIT"},l=vn((t-a+h+d)/r+1,i),c=vn((n-o+m+f)/s+1,i)}}return{padInfo:p,outHeight:l,outWidth:c}}(s,p,l,f,y,x,N,a,i),v=o?m*c:m;let S;return"channelsFirst"===i?S=[u,v,k,T]:"channelsLast"===i&&(S=[u,k,T,v]),{batchSize:u,dataFormat:i,inHeight:p,inWidth:l,inChannels:c,outHeight:k,outWidth:T,outChannels:v,padInfo:w,strideHeight:f,strideWidth:y,filterHeight:h,filterWidth:d,effectiveFilterHeight:x,effectiveFilterWidth:N,dilationHeight:g,dilationWidth:b,inShape:e,outShape:S,filterShape:t}}function kn(e){return"number"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function Tn(e,t){return t<=1?e:e+(e-1)*(t-1)}function vn(e,t){if(!t)return Math.trunc(e);switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error(`Unknown roundingMode ${t}`)}}function Sn(e){const[t,n,r]=kn(e);return 1===t&&1===n&&1===r}function _n(e,t){return Sn(e)||Sn(t)}function En(e){return kn(e).every((e=>e>0))}function In(e,t,n){if(null!=n){if("string"==typeof t)throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);if("number"==typeof t)J(se(t),(()=>`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`));else{if("object"!=typeof t)throw Error(`Error in ${e}: Unknown padding parameter: ${t}`);t.forEach((t=>{t.forEach((t=>{J(se(t),(()=>`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`))}))}))}}}const $n=sn({reshape_:function(e,t){const n={x:tn(e,"x","reshape","string_or_numeric")},r={shape:t};return Qt.runKernel("Reshape",n,r)}});const An=sn({avgPool_:function(e,t,n,r,s){const a=tn(e,"x","avgPool","float32");J(_n(n,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '1'`));let o=a,i=!1;3===a.rank&&(i=!0,o=$n(a,[1,a.shape[0],a.shape[1],a.shape[2]])),J(4===o.rank,(()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`)),In("avgPool",r,s);const u={x:o},p={filterSize:t,strides:n,pad:r,dimRoundingMode:s};let l=Qt.runKernel("AvgPool",u,p);return l=Nn(l,a.dtype),i?$n(l,[l.shape[1],l.shape[2],l.shape[3]]):l}});const Dn=sn({avgPool3d_:function(e,t,n,r,s,a="NDHWC"){const o=tn(e,"x","avgPool3d","float32");let i=o,u=!1;4===o.rank&&(u=!0,i=$n(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),J(5===i.rank,(()=>`Error in avgPool3d: x must be rank 5 but got rank ${i.rank}.`)),J("NDHWC"===a,(()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`)),J("number"==typeof n&&n>0||Array.isArray(n)&&n[0]>0&&n[1]>0&&n[2]>0,(()=>`Error in avgPool3d: Stride must be > 0, but got '${n}'`)),In("avgPool3d",r,s);const p={x:i},l={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:a};let c=Qt.runKernel("AvgPool3D",p,l);return c=Nn(c,i.dtype),u?$n(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}});const On=sn({clone_:function(e){const t={x:tn(e,"x","clone","string_or_numeric")};return Qt.runKernel(Ie,t)}});const Mn=sn({concat_:function(e,t=0){J(e.length>=1,(()=>"Pass at least one tensor to concat"));const n=nn(e,"tensors","concat","string_or_numeric");if("complex64"===n[0].dtype&&n.forEach((e=>{if("complex64"!==e.dtype)throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${e.dtype}. `)})),1===n.length)return On(n[0]);const r=n,s={axis:t};return Qt.runKernel("Concat",r,s)}});const Cn=sn({matMul_:function(e,t,n=!1,r=!1){let s=tn(e,"a","matMul"),a=tn(t,"b","matMul");[s,a]=Ut(s,a);const o={a:s,b:a},i={transposeA:n,transposeB:r};return Qt.runKernel("BatchMatMul",o,i)}});const Fn=sn({mul_:function(e,t){let n=tn(e,"a","mul"),r=tn(t,"b","mul");[n,r]=Ut(n,r);const s={a:n,b:r};return Qt.runKernel("Multiply",s)}});const Rn=sn({sigmoid_:function(e){const t={x:tn(e,"x","sigmoid","float32")};return Qt.runKernel("Sigmoid",t)}});const zn=sn({slice_:function(e,t,n){const r=tn(e,"x","slice","string_or_numeric");if(0===r.rank)throw new Error("Slicing scalar is not possible");const s={x:r},a={begin:t,size:n};return Qt.runKernel("Slice",s,a)}});const Vn=sn({tanh_:function(e){const t={x:tn(e,"x","tanh","float32")};return Qt.runKernel("Tanh",t)}});const Ln=sn({basicLSTMCell_:function(e,t,n,r,s,a){const o=tn(e,"forgetBias","basicLSTMCell"),i=tn(t,"lstmKernel","basicLSTMCell"),u=tn(n,"lstmBias","basicLSTMCell"),p=tn(r,"data","basicLSTMCell"),l=tn(s,"c","basicLSTMCell"),c=tn(a,"h","basicLSTMCell"),h=Mn([p,c],1),d=Cn(h,i),m=pn(d,u),f=m.shape[0],y=m.shape[1]/4,g=[f,y],b=zn(m,[0,0],g),x=zn(m,[0,y],g),N=zn(m,[0,2*y],g),w=zn(m,[0,3*y],g),k=pn(Fn(Rn(b),Vn(x)),Fn(l,Rn(pn(o,N))));return[k,Fn(Vn(k),Rn(w))]}});const Bn=sn({batchToSpaceND_:function(e,t,n){const r=tn(e,"x","batchToSpaceND"),s=t.reduce(((e,t)=>e*t));J(r.rank>=1+t.length,(()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`)),J(n.length===t.length,(()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`)),J(r.shape[0]%s==0,(()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${s}`));const a={x:r},o={blockShape:t,crops:n};return Qt.runKernel("BatchToSpaceND",a,o)}});const Pn=sn({batchNorm_:function(e,t,n,r,s,a){null==a&&(a=.001);const o=tn(e,"x","batchNorm"),i=tn(t,"mean","batchNorm"),u=tn(n,"variance","batchNorm");let p,l;null!=s&&(p=tn(s,"scale","batchNorm")),null!=r&&(l=tn(r,"offset","batchNorm")),J(i.rank===u.rank,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),J(null==l||i.rank===l.rank,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),J(null==p||i.rank===p.rank,(()=>"Batch normalization gradient requires mean and scale to have equal ranks."));const c=function(e){let t;return t=0===e.rank||1===e.rank?$n(e,[1,1,1,e.size]):2===e.rank?$n(e,[1,1,e.shape[0],e.shape[1]]):3===e.rank?$n(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e,t}(o),h={x:c,scale:p,offset:l,mean:i,variance:u},d={varianceEpsilon:a},m=Qt.runKernel("FusedBatchNorm",h,d);return $n(m,o.shape)}});const Kn=sn({batchNorm2d_:function(e,t,n,r,s,a){const o=tn(e,"x","batchNorm"),i=tn(t,"mean","batchNorm"),u=tn(n,"variance","batchNorm");let p,l;return null!=s&&(p=tn(s,"scale","batchNorm")),null!=r&&(l=tn(r,"offset","batchNorm")),J(2===o.rank,(()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`)),J(2===i.rank||1===i.rank,(()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`)),J(2===u.rank||1===u.rank,(()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`)),null!=p&&J(2===p.rank||1===p.rank,(()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${p.rank}.`)),null!=l&&J(2===l.rank||1===l.rank,(()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${l.rank}.`)),Pn(o,i,u,l,p,a)}});const qn=sn({batchNorm3d_:function(e,t,n,r,s,a){const o=tn(e,"x","batchNorm"),i=tn(t,"mean","batchNorm"),u=tn(n,"variance","batchNorm");let p,l;return null!=s&&(p=tn(s,"scale","batchNorm")),null!=r&&(l=tn(r,"offset","batchNorm")),J(3===o.rank,(()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`)),J(3===i.rank||1===i.rank,(()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`)),J(3===u.rank||1===u.rank,(()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`)),null!=p&&J(3===p.rank||1===p.rank,(()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${p.rank}.`)),null!=l&&J(3===l.rank||1===l.rank,(()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${l.rank}.`)),Pn(o,i,u,l,p,a)}});const Un=sn({batchNorm4d_:function(e,t,n,r,s,a){const o=tn(e,"x","batchNorm"),i=tn(t,"mean","batchNorm"),u=tn(n,"variance","batchNorm");let p,l;return null!=s&&(p=tn(s,"scale","batchNorm")),null!=r&&(l=tn(r,"offset","batchNorm")),J(4===o.rank,(()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`)),J(4===i.rank||1===i.rank,(()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`)),J(4===u.rank||1===u.rank,(()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`)),null!=p&&J(4===p.rank||1===p.rank,(()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${p.rank}.`)),null!=l&&J(4===l.rank||1===l.rank,(()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${l.rank}.`)),Pn(o,i,u,l,p,a)}});const jn=sn({bincount_:function(e,t,n){const r=tn(e,"x","bincount"),s=tn(t,"weights","bincount");J("int32"===r.dtype,(()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`)),J(n>=0,(()=>`size must be non-negative, but got ${n}.`)),J(s.size===r.size||0===s.size,(()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${s.shape}.`));const a={x:r,weights:s},o={size:n};return Qt.runKernel("Bincount",a,o)}});const Wn=sn({bitwiseAnd_:function(e,t){const n=tn(e,"x","bitwiseAnd"),r=tn(t,"y","bitwiseAnd");if(!re(n.shape,r.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${n.shape}, y: ${r.shape}`);if("int32"!==n.dtype||"int32"!==r.dtype)throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${n.dtype} and type of y: ${r.dtype}`);const s={a:n,b:r};return Qt.runKernel("BitwiseAnd",s)}});const Gn=sn({broadcastArgs_:function(e,t){const n=tn(e,"s0","broadcastArgs","int32"),r=tn(t,"s1","broadcastArgs","int32");if(1!==n.rank)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(1!==r.rank)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${r.rank}`);const s={s0:n,s1:r};return Qt.runKernel("BroadcastArgs",s)}});const Hn=sn({broadcastTo_:function(e,t){let n=tn(e,"broadcastTo","x");const r=n.shape;if(ge(t),t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const e=n.shape.slice();for(;e.length<t.length;)e.unshift(1);n=$n(n,e)}const s=n.shape,a=Array.from(t);for(let e=t.length-1;e>=0;e--)if(s[e]===t[e])a[e]=1;else if(1!==n.shape[e])throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(0===a.map(((e,t)=>e>1?t:-1)).filter((e=>e>=0)).length)return On(n);const o={x:n},i={reps:a};return Qt.runKernel($e,o,i)}});function Zn(e,t="float32",n){return t=t||"float32",ge(e),new At(e,t,n)}const Qn=sn({ceil_:function(e){const t={x:tn(e,"x","ceil","float32")};return Qt.runKernel("Ceil",t)}});function Yn(e,t,n){ge(e);const r={shape:e,value:t,dtype:n=n||le(t)};return Qt.runKernel("Fill",{},r)}const Xn=sn({clipByValue_:function(e,t,n){const r=tn(e,"x","clipByValue");if(J(t<=n,(()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`)),t===n)return Yn(r.shape,t,r.dtype);const s={x:r},a={clipValueMin:t,clipValueMax:n};return Qt.runKernel("ClipByValue",s,a)}});const Jn=sn({complex_:function(e,t){const n=tn(e,"real","complex"),r=tn(t,"imag","complex");ee(n.shape,r.shape,`real and imag shapes, ${n.shape} and ${r.shape}, must match in call to tf.complex().`);const s={real:n,imag:r};return Qt.runKernel("Complex",s)}});const er=sn({concat1d_:function(e){return Mn(e,0)}});const tr=sn({concat2d_:function(e,t){return Mn(e,t)}});const nr=sn({concat3d_:function(e,t){return Mn(e,t)}});const rr=sn({concat4d_:function(e,t){return Mn(e,t)}});const sr=sn({conv2d_:function(e,t,n,r,s="NHWC",a=[1,1],o){const i=tn(e,"x","conv2d","float32"),u=tn(t,"filter","conv2d","float32");let p=i,l=!1;3===i.rank&&(l=!0,p=$n(i,[1,i.shape[0],i.shape[1],i.shape[2]])),J(4===p.rank,(()=>`Error in conv2d: input must be rank 4, but got rank ${p.rank}.`)),J(4===u.rank,(()=>`Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`)),In("conv2d",r,o);const c="NHWC"===s?p.shape[3]:p.shape[1];J(c===u.shape[2],(()=>`Error in conv2d: depth of input (${c}) must match input depth for filter ${u.shape[2]}.`)),J(_n(n,a),(()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`)),J(En(a),(()=>"Error in conv2D: Dilated rates should be larger than 0.")),J(En(n),(()=>"Error in conv2D: Strides should be larger than 0."));const h={x:p,filter:u},d={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o},m=Qt.runKernel("Conv2D",h,d);return l?$n(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const ar=sn({conv1d_:function(e,t,n,r,s="NWC",a=1,o){const i=tn(e,"x","conv1d"),u=tn(t,"filter","conv1d");let p=i,l=!1;2===i.rank&&(l=!0,p=$n(i,[1,i.shape[0],i.shape[1]])),J(3===p.rank,(()=>`Error in conv1d: input must be rank 3, but got rank ${p.rank}.`)),J(3===u.rank,(()=>`Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`)),In("conv1d",r,o),J(p.shape[2]===u.shape[1],(()=>`Error in conv1d: depth of input (${p.shape[2]}) must match input depth for filter ${u.shape[1]}.`)),J(_n(n,a),(()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`)),J(En(a),(()=>"Error in conv1D: Dilated rates should be larger than 0.")),J(En(n),(()=>"Error in conv1D: Stride should be larger than 0.")),J("NWC"===s,(()=>`Error in conv1d: got dataFormat of ${s} but only NWC is currently supported.`));const c=$n(u,[1,u.shape[0],u.shape[1],u.shape[2]]),h=$n(p,[p.shape[0],1,p.shape[1],p.shape[2]]),d=sr(h,c,[1,n],r,"NHWC",[1,a],o);return $n(d,l?[d.shape[2],d.shape[3]]:[d.shape[0],d.shape[2],d.shape[3]])}});const or=sn({conv2DBackpropInput_:function(e,t,n,r,s,a="NHWC",o){J(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let i=e,u=t,p=!1;3===t.rank&&(p=!0,u=$n(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),J(4===i.length,(()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`)),J(4===u.rank,(()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`)),J(4===n.rank,(()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`));const l="NHWC"===a?i[3]:i[1],c="NHWC"===a?u.shape[3]:u.shape[1];J(l===n.shape[2],(()=>`Error in conv2dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[2]}.`)),J(c===n.shape[3],(()=>`Error in conv2dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[3]}.`)),In("conv2dDerInput",s,o);const h={dy:u,filter:n},d={strides:r,pad:s,dataFormat:a,dimRoundingMode:o,inputShape:i},m=Qt.runKernel("Conv2DBackpropInput",h,d);return p?$n(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const ir=sn({conv2dTranspose_:function(e,t,n,r,s,a){const o=tn(e,"x","conv2dTranspose"),i=tn(t,"filter","conv2dTranspose");return or(n,o,i,r,s,"NHWC",a)}});const ur=sn({conv3d_:function(e,t,n,r,s="NDHWC",a=[1,1,1]){const o=tn(e,"x","conv3d"),i=tn(t,"filter","conv3d");let u=o,p=!1;4===o.rank&&(p=!0,u=$n(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),J(5===u.rank,(()=>`Error in conv3d: input must be rank 5, but got rank ${u.rank}.`)),J(5===i.rank,(()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`)),J(u.shape[4]===i.shape[3],(()=>`Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${i.shape[3]}.`)),J(_n(n,a),(()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`)),J("NDHWC"===s,(()=>`Error in conv3d: got dataFormat of ${s} but only NDHWC is currently supported.`)),J(En(a),(()=>"Error in conv3D: Dilated rates should be larger than 0.")),J(En(n),(()=>"Error in conv3D: Strides should be larger than 0."));const l={x:u,filter:i},c={strides:n,pad:r,dataFormat:s,dilations:a},h=Qt.runKernel("Conv3D",l,c);return p?$n(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}});const pr=sn({conv3DBackpropInput_:function(e,t,n,r,s){J(e.length===t.rank,(()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`));let a=e,o=t,i=!1;4===t.rank&&(i=!0,o=$n(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);const u=a[4],p=o.shape[4];J(5===a.length,(()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`)),J(5===o.rank,(()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`)),J(5===n.rank,(()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`)),J(u===n.shape[3],(()=>`Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`)),J(p===n.shape[4],(()=>`Error in conv3dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[4]}.`));const l={dy:o,filter:n},c={pad:s,strides:r,inputShape:a},h=Qt.runKernel("Conv3DBackpropInputV2",l,c);return i?$n(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}});const lr=sn({conv3dTranspose_:function(e,t,n,r,s){const a=tn(e,"x","conv3dTranspose"),o=tn(t,"filter","conv3dTranspose");return pr(n,a,o,r,s)}});const cr=sn({cos_:function(e){const t={x:tn(e,"x","cos","float32")};return Qt.runKernel("Cos",t)}});const hr=sn({cosh_:function(e){const t={x:tn(e,"x","cosh","float32")};return Qt.runKernel("Cosh",t)}});const dr=sn({cumprod_:function(e,t=0,n=!1,r=!1){const s={x:tn(e,"x","cumprod")},a={axis:t,exclusive:n,reverse:r};return Qt.runKernel("Cumprod",s,a)}});const mr=sn({cumsum_:function(e,t=0,n=!1,r=!1){const s={x:tn(e,"x","cumsum")},a={axis:t,exclusive:n,reverse:r};return Qt.runKernel("Cumsum",s,a)}});const fr=sn({denseBincount_:function(e,t,n,r=!1){const s=tn(e,"x","denseBincount"),a=tn(t,"weights","denseBincount");J("int32"===s.dtype,(()=>`Error in denseBincount: input dtype must be int32, but got ${s.dtype}`)),J(s.rank<=2,(()=>`Error in denseBincount: input must be at most rank 2, but got rank ${s.rank}.`)),J(n>=0,(()=>`size must be non-negative, but got ${n}.`)),J(a.size===s.size||0===a.size,(()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${s.shape}, weights shape: ${a.shape}.`));const o={x:s,weights:a},i={size:n,binaryOutput:r};return Qt.runKernel("DenseBincount",o,i)}});const yr=sn({depthToSpace_:function(e,t,n="NHWC"){const r=tn(e,"x","depthToSpace","float32"),s="NHWC"===n?r.shape[1]:r.shape[2],a="NHWC"===n?r.shape[2]:r.shape[3],o="NHWC"===n?r.shape[3]:r.shape[1];J(t>1,(()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`)),J(s*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${s} and ${t}  for depthToSpace with input shape\n    ${r.shape}`)),J(a*t>=0,(()=>`Negative dimension size caused by overflow when multiplying\n    ${a} and ${t} for depthToSpace with input shape\n        ${r.shape}`)),J(o%(t*t)==0,(()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${r.shape}`));const i={x:r},u={blockSize:t,dataFormat:n};return Qt.runKernel("DepthToSpace",i,u)}});const gr=sn({depthwiseConv2d_:function(e,t,n,r,s="NHWC",a=[1,1],o){const i=tn(e,"x","depthwiseConv2d","float32"),u=tn(t,"filter","depthwiseConv2d","float32");let p=i,l=!1;3===i.rank&&(l=!0,p=$n(i,[1,i.shape[0],i.shape[1],i.shape[2]])),J(4===p.rank,(()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${p.rank}.`)),J(4===u.rank,(()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`));const c="NHWC"===s?p.shape[3]:p.shape[1];J(c===u.shape[2],(()=>`Error in depthwiseConv2d: number of input channels (${c}) must match the inChannels dimension in filter ${u.shape[2]}.`)),In("depthwiseConv2d",r,o);const h={x:p,filter:u},d={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o},m=Qt.runKernel("DepthwiseConv2dNative",h,d);return l?$n(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const br=sn({diag_:function(e){const t={x:tn(e,"x","diag")};return Qt.runKernel("Diag",t)}});const xr=sn({dilation2d_:function(e,t,n,r,s=[1,1],a="NHWC"){const o=tn(e,"x","dilation2d"),i=tn(t,"filter","dilation2d");J(3===o.rank||4===o.rank,(()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`)),J(3===i.rank,(()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`)),J("NHWC"===a,(()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`));let u=o,p=!1;3===o.rank&&(u=$n(o,[1,o.shape[0],o.shape[1],o.shape[2]]),p=!0),J(u.shape[3]===i.shape[2],(()=>`Error in dilation2d:  input and filter must have the same depth: ${u.shape[3]} vs ${i.shape[2]}`));const l={x:u,filter:i},c={strides:n,pad:r,dilations:s},h=Qt.runKernel("Dilation2D",l,c);return p?$n(h,[h.shape[1],h.shape[2],h.shape[3]]):h}});const Nr=sn({floorDiv_:function(e,t){let n=tn(e,"a","floorDiv"),r=tn(t,"b","floorDiv");[n,r]=Ut(n,r);const s={a:n,b:r};return Qt.runKernel("FloorDiv",s)}});const wr=sn({div_:function(e,t){let n=tn(e,"a","div"),r=tn(t,"b","div");if([n,r]=Ut(n,r),"int32"===n.dtype&&"int32"===r.dtype)return Nr(n,r);const s={a:n,b:r};return Qt.runKernel("RealDiv",s,{})}});function kr(e,t){const n=Math.max(e.length,t.length),r=new Array(n);for(let s=0;s<n;s++){let a=e[e.length-s-1];null==a&&(a=1);let o=t[t.length-s-1];if(null==o&&(o=1),1===a)r[n-s-1]=o;else if(1===o)r[n-s-1]=a;else{if(a!==o){throw Error(`Operands could not be broadcast together with shapes ${e} and ${t}.`)}r[n-s-1]=a}}return r}const Tr=sn({equal_:function(e,t){let n=tn(e,"a","equal","string_or_numeric"),r=tn(t,"b","equal","string_or_numeric");[n,r]=Ut(n,r),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("Equal",s)}});const vr=sn({where_:function(e,t,n){const r=tn(t,"a","where"),s=tn(n,"b","where"),a=tn(e,"condition","where","bool"),o=kr(kr(a.shape,r.shape),s.shape),i={condition:Hn(a,o),t:Hn(r,o),e:Hn(s,o)};return Qt.runKernel("Select",i)}});const Sr=sn({zerosLike_:function(e){const t={x:tn(e,"x","zerosLike")};return Qt.runKernel("ZerosLike",t)}});const _r=sn({divNoNan_:function(e,t){let n=tn(e,"a","div"),r=tn(t,"b","div");[n,r]=Ut(n,r);const s=wr(n,r),a=Sr(s),o=Tr(r,a);return vr(o,a,s)}});const Er=sn({dot_:function(e,t){const n=tn(e,"t1","dot"),r=tn(t,"t2","dot");J(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),(()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`));const s=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];if(J(s===a,(()=>`Error in dot: inner dimensions of inputs must match, but got ${s} and ${a}.`)),1===n.rank&&1===r.rank){const e=$n(n,[1,-1]),t=$n(r,[-1,1]),s=Cn(e,t);return $n(s,[])}if(1===n.rank&&2===r.rank){const e=$n(n,[1,-1]),t=$n(r,[r.shape[0],r.shape[1]]),s=Cn(e,t);return $n(s,[s.size])}if(2===n.rank&&1===r.rank){const e=$n(r,[-1,1]),t=Cn(n,e);return $n(t,[t.size])}{const e=$n(r,[r.shape[0],r.shape[1]]);return Cn(n,e)}}});const Ir=sn({einsum_:function(e,...t){const n=t.map(((e,t)=>tn(e,`tensors${t}`,"einsum"))),r={equation:e};return Qt.runKernel("Einsum",n,r)}});const $r=sn({elu_:function(e){const t={x:tn(e,"x","elu","float32")};return Qt.runKernel("Elu",t)}});const Ar=sn({ensureShape_:function(e,t){const n=tn(e,"x","ensureShape","string_or_numeric");if(!function(e,t){if(e===t)return!0;if(null==e||null==t)return!1;if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(null!==e[n]&&null!==t[n]&&e[n]!==t[n])return!1;return!0}(n.shape,t))throw new Error(`EnsureShape: Shape of tensor ${n.shape} is not compatible with expected shape ${t}`);return e}});const Dr=sn({erf_:function(e){let t=tn(e,"x","erf");J("int32"===t.dtype||"float32"===t.dtype,(()=>"Input dtype must be `int32` or `float32`.")),"int32"===t.dtype&&(t=Nn(t,"float32"));const n={x:t};return Qt.runKernel("Erf",n)}});function Or(e,t){return function(e,t,n){const r=e.length+t.length,s=[];let a=0,o=0;for(let i=0;i<r;i++)-1===n.indexOf(i)?s.push(e[a++]):s.push(t[o++]);return s}(e,t.map((e=>1)),t)}const Mr=sn({max_:function(e,t=null,n=!1){const r={x:tn(e,"x","max")},s={reductionIndices:t,keepDims:n};return Qt.runKernel("Max",r,s)}});const Cr=sn({min_:function(e,t=null,n=!1){const r={x:tn(e,"x","min")},s={axis:t,keepDims:n};return Qt.runKernel("Min",r,s)}});const Fr=sn({pow_:function(e,t){let n=tn(e,"base","pow"),r=tn(t,"exp","pow");[n,r]=Ut(n,r);const s={a:n,b:r};return Qt.runKernel("Pow",s)}});function Rr(e,t,n,r){if(null==r)r=le(e);else if("complex64"===r)throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(qt(e)||Kt(e)){if("float32"!==r&&"int32"!==r)throw new Error(`Creating tensor from GPU data only supports 'float32'|'int32' dtype, while the dtype is ${r}.`);return Qt.backend.createTensorFromGPUData(e,t||n,r)}if(!Nt(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(null!=t){ge(t);const e=ne(t),r=ne(n);J(e===r,(()=>`Based on the provided shape, [${t}], the tensor should have ${e} values but has ${r}`));for(let e=0;e<n.length;++e){const r=n[e],s=e!==n.length-1||r!==ne(t.slice(e));J(n[e]===t[e]||!s,(()=>`Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `))}}return Nt(e)||Array.isArray(e)||(e=[e]),t=t||n,e="string"!==r?gt(e,r):wt(e,[],!0),Qt.makeTensor(e,t,r)}function zr(e,t){if((Nt(e)&&"string"!==t||Array.isArray(e))&&"complex64"!==t)throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if("string"===t&&Nt(e)&&!(e instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return Rr(e,[],[],t)}const Vr=sn({sqrt_:function(e){const t={x:tn(e,"x","sqrt","float32")};return Qt.runKernel("Sqrt",t)}});const Lr=sn({square_:function(e){const t=tn(e,"x","square");return Qt.runKernel("Square",{x:t},{})}});const Br=sn({sum_:function(e,t=null,n=!1){let r=tn(e,"x","sum");"bool"===r.dtype&&(r=Nn(r,"int32"));const s={x:r},a={axis:t,keepDims:n};return Qt.runKernel("Sum",s,a)}});function Pr(e,t,n=null){if(0===e.rank)return an(e);if(1!==e.rank&&null===n)return Pr($n(e,[-1]),t,n);if(1===e.rank||"number"==typeof n||Array.isArray(n)&&1===n.length){if(1===t)return Br(an(e),n);if(t===1/0)return Mr(an(e),n);if(t===-1/0)return Cr(an(e),n);if("euclidean"===t||2===t)return Vr(Br(Fr(an(e),zr(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&2===n.length){if(1===t)return Mr(Br(an(e),n[0]),n[1]-1);if(t===1/0)return Mr(Br(an(e),n[1]),n[0]);if(t===-1/0)return Cr(Br(an(e),n[1]),n[0]);if("fro"===t||"euclidean"===t)return Vr(Br(Lr(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}const Kr=sn({norm_:function(e,t="euclidean",n=null,r=!1){const s=Pr(e=tn(e,"x","norm"),t,n);let a=s.shape;if(r){const t=oe(n,e.shape);a=Or(s.shape,t)}return $n(s,a)}});const qr=sn({euclideanNorm_:function(e,t=null,n=!1){return Kr(e,"euclidean",t,n)}});const Ur=sn({exp_:function(e){const t={x:tn(e,"x","exp")};return Qt.runKernel("Exp",t)}});const jr=sn({expandDims_:function(e,t=0){const n=tn(e,"x","expandDims","string_or_numeric");J(t<=n.rank,(()=>"Axis must be <= rank of the tensor"));const r={input:n},s={dim:t};return Qt.runKernel("ExpandDims",r,s)}});const Wr=sn({expm1_:function(e){const t={x:tn(e,"x","expm1")};return Qt.runKernel("Expm1",t)}});const Gr=sn({tile_:function(e,t){const n=tn(e,"x","tile","string_or_numeric");J(n.rank===t.length,(()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`));const r={x:n},s={reps:t};return Qt.runKernel($e,r,s)}});const Hr=sn({eye_:function(e,t,n,r="float32"){null==t&&(t=e);const s=Zn([e,t],r),a=e<=t?e:t;for(let e=0;e<a;++e)s.set(1,e,e);const o=$n(s.toTensor(),[e,t]);if(null==n)return o;if(1===n.length)return Gr(jr(o,0),[n[0],1,1]);if(2===n.length)return Gr(jr(jr(o,0),0),[n[0],n[1],1,1]);if(3===n.length)return Gr(jr(jr(jr(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}});const Zr=sn({floor_:function(e){const t={x:tn(e,"x","floor","float32")};return Qt.runKernel("Floor",t)}});const Qr=sn({gather_:function(e,t,n=0,r=0){const s={x:tn(e,"x","gather"),indices:tn(t,"indices","gather","int32")},a={axis:n,batchDims:r};return Qt.runKernel("GatherV2",s,a)}});const Yr=sn({greater_:function(e,t){let n=tn(e,"a","greater","string_or_numeric"),r=tn(t,"b","greater","string_or_numeric");[n,r]=Ut(n,r),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("Greater",s)}});const Xr=sn({greaterEqual_:function(e,t){let n=tn(e,"a","greaterEqual","string_or_numeric"),r=tn(t,"b","greaterEqual","string_or_numeric");[n,r]=Ut(n,r),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("GreaterEqual",s)}});const Jr=sn({imag_:function(e){const t={input:tn(e,"input","imag")};return Qt.runKernel("Imag",t)}});const es=sn({isFinite_:function(e){const t={x:tn(e,"x","isFinite")};return Qt.runKernel("IsFinite",t)}});const ts=sn({isInf_:function(e){const t={x:tn(e,"x","isInf")};return Qt.runKernel("IsInf",t)}});const ns=sn({isNaN_:function(e){const t={x:tn(e,"x","isNaN")};return Qt.runKernel("IsNan",t)}});const rs=sn({leakyRelu_:function(e,t=.2){const n={x:tn(e,"x","leakyRelu")},r={alpha:t};return Qt.runKernel("LeakyRelu",n,r)}});const ss=sn({less_:function(e,t){let n=tn(e,"a","less","string_or_numeric"),r=tn(t,"b","less","string_or_numeric");[n,r]=Ut(n,r),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("Less",s)}});const as=sn({lessEqual_:function(e,t){let n=tn(e,"a","lessEqual","string_or_numeric"),r=tn(t,"b","lessEqual","string_or_numeric");[n,r]=Ut(n,r),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("LessEqual",s)}});const os=sn({localResponseNormalization_:function(e,t=5,n=1,r=1,s=.5){const a=tn(e,"x","localResponseNormalization");J(4===a.rank||3===a.rank,(()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${a.rank}.`)),J(se(t),(()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`));let o=a,i=!1;3===a.rank&&(i=!0,o=$n(a,[1,a.shape[0],a.shape[1],a.shape[2]]));const u={x:o},p={depthRadius:t,bias:n,alpha:r,beta:s},l=Qt.runKernel("LRN",u,p);return i?$n(l,[l.shape[1],l.shape[2],l.shape[3]]):l}});const is=sn({log_:function(e){const t={x:tn(e,"x","log","float32")};return Qt.runKernel("Log",t)}});const us=sn({log1p_:function(e){const t={x:tn(e,"x","log1p")};return Qt.runKernel("Log1p",t)}});function ps(e){return Qt.customGrad(e)}const ls=sn({neg_:function(e){const t={x:tn(e,"x","neg")};return Qt.runKernel("Neg",t)}});const cs=sn({softplus_:function(e){const t={x:tn(e,"x","softplus")};return Qt.runKernel("Softplus",t)}});const hs=sn({logSigmoid_:function(e){const t=tn(e,"x","logSigmoid"),n=ps((e=>({value:ls(cs(ls(e))),gradFunc:t=>Fn(t,Rn(ls(e)))})));return n(t)}});const ds=sn({sub_:function(e,t){let n=tn(e,"a","sub"),r=tn(t,"b","sub");[n,r]=Ut(n,r);const s={a:n,b:r};return Qt.runKernel("Sub",s)}});const ms=sn({logSoftmax_:function(e,t=-1){const n=tn(e,"logits","logSoftmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);const r=ps(((e,n)=>{const r=Mr(e,t,!0),s=ds(e,r),a=ds(Nn(s,"float32"),is(Br(Ur(s),t,!0)));n([a]);return{value:a,gradFunc:(e,n)=>{const[r]=n,s=Ur(r);return ds(e,Fn(Br(e,t,!0),s))}}}));return r(n)}});const fs=sn({logSumExp_:function(e,t=null,n=!1){const r=tn(e,"x","logSumExp"),s=oe(t,r.shape),a=Mr(r,s,!0),o=ds(r,a),i=Ur(o),u=Br(i,s),p=is(u),l=pn($n(a,p.shape),p);if(n){const e=Or(l.shape,s);return $n(l,e)}return l}});const ys=sn({logicalAnd_:function(e,t){const n=tn(e,"a","logicalAnd","bool"),r=tn(t,"b","logicalAnd","bool");kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("LogicalAnd",s)}});const gs=sn({logicalNot_:function(e){const t={x:tn(e,"x","logicalNot","bool")};return Qt.runKernel("LogicalNot",t)}});const bs=sn({logicalOr_:function(e,t){const n=tn(e,"a","logicalOr","bool"),r=tn(t,"b","logicalOr","bool");kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("LogicalOr",s)}});const xs=sn({logicalXor_:function(e,t){const n=tn(e,"a","logicalXor","bool"),r=tn(t,"b","logicalXor","bool");return kr(n.shape,r.shape),ys(bs(e,t),gs(ys(e,t)))}}),Ns=2147483648;const ws=sn({searchSorted_:function(e,t,n="left"){const r=tn(e,"sortedSequence","searchSorted"),s=tn(t,"values","searchSorted"),a=r.shape[r.shape.length-1],o=s.shape[s.shape.length-1],i=$n(r,[-1,a]),u=$n(s,[-1,o]);if(i.rank<2)throw new Error("Sorted input argument must be at least 2-dimensional");if(i.shape[0]!==u.shape[0])throw new Error("Leading dimension of 'sortedSequence' and 'values' must match.");if(ne(u.shape)>=Ns)throw new Error("values tensor size must less than 2147483648");if(i.shape[1]>=Ns)throw new Error(`trailing dim_size must less than 2147483648 for int32 output type, was ${i.shape[1]}`);const p={sortedSequence:i,values:u},l={side:n};return Qt.runKernel("SearchSorted",p,l)}});const ks=sn({maxPool_:function(e,t,n,r,s){const a=tn(e,"x","maxPool");let o=a,i=!1;3===a.rank&&(i=!0,o=$n(a,[1,a.shape[0],a.shape[1],a.shape[2]])),J(4===o.rank,(()=>`Error in maxPool: input must be rank 4 but got rank ${o.rank}.`)),J(_n(n,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '1'`)),In("maxPool",r,s);const u={x:o},p={filterSize:t,strides:n,pad:r,dimRoundingMode:s},l=Qt.runKernel("MaxPool",u,p);return i?$n(l,[l.shape[1],l.shape[2],l.shape[3]]):l}});const Ts=sn({maxPool3d_:function(e,t=[1,1,1],n,r,s,a="NDHWC"){const o=tn(e,"x","maxPool3d");let i=o,u=!1;4===o.rank&&(u=!0,i=$n(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),J(5===i.rank,(()=>`Error in maxPool3d: x must be rank 5 but got rank ${i.rank}.`)),J("NDHWC"===a,(()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`)),In("maxPool3d",r,s);const p={x:i},l={filterSize:t,strides:n,pad:r,dimRoundingMode:s,dataFormat:a},c=Qt.runKernel("MaxPool3D",p,l);return u?$n(c,[c.shape[1],c.shape[2],c.shape[3],c.shape[4]]):c}});const vs=sn({maxPoolWithArgmax_:function(e,t,n,r,s=!1){const a={x:tn(e,"x","maxPoolWithArgmax")},o={filterSize:t,strides:n,pad:r,includeBatchInIndex:s},i=Qt.runKernel("MaxPoolWithArgmax",a,o);return{result:i[0],indexes:i[1]}}});const Ss=sn({maximum_:function(e,t){let n=tn(e,"a","maximum"),r=tn(t,"b","maximum");[n,r]=Ut(n,r),"bool"===n.dtype&&(n=Nn(n,"int32"),r=Nn(r,"int32")),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("Maximum",s)}});const _s=sn({mean_:function(e,t=null,n=!1){const r={x:tn(e,"x","mean")},s={axis:t,keepDims:n};return Qt.runKernel("Mean",r,s)}});function Es(e,t="float32"){if(ge(e),"complex64"===t){const t=Es(e,"float32"),n=Es(e,"float32");return Jn(t,n)}const n=ye(ne(e),t);return Qt.makeTensor(n,e,t)}function Is(e,t="float32"){if(ge(e),"complex64"===t){const t=Is(e,"float32"),n=Es(e,"float32");return Jn(t,n)}const n=fe(ne(e),t);return Qt.makeTensor(n,e,t)}const $s=sn({minimum_:function(e,t){let n=tn(e,"a","minimum"),r=tn(t,"b","minimum");[n,r]=Ut(n,r),"bool"===n.dtype&&(n=Nn(n,"int32"),r=Nn(r,"int32")),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("Minimum",s)}});const As=sn({mirrorPad_:function(e,t,n){J("reflect"===n||"symmetric"===n,(()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`));const r=tn(e,"x","mirrorPad");if(0===r.rank)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");J(t.length===r.rank,(()=>`Padding doesn't match input. Must be ${r.rank}. Got ${t.length}.`));const s="reflect"===n?1:0;for(let e=0;e<r.rank;e++)J(2===t[e].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),J(t[e][0]>=0&&t[e][0]<=r.shape[e]-s&&t[e][1]>=0&&t[e][1]<=r.shape[e]-s,(()=>`Padding in dimension ${e} cannot be greater than or equal to ${r.shape[e]-s} or less than 0 for input of shape ${r.shape}`));const a={paddings:t,mode:n},o={x:r};return Qt.runKernel("MirrorPad",o,a)}});const Ds=sn({mod_:function(e,t){let n=tn(e,"a","mod"),r=tn(t,"b","mod");[n,r]=Ut(n,r);const s={a:n,b:r};return Qt.runKernel("Mod",s)}});const Os=sn({moments_:function(e,t=null,n=!1){const r=oe(t,(e=tn(e,"x","moments")).shape),s=_s(e,r,n);let a=s.shape;n||(a=Or(s.shape,r));const o=Lr(ds(Nn(e,"float32"),$n(s,a)));return{mean:s,variance:_s(o,r,n)}}});const Ms=sn({multiRNNCell_:function(e,t,n,r){const s=tn(t,"data","multiRNNCell"),a=nn(n,"c","multiRNNCell"),o=nn(r,"h","multiRNNCell");let i=s;const u=[];for(let t=0;t<e.length;t++){const n=e[t](i,a[t],o[t]);u.push(n[0]),u.push(n[1]),i=n[1]}const p=[],l=[];for(let e=0;e<u.length;e+=2)p.push(u[e]),l.push(u[e+1]);return[p,l]}});const Cs=sn({multinomial_:function(e,t,n,r=!1){const s=tn(e,"logits","multinomial"),a=s.size,o=s.rank;if(a<2)throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);if(o>2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${o}`);n=n||Math.random();const i={logits:1===o?$n(s,[1,-1]):s},u={numSamples:t,seed:n,normalized:r},p=Qt.runKernel("Multinomial",i,u);return 1===o?$n(p,[p.size]):p}});const Fs=sn({notEqual_:function(e,t){let n=tn(e,"a","notEqual","string_or_numeric"),r=tn(t,"b","notEqual","string_or_numeric");[n,r]=Ut(n,r),kr(n.shape,r.shape);const s={a:n,b:r};return Qt.runKernel("NotEqual",s)}});const Rs=sn({oneHot_:function(e,t,n=1,r=0,s="int32"){if(t<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);const a={indices:tn(e,"indices","oneHot","int32")},o={dtype:s,depth:t,onValue:n,offValue:r};return Qt.runKernel("OneHot",a,o)}});const zs=sn({onesLike_:function(e){const t={x:tn(e,"x","onesLike")};return Qt.runKernel("OnesLike",t)}});const Vs=sn({outerProduct_:function(e,t){const n=tn(e,"v1","outerProduct"),r=tn(t,"v2","outerProduct");J(1===n.rank&&1===r.rank,(()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${r.rank}.`));const s=$n(n,[-1,1]),a=$n(r,[1,-1]);return Cn(s,a)}});const Ls=sn({pad_:function(e,t,n=0){const r=tn(e,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");const s={paddings:t,constantValue:n},a={x:r};return Qt.runKernel("PadV2",a,s)}});const Bs=sn({pad1d_:function(e,t,n=0){return J(2===t.length,(()=>"Invalid number of paddings. Must be length of 2.")),Ls(e,[t],n)}});const Ps=sn({pad2d_:function(e,t,n=0){return J(2===t.length&&2===t[0].length&&2===t[1].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),Ls(e,t,n)}});const Ks=sn({pad3d_:function(e,t,n=0){return J(3===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),Ls(e,t,n)}});const qs=sn({pad4d_:function(e,t,n=0){return J(4===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length&&2===t[3].length,(()=>"Invalid number of paddings. Must be length of 2 each.")),Ls(e,t,n)}});const Us=sn({spaceToBatchND_:function(e,t,n){const r=tn(e,"x","spaceToBatchND");J(r.rank>=1+t.length,(()=>`input rank ${r.rank} should be > than [blockShape] ${t.length}`)),J(n.length===t.length,(()=>`paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`)),J(r.shape.reduce(((e,r,s)=>s>0&&s<=t.length?e&&(r+n[s-1][0]+n[s-1][1])%t[s-1]==0:e),!0),(()=>`input spatial dimensions ${r.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`));const s={x:r},a={blockShape:t,paddings:n};return Qt.runKernel("SpaceToBatchND",s,a)}});const js=sn({pool_:function(e,t,n,r,s,a,o){null==s&&(s=[1,1]),null==a&&(a=1),0===r&&(r="valid");const i=tn(e,"x","maxPool");let u=i,p=!1;3===i.rank&&(p=!0,u=$n(i,[1,i.shape[0],i.shape[1],i.shape[2]])),J(_n(a,s),(()=>`Error in pool: Either strides or dilations must be 1. 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r=tn(e,"x","slice2d");return J(2===r.rank,(()=>`slice2d expects a rank-2 tensor, but got a rank-${r.rank} tensor`)),zn(r,t,n)}});const Ha=sn({slice3d_:function(e,t,n){const r=tn(e,"x","slice3d");return J(3===r.rank,(()=>`slice3d expects a rank-3 tensor, but got a rank-${r.rank} tensor`)),zn(r,t,n)}});const Za=sn({slice4d_:function(e,t,n){const r=tn(e,"x","slice4d");return J(4===r.rank,(()=>`slice4d expects a rank-4 tensor, but got a rank-${r.rank} tensor`)),zn(r,t,n)}});const Qa=sn({softmax_:function(e,t=-1){const n=tn(e,"logits","softmax","float32");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. 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n=tn(e,"x","squeeze","string_or_numeric");return $n(n,function(e,t){const n=[],r=[],s=null!=t&&Array.isArray(t)&&0===t.length,a=null==t||s?null:oe(t,e).sort();let o=0;for(let t=0;t<e.length;++t){if(null!=a){if(a[o]===t&&1!==e[t])throw new Error(`Can't squeeze axis ${t} since its dim '${e[t]}' is not 1`);(null==a[o]||a[o]>t)&&1===e[t]&&(n.push(e[t]),r.push(t)),a[o]<=t&&o++}1!==e[t]&&(n.push(e[t]),r.push(t))}return{newShape:n,keptDims:r}}(n.shape,t).newShape)}});const so=sn({stack_:function(e,t=0){const n=nn(e,"tensors","stack","string_or_numeric");J(n.length>=1,(()=>"Pass at least one tensor to tf.stack")),n.length>0&&J(t<=n[0].rank,(()=>"Axis must be <= rank of the tensor"));const r=n,s={axis:t};return Qt.runKernel("Pack",r,s)}});const ao=sn({step_:function(e,t=0){const n={x:tn(e,"x","step")},r={alpha:t};return Qt.runKernel("Step",n,r)}});const oo=sn({stridedSlice_:function(e,t,n,r,s=0,a=0,o=0,i=0,u=0){const p={x:tn(e,"x","stridedSlice","string_or_numeric")},l={begin:t,end:n,strides:r,beginMask:s,endMask:a,ellipsisMask:o,newAxisMask:i,shrinkAxisMask:u};return Qt.runKernel("StridedSlice",p,l)}});const io=sn({tan_:function(e){const t={x:tn(e,"x","tan","float32")};return Qt.runKernel("Tan",t)}});function uo(e,t,n){return Rr(e,t,Xt(e,n),n)}function po(e,t){te(e);const n=Xt(e,t);if(1!==n.length)throw new Error("tensor1d() requires values to be a flat/TypedArray");return Rr(e,null,n,t)}function lo(e,t,n){if(te(e),null!=t&&2!==t.length)throw new Error("tensor2d() requires shape to have two numbers");const r=Xt(e,n);if(2!==r.length&&1!==r.length)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return Rr(e,t,r,n)}function co(e,t,n){if(t.rank<1)throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);if(e.rank<1)throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${e.rank}.`);if("int32"!==t.dtype)throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${t.dtype}`);if(n.length<1)throw new Error(`Output rank must be greater or equal to 1, but got shape: ${n}`);if(0===n.length){if(0===t.size)throw new Error(`Indices specified for empty output. indices shape: ${t.shape}`);if(0===e.size)throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`)}!function(e,t,n){const r=t.rank>1?t.shape[t.rank-1]:1,s=t.rank>1?t.rank-1:1,a=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${r}, and batchDim: ${s}.`;if(n.rank<s)throw new Error(a+` update.rank < ${s}. `);if(e.length<r+(n.rank-s))throw new Error(a+` Output shape length < ${r+(n.rank-s)}`);if(n.rank!==s+e.length-r)throw new Error(a+" update.rank != 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new Error(`'k' passed to topk() must be <= the last dimension (${s}) but got ${t}`);const a={x:r},o={k:t,sorted:n},[i,u]=Qt.runKernel("TopK",a,o);return{values:i,indices:u}}});const fo=sn({truncatedNormal_:function(e,t=0,n=1,r,s){if(ge(e),null!=r&&"bool"===r)throw new Error("Unsupported data type $ { dtype }");const a=new Na(t,n,r,!0,s),o=Zn(e,r);for(let e=0;e<o.values.length;e++)o.values[e]=a.nextValue();return o.toTensor()}});const yo=sn({unique_:function(e,t=0){const n=tn(e,"x","unique","string_or_numeric");J(n.rank>0,(()=>"The input tensor must be at least 1D"));const r={x:n},s={axis:t},[a,o]=Qt.runKernel("Unique",r,s);return{values:a,indices:o}}});const go=sn({unsortedSegmentSum_:function(e,t,n){const r=tn(e,"x","unsortedSegmentSum"),s=tn(t,"segmentIds","unsortedSegmentSum","int32");J(se(n),(()=>"numSegments must be of dtype int"));const a={x:r,segmentIds:s},o={numSegments:n};return Qt.runKernel("UnsortedSegmentSum",a,o)}});const bo=sn({unstack_:function(e,t=0){const n=tn(e,"x","unstack","string_or_numeric");J(t>=-n.shape.length&&t<n.shape.length,(()=>`Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`));const r={value:n},s={axis:t};return Qt.runKernel("Unpack",r,s)}});const xo=async function(e){const t=tn(e,"condition","whereAsync","bool"),n=await t.data(),r=function(e,t){const n=[];for(let e=0;e<t.length;e++)t[e]&&n.push(e);const r=Zn(e,"int32"),s=Zn([n.length,e.length],"int32");for(let t=0;t<n.length;t++){const a=r.indexToLoc(n[t]),o=t*e.length;s.values.set(a,o)}return s.toTensor()}(t.shape,n);return e!==t&&t.dispose(),r};const No=async function(e,t,n){const r=tn(e,"tensor","boolMask"),s=tn(t,"mask","boolMask","bool"),a=null==n?0:n,o=s.rank,i=r.shape;J(o>0,(()=>"mask cannot be scalar")),ee(i.slice(a,a+o),s.shape,"mask's shape must match the first K dimensions of tensor's shape,");let u=1;for(let e=a;e<a+o;e++)u*=i[e];const p=i.slice(0,a).concat([u],i.slice(a+o)),l=$n(r,p),c=$n(s,[-1]),h=await xo(c),d=ro(h,[1]),m=Qr(l,d,a);return e!==r&&r.dispose(),t!==s&&s.dispose(),d.dispose(),l.dispose(),c.dispose(),h.dispose(),m};const wo=sn({transpose_:function(e,t,n){const r=tn(e,"x","transpose");if(null==t&&(t=r.shape.map(((e,t)=>t)).reverse()),J(r.rank===t.length,(()=>`Error in transpose: rank of input ${r.rank} must match length of perm ${t}.`)),t.forEach((e=>{J(e>=0&&e<r.rank,(()=>"All entries in 'perm' must be between 0 and "+(r.rank-1)+` but got ${t}`))})),r.rank<=1)return r.clone();const s={x:r},a={perm:t};return"complex64"===r.dtype?(o=()=>{let e=$a(r),t=Jr(r);return e=Qt.runKernel(Ae,{x:e},a),t=Qt.runKernel(Ae,{x:t},a),n&&(t=ls(t)),Jn(e,t)},Qt.tidy(o,i)):Qt.runKernel(Ae,s,a);var o,i}});const ko=sn({movingAverage_:function(e,t,n,r,s=!0){const a=tn(e,"v","movingAverage"),o=tn(t,"x","movingAverage"),i=tn(n,"decay","movingAverage");var u,p;p=o,J((u=a).dtype===p.dtype,(()=>`The dtypes of the first(${u.dtype}) and second(${p.dtype}) input must match`)),J(re(a.shape,o.shape),(()=>"Shape mismatch in v and x"));const l=zr(1),c=ds(l,i);let h=Fn(ds(o,a),c);if(s){J(null!=r,(()=>"When using zeroDebias: true, step is required."));const e=tn(r,"step","movingAverage");h=wr(h,ds(l,Fr(i,e)))}return pn(a,h)}});const To=sn({scatterND_:function(e,t,n){ge(n);const r=tn(e,"indices","scatterND","int32"),s=tn(t,"updates","scatterND");co(s,r,n);const a={indices:r,updates:s},o={shape:n};return Qt.runKernel("ScatterNd",a,o)}});const vo=sn({sparseToDense_:function(e,t,n,r=0){ge(n);const s=tn(e,"sparseIndices","sparseToDense","int32"),a=tn(t,"sparseValues","sparseToDense","string_or_numeric"),o=tn(r,"defaultValue","sparseToDense",a.dtype);!function(e,t,n,r){if("int32"!==e.dtype)throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);if(e.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${e.shape}.`);const s=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(`outputShape has incorrect number of 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t;if(e.shape.length===t.length){const n=[];for(let r=0;r<e.shape.length;r++)null==t[r]&&null!=e.shape[r]?n.push(e.shape[r]):n.push(t[r]);return n}return t}(s,n),o=1-t,i=wr(Zr(pn(_a(a,0,1,"float32",r),o)),o);return Fn(s,i)}});function Eo(e){return Math.floor(Math.pow(2,Math.ceil(Math.log(e)/Math.log(2))))}function Io(e,t,n){const r=1-e%2,s=new Float32Array(e);for(let a=0;a<e;++a){const o=2*Math.PI*a/(e+r-1);s[a]=t-n*Math.cos(o)}return po(s,"float32")}const $o=async function(e,t,n=1){const r=tn(e,"predictions","inTopK"),s=tn(t,"targets","inTopK");J(r.rank>1,(()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`)),J(r.rank-1===s.rank,(()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${s.rank}`)),ee(r.shape.slice(0,r.shape.length-1),s.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");const a=r.shape[r.shape.length-1];J(n>0&&n<=a,(()=>`'k' passed to 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p="NHWC"===a?i.shape[3]:i.shape[1],l="NHWC"===a?u.shape[3]:u.shape[1];J(p===n[2],(()=>`Error in conv2dDerFilter: depth of input ${p}) must match input depth in filter (${n[2]}.`)),J(l===n[3],(()=>`Error in conv2dDerFilter: depth of dy (${l}) must match output depth for filter (${n[3]}).`)),In("conv2dDerFilter",s,o);const c={x:i,dy:u},h={strides:r,pad:s,dataFormat:a,dimRoundingMode:o,filterShape:n};return Qt.runKernel("Conv2DBackpropFilter",c,h)}});function Do(e,t,n){if(null==n||"linear"===n)return e;if("relu"===n)return Fn(e,ao(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Oo(e,t){let n=t;const r=function(e,t){const n=[];for(let r=0;r<t.length;r++){const s=e[e.length-r-1],a=t.length-r-1,o=t[a];(null==s||1===s&&o>1)&&n.unshift(a)}return n}(e.shape,t.shape);return r.length>0&&(n=Br(n,r)),$n(n,e.shape)}function Mo(e,t,n,r){if("linear"===t)return e;if("relu"===t)return Da(e);if("elu"===t)return $r(e);if("relu6"===t)return Oa(e);if("prelu"===t)return Ws(e,n);if("leakyrelu"===t)return rs(e,r);if("sigmoid"===t)return Rn(e);throw new Error(`Unknown fused activation ${t}.`)}const Co=(e,t)=>!(e>0)||"linear"===t;const Fo=sn({fusedConv2d_:function({x:e,filter:t,strides:n,pad:r,dataFormat:s="NHWC",dilations:a=[1,1],dimRoundingMode:o,bias:i,activation:u="linear",preluActivationWeights:p,leakyreluAlpha:l}){if(u=u||"linear",!1===Co(Qt.state.gradientDepth,u)){J("NHWC"===s,(()=>`Error in fused conv2d: got dataFormat of ${s} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`));let c=sr(e,t,n,r,s,a,o);return null!=i&&(c=pn(c,i)),Mo(c,u,p,l)}const c=tn(e,"x","conv2d","float32"),h=tn(t,"filter","conv2d","float32");let d=c,m=!1;3===c.rank&&(m=!0,d=$n(c,[1,c.shape[0],c.shape[1],c.shape[2]])),J(4===d.rank,(()=>`Error in fused conv2d: input must be rank 4, but got rank ${d.rank}.`)),J(4===h.rank,(()=>`Error in fused conv2d: filter must be rank 4, but got rank ${h.rank}.`)),In("fused 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Got strides ${n} and dilations '${a}'`));const y=wn(d.shape,h.shape,n,a,r,o);let g,b;if(null!=i&&(g=tn(i,"bias","fused conv2d"),[g]=Ut(g,c),"NHWC"===s?kr(y.outShape,g.shape):(J(g.shape.length<=1,(()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${g.shape.length}.`)),J(0===g.shape.length||g.shape[0]===y.outChannels||1===g.shape[0],(()=>`Error in fused conv2d: bias shape (${g.shape}) is not compatible with the number of output channels (${y.outChannels})`)))),null!=p){const e=p.shape;if(J(e.length<=1||3===e.length,(()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${e.length}.`)),1===e.length)J(1===e[0]||e[0]===y.outChannels,(()=>`Error in fused conv2d: PReLU activation weights (${e}) is not compatible with the number of output channels (${y.outChannels}).`));else if(3===e.length)try{kr(e,y.outShape)}catch(t){const n=`Error in fused conv2d: PReLU activation weights (${e}) is not compatible with the output shape of the conv2d (${y.outShape}).`;throw Error(n)}b=tn(p,"prelu weights","fused conv2d")}const x=(e,t)=>{J("NHWC"===s,(()=>`Error in gradient of fused conv2D: got dataFormat of ${s} but only NHWC is currently supported.`));const[o,i,p,l]=t,c=Do(e,p,u);J(Sn(a),(()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. 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Got strides ${n} and dilations '${a}'`)),In("fused depthwiseConv2d",r,o);const f=wn(d.shape,h.shape,n,a,r,o,!0);let y,g;null!=i&&(y=tn(i,"bias","fused conv2d"),[y]=Ut(y,c),kr(f.outShape,y.shape)),null!=p&&(g=tn(p,"prelu weights","fused depthwiseConv2d"));const b=(e,t)=>{J(Sn(a),(()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`));const[s,i,p,l]=t,c=Do(e,p,u),h=zo(i.shape,c,s,n,r,a,o),d=Ro(i,c,s.shape,n,r,a,o);if(null!=l){return[h,d,Oo(y,c)]}return[h,d]},x={x:d,filter:h,bias:y,preluActivationWeights:g},N={strides:n,pad:r,dataFormat:s,dilations:a,dimRoundingMode:o,activation:u,leakyreluAlpha:l};if(null==i){const e=ps(((e,t,n)=>{let r=Qt.runKernel(Me,x,N);return n([t,e,r]),m&&(r=$n(r,[r.shape[1],r.shape[2],r.shape[3]])),{value:r,gradFunc:b}}));return e(d,h)}{const e=ps(((e,t,n,r)=>{let s=Qt.runKernel(Me,x,N);return r([t,e,s,n]),m&&(s=$n(s,[s.shape[1],s.shape[2],s.shape[3]])),{value:s,gradFunc:b}}));return e(d,h,y)}}});var Lo={__proto__:null,conv2d:Fo,depthwiseConv2d:Vo,matMul:sn({fusedMatMul_:function({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:s,activation:a="linear",preluActivationWeights:o,leakyreluAlpha:i=.2}){if(!1===Co(Qt.state.gradientDepth,a)){let u=Cn(e,t,n,r);return null!=s&&(u=pn(u,s)),Mo(u,a,o,i)}let u=tn(e,"a","fused matMul"),p=tn(t,"b","fused matMul");[u,p]=Ut(u,p);const l=n?u.shape[u.rank-2]:u.shape[u.rank-1],c=r?p.shape[p.rank-1]:p.shape[p.rank-2],h=n?u.shape[u.rank-1]:u.shape[u.rank-2],d=r?p.shape[p.rank-2]:p.shape[p.rank-1],m=u.shape.slice(0,-2),f=p.shape.slice(0,-2),y=ne(m),g=ne(f);J(l===c,(()=>`Error in fused matMul: inner shapes (${l}) and (${c}) of Tensors with shapes ${u.shape} and ${p.shape} and transposeA=${n} and transposeB=${r} must match.`));const b=kr(u.shape.slice(0,-2),p.shape.slice(0,-2)).concat([h,d]),x=$n(u,n?[y,l,h]:[y,h,l]),N=$n(p,r?[g,d,c]:[g,c,d]);let w,k;null!=s&&(w=tn(s,"bias","fused matMul"),[w]=Ut(w,u),kr(b,w.shape)),null!=o&&(k=tn(o,"prelu weights","fused matMul"));const T=(e,t)=>{const[o,i,u,p]=t,l=Do($n(e,u.shape),u,a);let c,h;if(n||r?!n&&r?(c=Cn(l,i,!1,!1),h=Cn(l,o,!0,!1)):n&&!r?(c=Cn(i,l,!1,!0),h=Cn(o,l,!1,!1)):(c=Cn(i,l,!0,!0),h=Cn(l,o,!0,!0)):(c=Cn(l,i,!1,!0),h=Cn(o,l,!0,!1)),null!=s){return[c,h,Oo(p,l)]}return[c,h]},v={a:x,b:N,bias:w,preluActivationWeights:k},S={transposeA:n,transposeB:r,activation:a,leakyreluAlpha:i};if(null==s){const e=ps(((e,t,n)=>{const r=Qt.runKernel(De,v,S);return n([e,t,r]),{value:$n(r,b),gradFunc:T}}));return e(x,N)}{const e=ps(((e,t,n,r)=>{const s=Qt.runKernel(De,v,S);return r([e,t,s,n]),{value:$n(s,b),gradFunc:T}}));return e(x,N,w)}}})};const Bo=sn({hammingWindow_:function(e){return Io(e,.54,.46)}});const Po=sn({hannWindow_:function(e){return Io(e,.5,.5)}});const Ko=sn({frame_:function(e,t,n,r=!1,s=0){let a=0;const o=[];for(;a+t<=e.size;)o.push(zn(e,a,t)),a+=n;if(r)for(;a<e.size;){const r=a+t-e.size,i=Mn([zn(e,a,t-r),Yn([r],s)]);o.push(i),a+=n}return 0===o.length?lo([],[0,t]):$n(Mn(o),[o.length,t])}});const qo=sn({stft_:function(e,t,n,r,s=Po){null==r&&(r=Eo(t));const a=Ko(e,t,n),o=Fn(a,s(t));return to(o,r)}});const Uo=sn({cropAndResize_:function(e,t,n,r,s="bilinear",a=0){const o=tn(e,"image","cropAndResize"),i=tn(t,"boxes","cropAndResize","float32"),u=tn(n,"boxInd","cropAndResize","int32"),p=i.shape[0];J(4===o.rank,(()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`)),J(2===i.rank&&4===i.shape[1],(()=>`Error in cropAndResize: boxes must be have size [${p},4] but had shape ${i.shape}.`)),J(1===u.rank&&u.shape[0]===p,(()=>`Error in cropAndResize: boxInd must be have size [${p}] but had shape ${i.shape}.`)),J(2===r.length,(()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`)),J(r[0]>=1&&r[1]>=1,(()=>`cropSize must be atleast [1,1], but was ${r}`)),J("bilinear"===s||"nearest"===s,(()=>`method must be bilinear or nearest, but was ${s}`));const l={image:o,boxes:i,boxInd:u},c={method:s,extrapolationValue:a,cropSize:r};return Qt.runKernel("CropAndResize",l,c)}});const jo=sn({flipLeftRight_:function(e){const t=tn(e,"image","flipLeftRight","float32");J(4===t.rank,(()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`));const n={image:t};return Qt.runKernel("FlipLeftRight",n,{})}});const Wo=sn({grayscaleToRGB_:function(e){const t=tn(e,"image","grayscaleToRGB"),n=t.rank-1,r=t.shape[n];J(t.rank>=2,(()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`)),J(1===r,(()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${r}.`));const s=new Array(t.rank);return s.fill(1,0,n),s[n]=3,Gr(t,s)}});const Go=sn({rgbToGrayscale_:function(e){const t=tn(e,"image","RGBToGrayscale"),n=t.rank-1,r=t.shape[n];J(t.rank>=2,(()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`)),J(3===r,(()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${r}.`));const s=t.dtype,a=Nn(t,"float32"),o=po([.2989,.587,.114]);let i;switch(t.rank){case 2:i=Ir("ij,j->i",a,o);break;case 3:i=Ir("ijk,k->ij",a,o);break;case 4:i=Ir("ijkl,l->ijk",a,o);break;case 5:i=Ir("ijklm,m->ijkl",a,o);break;case 6:i=Ir("ijklmn,n->ijklm",a,o);break;default:throw new Error("Not a valid tensor rank.")}return i=jr(i,-1),Nn(i,s)}});const Ho=sn({rotateWithOffset_:function(e,t,n=0,r=.5){const s=tn(e,"image","rotateWithOffset","float32");J(4===s.rank,(()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${s.rank}.`));const a={image:s},o={radians:t,fillValue:n,center:r};return Qt.runKernel("RotateWithOffset",a,o)}});function Zo(e,t,n,r,s,a){null==r&&(r=.5),null==s&&(s=Number.NEGATIVE_INFINITY),null==a&&(a=0);const o=e.shape[0];return n=Math.min(n,o),J(0<=r&&r<=1,(()=>`iouThreshold must be in [0, 1], but was '${r}'`)),J(2===e.rank,(()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`)),J(4===e.shape[1],(()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`)),J(1===t.rank,(()=>"scores must be a 1D tensor")),J(t.shape[0]===o,(()=>`scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`)),J(0<=a&&a<=1,(()=>`softNmsSigma must be in [0, 1], but was '${a}'`)),{maxOutputSize:n,iouThreshold:r,scoreThreshold:s,softNmsSigma:a}}const Qo=sn({nonMaxSuppression_:function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){const a=tn(e,"boxes","nonMaxSuppression","float32"),o=tn(t,"scores","nonMaxSuppression","float32"),i=Zo(a,o,n,r,s),u={maxOutputSize:n=i.maxOutputSize,iouThreshold:r=i.iouThreshold,scoreThreshold:s=i.scoreThreshold};return Qt.runKernel("NonMaxSuppressionV3",{boxes:a,scores:o},u)}});function Yo(e,t,n){const r=function(e,t,n){return function(e,t,n){let r=0,s=e.length,a=0,o=!1;for(;r<s;){a=r+(s-r>>>1);const i=n(t,e[a]);i>0?r=a+1:(s=a,o=!i)}return o?r:-r-1}(e,t,n||Xo)}(e,t,n),s=r<0?-(r+1):r;e.splice(s,0,t)}function Xo(e,t){return e>t?1:e<t?-1:0}function Jo(e,t,n,r,s,a,o=!1,i=!1,u=!1){const p=[];for(let e=0;e<t.length;e++)t[e]>s&&p.push({score:t[e],boxIndex:e,suppressBeginIndex:0});p.sort(ni);const l=a>0?-.5/a:0,c=[],h=[];for(;c.length<n&&p.length>0;){const t=p.pop(),{score:n,boxIndex:a,suppressBeginIndex:o}=t;if(n<s)break;let i=!1;for(let n=c.length-1;n>=o;--n){const o=ei(e,a,c[n]);if(o>=r){i=!0;break}if(t.score=t.score*ti(r,l,o),t.score<=s)break}t.suppressBeginIndex=c.length,i||(t.score===n?(c.push(a),h.push(t.score)):t.score>s&&Yo(p,t,ni))}const d=c.length,m=n-d;i&&m>0&&(c.push(...new Array(m).fill(0)),h.push(...new Array(m).fill(0)));const f={selectedIndices:c};return o&&(f.selectedScores=h),u&&(f.validOutputs=d),f}function ei(e,t,n){const r=e.subarray(4*t,4*t+4),s=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),o=Math.min(r[1],r[3]),i=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),p=Math.min(s[0],s[2]),l=Math.min(s[1],s[3]),c=Math.max(s[0],s[2]),h=Math.max(s[1],s[3]),d=(i-a)*(u-o),m=(c-p)*(h-l);if(d<=0||m<=0)return 0;const f=Math.max(a,p),y=Math.max(o,l),g=Math.min(i,c),b=Math.min(u,h),x=Math.max(g-f,0)*Math.max(b-y,0);return x/(d+m-x)}function ti(e,t,n){const r=Math.exp(t*n*n);return n<=e?r:0}function ni(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}const ri=async function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY){const a=tn(e,"boxes","nonMaxSuppressionAsync"),o=tn(t,"scores","nonMaxSuppressionAsync"),i=Zo(a,o,n,r,s);n=i.maxOutputSize,r=i.iouThreshold,s=i.scoreThreshold;const u=await Promise.all([a.data(),o.data()]),p=u[0],l=u[1],{selectedIndices:c}=function(e,t,n,r,s){return Jo(e,t,n,r,s,0)}(p,l,n,r,s);return a!==e&&a.dispose(),o!==t&&o.dispose(),po(c,"int32")};const si=sn({nonMaxSuppressionWithScore_:function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=0){const o=tn(e,"boxes","nonMaxSuppression"),i=tn(t,"scores","nonMaxSuppression"),u=Zo(o,i,n,r,s,a),p={boxes:o,scores:i},l={maxOutputSize:n=u.maxOutputSize,iouThreshold:r=u.iouThreshold,scoreThreshold:s=u.scoreThreshold,softNmsSigma:a=u.softNmsSigma},c=Qt.runKernel("NonMaxSuppressionV5",p,l);return{selectedIndices:c[0],selectedScores:c[1]}}});const ai=async function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=0){const o=tn(e,"boxes","nonMaxSuppressionAsync"),i=tn(t,"scores","nonMaxSuppressionAsync"),u=Zo(o,i,n,r,s,a);n=u.maxOutputSize,r=u.iouThreshold,s=u.scoreThreshold,a=u.softNmsSigma;const p=await Promise.all([o.data(),i.data()]),l=p[0],c=p[1],{selectedIndices:h,selectedScores:d}=function(e,t,n,r,s,a){return Jo(e,t,n,r,s,a,!0)}(l,c,n,r,s,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:po(h,"int32"),selectedScores:po(d)}};const oi=sn({nonMaxSuppressionPadded_:function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=!1){const o=tn(e,"boxes","nonMaxSuppression"),i=tn(t,"scores","nonMaxSuppression"),u=Zo(o,i,n,r,s,null),p={boxes:o,scores:i},l={maxOutputSize:u.maxOutputSize,iouThreshold:u.iouThreshold,scoreThreshold:u.scoreThreshold,padToMaxOutputSize:a},c=Qt.runKernel("NonMaxSuppressionV4",p,l);return{selectedIndices:c[0],validOutputs:c[1]}}});const ii=async function(e,t,n,r=.5,s=Number.NEGATIVE_INFINITY,a=!1){const o=tn(e,"boxes","nonMaxSuppressionAsync"),i=tn(t,"scores","nonMaxSuppressionAsync"),u=Zo(o,i,n,r,s,null),p=u.maxOutputSize,l=u.iouThreshold,c=u.scoreThreshold,[h,d]=await Promise.all([o.data(),i.data()]),{selectedIndices:m,validOutputs:f}=function(e,t,n,r,s,a){return Jo(e,t,n,r,s,0,!1,a,!0)}(h,d,p,l,c,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:po(m,"int32"),validOutputs:zr(f,"int32")}};const ui=sn({resizeBilinear_:function(e,t,n=!1,r=!1){const s=tn(e,"images","resizeBilinear");J(3===s.rank||4===s.rank,(()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${s.rank}.`)),J(2===t.length,(()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`)),J(!1===r||!1===n,(()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false."));let a=s,o=!1;3===s.rank&&(o=!0,a=$n(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const i={images:a},u={alignCorners:n,halfPixelCenters:r,size:t},p=Qt.runKernel("ResizeBilinear",i,u);return o?$n(p,[p.shape[1],p.shape[2],p.shape[3]]):p}});const pi=sn({resizeNearestNeighbor_:function(e,t,n=!1,r=!1){const s=tn(e,"images","resizeNearestNeighbor");J(3===s.rank||4===s.rank,(()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${s.rank}.`)),J(2===t.length,(()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`)),J("float32"===s.dtype||"int32"===s.dtype,(()=>"`images` must have `int32` or `float32` as dtype")),J(!1===r||!1===n,(()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false."));let a=s,o=!1;3===s.rank&&(o=!0,a=$n(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const i={images:a},u={alignCorners:n,halfPixelCenters:r,size:t},p=Qt.runKernel("ResizeNearestNeighbor",i,u);return o?$n(p,[p.shape[1],p.shape[2],p.shape[3]]):p}});const li=sn({threshold_:function(e,t="binary",n=!1,r=.5){const s=tn(e,"image","threshold"),a=s.shape[0]*s.shape[1];let o,i,u,p,l=Fn(po([r]),255);if(J(3===s.rank,(()=>`Error in threshold: image must be rank 3,but got rank ${s.rank}.`)),J(3===s.shape[2]||1===s.shape[2],(()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${s.shape[2]}.`)),J("int32"===s.dtype||"float32"===s.dtype,(()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${s.dtype}.`)),J("otsu"===t||"binary"===t,(()=>`Method must be binary or otsu, but was ${t}`)),3===s.shape[2]){[o,i,u]=eo(s,[1,1,1],-1);const e=Fn(o,.2989),t=Fn(i,.587),n=Fn(u,.114);p=pn(pn(e,t),n)}else p=e;if("otsu"===t){l=function(e,t){let n,r,s,a,o,i,u=po([-1]),p=po([0]),l=po([0]);for(let c=0;c<e.size-1;c++){n=zn(e,0,c+1),r=zn(e,c+1),o=wr(Br(n),t),i=wr(Br(r),t);const h=Br(Fn(n,Ia(0,n.size)));s=wr(h,Br(n));const d=Yn(r.shape,n.size),m=pn(Ia(0,r.size),d),f=Fn(r,m);a=wr(Br(f),Br(r));const y=ds(s,a),g=ds(s,a),b=Fn(o,i);l=Fn(Fn(b,y),g);const x=Yr(l,p);p=vr(x,l,p),u=vr(x,po([c]),u)}return u}(jn(Nn(Va(p),"int32"),uo([]),256),a)}const c=n?as(p,l):Yr(p,l);return Nn(Fn(c,255),"int32")}});const 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t=!0,e=eo(e,e.shape[0],0).map((e=>ro(e,[0])));J(e.length<=e[0].shape[0],(()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`));const n=[],r=e;for(let t=0;t<e.length;++t)n.push(Qt.tidy((()=>{let e=r[t];if(t>0)for(let r=0;r<t;++r){const t=Fn(Br(Fn(n[r],e)),n[r]);e=ds(e,t)}return wr(e,Kr(e,"euclidean"))})));return t?so(n,0):n}});function mi(e,t=!1){return Qt.tidy((()=>{J(2===e.shape.length,(()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`));const n=e.shape[0],r=e.shape[1];let s=Hr(n),a=On(e);const o=lo([[1]],[1,1]);let i=On(o);const u=n>=r?r:n;for(let e=0;e<u;++e){const t=a,u=i,p=s;[i,a,s]=Qt.tidy((()=>{const t=zn(a,[e,e],[n-e,1]),u=Kr(t),p=zn(a,[e,e],[1,1]),l=vr(Yr(p,0),lo([[-1]]),lo([[1]])),c=ds(p,Fn(l,u)),h=wr(t,c);i=1===h.shape[0]?On(o):Mn([o,zn(h,[1,0],[h.shape[0]-1,h.shape[1]])],0);const d=ls(wr(Cn(l,c),u)),m=zn(a,[e,0],[n-e,r]),f=Fn(d,i),y=wo(i);if(0===e)a=ds(m,Cn(f,Cn(y,m)));else{const 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s=null;null!=t&&(s=tn(t,"weights","computeWeightedLoss"));const a=null==s?r:Fn(r,s);if(n===yi.NONE)return a;if(n===yi.SUM)return Br(a);if(n===yi.MEAN){if(null==s)return _s(a);{const e=r.size/s.size,t=wr(Br(a),Br(s));return e>1?wr(t,zr(e)):t}}if(n===yi.SUM_BY_NONZERO_WEIGHTS){if(null==s)return wr(Br(a),zr(r.size));{const e=Fn(s,Is(r.shape)),t=Nn(Br(Fs(e,zr(0))),"float32");return wr(Br(a),t)}}throw Error(`Unknown reduction: ${n}`)}});const bi=sn({absoluteDifference_:function(e,t,n,r=yi.SUM_BY_NONZERO_WEIGHTS){const s=tn(e,"labels","absoluteDifference"),a=tn(t,"predictions","absoluteDifference");let o=null;null!=n&&(o=tn(n,"weights","absoluteDifference")),ee(s.shape,a.shape,"Error in absoluteDifference: ");const i=an(ds(s,a));return gi(i,o,r)}});const xi=sn({cosineDistance_:function(e,t,n,r,s=yi.SUM_BY_NONZERO_WEIGHTS){const a=tn(e,"labels","cosineDistance"),o=tn(t,"predictions","cosineDistance");let i=null;null!=r&&(i=tn(r,"weights","cosineDistance")),ee(a.shape,o.shape,"Error in 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new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Rr(e,t,r,n)},tensor6d:function(e,t,n){if(te(e),null!=t&&6!==t.length)throw new Error("tensor6d() requires shape to have six numbers");const r=Xt(e,n);if(6!==r.length&&1!==r.length)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return Rr(e,t=t||r,r,n)},tensorScatterUpdate:ho,tile:Gr,topk:mo,transpose:wo,truncatedNormal:fo,unique:yo,unsortedSegmentSum:go,unstack:bo,upperBound:function(e,t){return ws(e,t,"right")},variable:function(e,t=!0,n,r){return Qt.makeVariable(e,t,n,r)},where:vr,whereAsync:xo,zeros:Es,zerosLike:Sr};function Oi(e,n,r=""){if("number"!=typeof e&&"number"!=typeof n){t.util.assert(e.length===n.length,(()=>r+` Shapes ${e} and ${n} must match`));for(let s=0;s<e.length;s++){const a=e[s],o=n[s];t.util.assert(a<0||o<0||a===o,(()=>r+` Shapes ${e} and ${n} must match`))}}}function Mi(e){return"number"!=typeof e&&!e.some((e=>e<0))}function Ci(e,t,n){let r=Fi(e,n);const s=!Mi(r);if(s&&0===t.length)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${r}`);if(s&&t.forEach((e=>{r=Fi(e.shape,r)})),!Mi(r))throw new Error(`Non-fully-defined elementShape: ${r}`);return r}function Fi(e,t){if("number"==typeof e)return t;if("number"==typeof t)return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);const n=[];for(let r=0;r<e.length;++r){const s=e[r],a=t[r];if(s>=0&&a>=0&&s!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[r]=s>=0?s:a}return n}class Ri{constructor(e,n,r,s,a,o,i){this.name=e,this.dtype=n,this.maxSize=r,this.elementShape=s,this.identicalElementShapes=a,this.dynamicSize=o,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=t.scalar(0),t.keep(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.tensor.id)||t.tensor.dispose()})),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);const t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map((e=>this.read(e)))}write(e,n){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);const r=this.tensors[e]||{};if(n.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},\n          because the value dtype is ${n.dtype}, but TensorArray dtype is ${this.dtype}.`);if(0!==this.size()||null!=this.elementShape&&0!==this.elementShape.length||(this.elementShape=n.shape),Oi(this.elementShape,n.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),r.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(r.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);r.tensor=n,t.keep(n),r.written=!0,this.tensors[e]=r}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach(((e,n)=>this.write(e,t[n])))}gather(e,n){if(n&&n!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${n}`);if(e)e=e.slice(0,this.size());else{e=[];for(let t=0;t<this.size();t++)e.push(t)}if(0===e.length)return t.tensor([],[0].concat(this.elementShape));const r=this.readMany(e);return Oi(this.elementShape,r[0].shape,"TensorArray shape mismatch: "),t.stack(r,0)}concat(e){if(e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(0===this.size())return t.tensor([],[0].concat(this.elementShape));const n=[];for(let e=0;e<this.size();e++)n.push(e);const r=this.readMany(n);return Oi(this.elementShape,r[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${r[0].shape})`),t.concat(r,0)}scatter(e,n){if(n.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${n.dtype}`);if(e.length!==n.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${n.shape[0]}`);const r=Math.max(...e);if(!this.dynamicSize&&r>=this.maxSize)throw new Error(`Max index must be < array size (${r}  vs. ${this.maxSize})`);this.writeMany(e,t.unstack(n,0))}split(e,n){if(n.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${n.dtype}`);let r=0;const s=e.map((e=>(r+=e,r)));if(r!==n.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${r}, and tensor's shape is: ${n.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);const a=0===r?0:n.size/r,o=[];t.tidy((()=>{n=t.reshape(n,[1,r,a]);for(let r=0;r<e.length;++r){const i=[0,0===r?0:s[r-1],0],u=[1,e[r],a];o[r]=t.reshape(t.slice(n,i,u),this.elementShape)}return o}));const i=[];for(let t=0;t<e.length;t++)i[t]=t;this.writeMany(i,o)}}class zi{get id(){return this.idTensor.id}constructor(e,n,r,s=-1){this.tensors=e,this.elementShape=n,this.elementDtype=r,null!=e&&e.forEach((e=>{if(r!==e.dtype)throw new Error(`Invalid data types; op elements ${r}, but list elements ${e.dtype}`);Oi(n,e.shape,"TensorList shape mismatch: "),t.keep(e)})),this.idTensor=t.scalar(0),this.maxNumElements=s,t.keep(this.idTensor)}copy(){return new zi([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach((t=>{null!=e&&e.has(t.id)||t.dispose()})),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,n,r=-1){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(-1!==r&&this.tensors.length!==r)throw new Error(`Operation expected a list with ${r} elements but got a list with ${this.tensors.length} elements.`);Oi(e,this.elementShape,"TensorList shape mismatch: ");const s=Ci(this.elementShape,this.tensors,e);return t.tidy((()=>{const e=this.tensors.map((e=>t.reshape(e,s)));return t.stack(e,0)}))}popBack(e,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(0===this.size())throw new Error("Trying to pop from an empty list.");const r=Ci(this.elementShape,this.tensors,e),s=this.tensors.pop();return s.kept=!1,Oi(s.shape,e,"TensorList shape mismatch: "),t.reshape(s,r)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(Oi(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");t.keep(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(-1!==this.maxNumElements&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);const t=new zi([],this.elementShape,this.elementDtype,this.maxNumElements);t.tensors.length=e;for(let n=0;n<Math.min(this.tensors.length,e);++n)t.tensors[n]=this.tensors[n];return t}getItem(e,n,r){if(r!==this.elementDtype)throw new Error(`Invalid data types; op elements ${r}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(null==this.tensors[e])throw new Error(`element at index ${e} is null.`);Oi(this.tensors[e].shape,n,"TensorList shape mismatch: ");const s=Ci(this.elementShape,this.tensors,n);return t.reshape(this.tensors[e],s)}setItem(e,n){if(n.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n.dtype}, but list elements ${this.elementDtype}`);if(e<0||-1!==this.maxNumElements&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);Oi(this.elementShape,n.shape,"TensorList shape mismatch: "),t.keep(n),null!=this.tensors[e]&&(this.tensors[e].kept=!1),this.tensors[e]=n}gather(e,n,r){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);Oi(this.elementShape,r,"TensorList shape mismatch: "),e=e.slice(0,this.size());const s=Ci(this.elementShape,this.tensors,r);return 0===e.length?t.tensor([],[0].concat(s)):t.tidy((()=>{const n=e.map((e=>t.reshape(this.tensors[e],s)));return t.stack(n,0)}))}concat(e,n){if(e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Oi(this.elementShape,n,"TensorList shape mismatch: ");const r=Ci(this.elementShape,this.tensors,n);return 0===this.size()?t.tensor([],[0].concat(r)):t.tidy((()=>{const e=this.tensors.map((e=>t.reshape(e,r)));return t.concat(e,0)}))}}const Vi=async(e,n,r)=>{switch(e.op){case"If":case"StatelessIf":{const t=p("thenBranch",e,n,r),s=p("elseBranch",e,n,r),a=p("cond",e,n,r),o=p("args",e,n,r);return(await a.data())[0]?r.functionMap[t].executeFunctionAsync(o,r.tensorArrayMap,r.tensorListMap):r.functionMap[s].executeFunctionAsync(o,r.tensorArrayMap,r.tensorListMap)}case"While":case"StatelessWhile":{const t=p("body",e,n,r),s=p("cond",e,n,r),a=p("args",e,n,r),o=await r.functionMap[s].executeFunctionAsync(a,r.tensorArrayMap,r.tensorListMap),i=a.map((e=>e.id));let u=await o[0].data();o.forEach((e=>{e.kept||-1!==i.indexOf(e.id)||e.dispose()}));let l=a;for(;u[0];){const e=l;l=await r.functionMap[t].executeFunctionAsync(l,r.tensorArrayMap,r.tensorListMap);const n=l.map((e=>e.id));e.forEach((e=>{e.kept||-1!==i.indexOf(e.id)||-1!==n.indexOf(e.id)||e.dispose()}));const a=await r.functionMap[s].executeFunctionAsync(l,r.tensorArrayMap,r.tensorListMap);u=await a[0].data(),a.forEach((e=>{e.kept||-1!==i.indexOf(e.id)||-1!==n.indexOf(e.id)||e.dispose()}))}return l}case"LoopCond":return[y(p("pred",e,n,r))];case"Switch":{const t=p("pred",e,n,r);let s=p("data",e,n,r);return s.kept||(s=y(s)),(await t.data())[0]?[void 0,s]:[s,void 0]}case"Merge":{const t=e.inputNames.find((e=>void 0!==l(e,n,r)));if(t){return[y(l(t,n,r))]}return}case"Enter":{const t=p("frameName",e,n,r),s=p("tensor",e,n,r);return r.enterFrame(t),[y(s)]}case"Exit":{const t=p("tensor",e,n,r);return r.exitFrame(),[y(t)]}case"NextIteration":{const t=p("tensor",e,n,r);return r.nextIteration(),[y(t)]}case"TensorArrayV3":{const s=p("size",e,n,r),a=p("dtype",e,n,r),o=p("elementShape",e,n,r),i=p("dynamicSize",e,n,r),u=p("clearAfterRead",e,n,r),l=p("identicalElementShapes",e,n,r),c=p("name",e,n,r),h=new Ri(c,a,s,o,l,i,u);return r.addTensorArray(h),[h.idTensor,t.scalar(1)]}case"TensorArrayWriteV3":{const t=p("tensorArrayId",e,n,r),s=p("index",e,n,r),a=p("tensor",e,n,r),o=r.getTensorArray(t.id);return o.write(s,a),[o.idTensor]}case"TensorArrayReadV3":{const t=p("tensorArrayId",e,n,r),s=p("index",e,n,r);return[r.getTensorArray(t.id).read(s)]}case"TensorArrayGatherV3":{const t=p("tensorArrayId",e,n,r),s=p("indices",e,n,r),a=p("dtype",e,n,r);return[r.getTensorArray(t.id).gather(s,a)]}case"TensorArrayScatterV3":{const t=p("tensorArrayId",e,n,r),s=p("indices",e,n,r),a=p("tensor",e,n,r),o=r.getTensorArray(t.id);return o.scatter(s,a),[o.idTensor]}case"TensorArrayConcatV3":{const t=p("tensorArrayId",e,n,r),s=r.getTensorArray(t.id),a=p("dtype",e,n,r);return[s.concat(a)]}case"TensorArraySplitV3":{const t=p("tensorArrayId",e,n,r),s=p("tensor",e,n,r),a=p("lengths",e,n,r),o=r.getTensorArray(t.id);return o.split(a,s),[o.idTensor]}case"TensorArraySizeV3":{const s=p("tensorArrayId",e,n,r),a=r.getTensorArray(s.id);return[t.scalar(a.size(),"int32")]}case"TensorArrayCloseV3":{const t=p("tensorArrayId",e,n,r),s=r.getTensorArray(t.id);return s.clearAndClose(),[s.idTensor]}case"TensorListSetItem":{const t=p("tensorListId",e,n,r),s=p("index",e,n,r),a=p("tensor",e,n,r),o=r.getTensorList(t.id);return o.setItem(s,a),[o.idTensor]}case"TensorListGetItem":{const t=p("tensorListId",e,n,r),s=p("index",e,n,r),a=p("elementShape",e,n,r),o=p("elementDType",e,n,r);return[r.getTensorList(t.id).getItem(s,a,o)]}case"TensorListScatterV2":case"TensorListScatter":{const s=p("indices",e,n,r),a=function(e,n,r,s){if(n.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${n.length} vs. ${e.shape[0]}`);const a=Math.max(...n);if(null!=s&&-1!==s&&a>=s)throw new Error(`Max index must be < array size (${a}  vs. ${s})`);const o=new zi([],r,e.dtype,s),i=t.unstack(e,0);return n.forEach(((e,t)=>{o.setItem(e,i[t])})),o}(p("tensor",e,n,r),s,p("elementShape",e,n,r),p("numElements",e,n,r));return r.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{const t=p("elementShape",e,n,r),s=p("elementDType",e,n,r);let a;a="TensorListReserve"===e.op?"numElements":"maxNumElements";const o=p(a,e,n,r),i=function(e,t,n,r){return new zi([],e,t,r)}(t,s,0,"TensorListReserve"===e.op?-1:o);return r.addTensorList(i),[i.idTensor]}case"TensorListGather":{const t=p("tensorListId",e,n,r),s=p("indices",e,n,r),a=p("elementShape",e,n,r),o=p("elementDType",e,n,r);return[r.getTensorList(t.id).gather(s,o,a)]}case"TensorListStack":{const t=p("tensorListId",e,n,r),s=p("elementShape",e,n,r),a=p("elementDType",e,n,r),o=p("numElements",e,n,r);return[r.getTensorList(t.id).stack(s,a,o)]}case"TensorListFromTensor":{const s=function(e,n,r){const s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==r)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${r}`);Oi(e.shape.slice(1),n,"TensorList shape mismatch: ");const a=t.unstack(e);return new zi(a,n,s)}(p("tensor",e,n,r),p("elementShape",e,n,r),p("elementDType",e,n,r));return r.addTensorList(s),[s.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{const t=p("tensorListId",e,n,r),s=r.getTensorList(t.id),a=p("dtype",e,n,r),o=p("elementShape",e,n,r);return[s.concat(a,o)]}case"TensorListPushBack":{const t=p("tensorListId",e,n,r),s=p("tensor",e,n,r),a=r.getTensorList(t.id);return a.pushBack(s),[a.idTensor]}case"TensorListPopBack":{const t=p("tensorListId",e,n,r),s=p("elementShape",e,n,r),a=p("elementDType",e,n,r);return[r.getTensorList(t.id).popBack(s,a)]}case"TensorListSplit":{const s=p("tensor",e,n,r),a=p("elementShape",e,n,r),o=function(e,n,r){let s=0;const a=n.map((e=>(s+=e,s)));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${s}, and tensor's shape is: ${e.shape}`);const o=Fi(e.shape.slice(1),r),i=0===s?0:e.size/s,u=t.tidy((()=>{const r=[];e=t.reshape(e,[1,s,i]);for(let s=0;s<n.length;++s){const u=[0,0===s?0:a[s-1],0],p=[1,n[s],i];r[s]=t.reshape(t.slice(e,u,p),o)}return e.dispose(),r})),p=new zi([],r,e.dtype,n.length);for(let e=0;e<u.length;e++)p.setItem(e,u[e]);return p}(s,p("lengths",e,n,r),a);return r.addTensorList(o),[o.idTensor]}case"TensorListLength":{const s=p("tensorListId",e,n,r),a=r.getTensorList(s.id);return[t.scalar(a.size(),"int32")]}case"TensorListResize":{const t=p("tensorListId",e,n,r),s=p("size",e,n,r),a=r.getTensorList(t.id).resize(s);return r.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Li(e,t,n){const[r,s]=p("fusedOps",e,t,n),a="biasadd"===r,o=!a,i="prelu"===s,u="fusedbatchnorm"===r,l=p("numArgs",e,t,n);if(a){if(i&&2!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&a&&1!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");const c=p("strides",e,t,n),h=f(e,t,n),d=p("dataFormat",e,t,n).toUpperCase(),m=p("dilations",e,t,n);let[y,g]=p("args",e,t,n);o&&(g=y,y=void 0);return{stride:c,pad:h,dataFormat:d,dilations:m,biasArg:y,preluArg:g,activationFunc:s,leakyreluAlpha:p("leakyreluAlpha",e,t,n)}}function Bi(e,t,n){return{boxes:p("boxes",e,t,n),scores:p("scores",e,t,n),maxOutputSize:p("maxOutputSize",e,t,n),iouThreshold:p("iouThreshold",e,t,n),scoreThreshold:p("scoreThreshold",e,t,n),softNmsSigma:p("softNmsSigma",e,t,n)}}class Pi{get id(){return this.handle.id}constructor(e,n){this.keyDType=e,this.valueDType=n,this.handle=t.scalar(0),this.tensorMap=new Map,t.keep(this.handle)}clearAndClose(){this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return zr(this.size(),"int32")}async import(e,n){this.checkKeyAndValueTensor(e,n);const r=await e.data();return this.tensorMap.forEach((e=>e.dispose())),this.tensorMap.clear(),t.tidy((()=>{const e=t.unstack(n),s=r.length,a=e.length;t.util.assert(s===a,(()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`));for(let n=0;n<s;n++){const s=r[n],a=e[n];t.keep(a),this.tensorMap.set(s,a)}return this.handle}))}async find(e,n){this.checkKeyAndValueTensor(e,n);const r=await e.data();return t.tidy((()=>{const e=[];for(let t=0;t<r.length;t++){const s=r[t],a=this.findWithDefault(s,n);e.push(a)}return t.stack(e)}))}findWithDefault(e,t){const n=this.tensorMap.get(e);return null!=n?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}}function Ki(e,n,r,a,o=s.tidy){const i=((e,n,r)=>{switch(e.category){case"arithmetic":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[r.add(p("a",e,t,n),p("b",e,t,n))];case"AddN":return[r.addN(p("tensors",e,t,n))];case"FloorMod":case"Mod":return[r.mod(p("a",e,t,n),p("b",e,t,n))];case"Mul":return[r.mul(p("a",e,t,n),p("b",e,t,n))];case"RealDiv":case"Div":return[r.div(p("a",e,t,n),p("b",e,t,n))];case"DivNoNan":return[r.divNoNan(p("a",e,t,n),p("b",e,t,n))];case"FloorDiv":return[r.floorDiv(p("a",e,t,n),p("b",e,t,n))];case"Sub":return[r.sub(p("a",e,t,n),p("b",e,t,n))];case"Minimum":return[r.minimum(p("a",e,t,n),p("b",e,t,n))];case"Maximum":return[r.maximum(p("a",e,t,n),p("b",e,t,n))];case"Pow":return[r.pow(p("a",e,t,n),p("b",e,t,n))];case"SquaredDifference":return[r.squaredDifference(p("a",e,t,n),p("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"basic_math":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"Abs":case"ComplexAbs":return[r.abs(p("x",e,t,n))];case"Acos":return[r.acos(p("x",e,t,n))];case"Acosh":return[r.acosh(p("x",e,t,n))];case"Asin":return[r.asin(p("x",e,t,n))];case"Asinh":return[r.asinh(p("x",e,t,n))];case"Atan":return[r.atan(p("x",e,t,n))];case"Atan2":return[r.atan2(p("x",e,t,n),p("y",e,t,n))];case"Atanh":return[r.atanh(p("x",e,t,n))];case"Ceil":return[r.ceil(p("x",e,t,n))];case"Complex":return[r.complex(p("real",e,t,n),p("imag",e,t,n))];case"Cos":return[r.cos(p("x",e,t,n))];case"Cosh":return[r.cosh(p("x",e,t,n))];case"Elu":return[r.elu(p("x",e,t,n))];case"Erf":return[r.erf(p("x",e,t,n))];case"Exp":return[r.exp(p("x",e,t,n))];case"Expm1":return[r.expm1(p("x",e,t,n))];case"Floor":return[r.floor(p("x",e,t,n))];case"Log":return[r.log(p("x",e,t,n))];case"Log1p":return[r.log1p(p("x",e,t,n))];case"Imag":return[r.imag(p("x",e,t,n))];case"Neg":return[r.neg(p("x",e,t,n))];case"Reciprocal":return[r.reciprocal(p("x",e,t,n))];case"Real":return[r.real(p("x",e,t,n))];case"Relu":return[r.relu(p("x",e,t,n))];case"Round":return[r.round(p("x",e,t,n))];case"Selu":return[r.selu(p("x",e,t,n))];case"Sigmoid":return[r.sigmoid(p("x",e,t,n))];case"Sin":return[r.sin(p("x",e,t,n))];case"Sign":return[r.sign(p("x",e,t,n))];case"Sinh":return[r.sinh(p("x",e,t,n))];case"Softplus":return[r.softplus(p("x",e,t,n))];case"Sqrt":return[r.sqrt(p("x",e,t,n))];case"Square":return[r.square(p("x",e,t,n))];case"Tanh":return[r.tanh(p("x",e,t,n))];case"Tan":return[r.tan(p("x",e,t,n))];case"ClipByValue":return[r.clipByValue(p("x",e,t,n),p("clipValueMin",e,t,n),p("clipValueMax",e,t,n))];case"Relu6":return[r.relu6(p("x",e,t,n))];case"Rsqrt":return[r.rsqrt(l(e.inputNames[0],t,n))];case"LeakyRelu":return[r.leakyRelu(p("x",e,t,n),p("alpha",e,t,n))];case"Prelu":return[r.prelu(p("x",e,t,n),p("alpha",e,t,n))];case"IsNan":return[r.isNaN(l(e.inputNames[0],t,n))];case"IsInf":return[r.isInf(l(e.inputNames[0],t,n))];case"IsFinite":return[r.isFinite(l(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"control":return Vi(e,n,r);case"convolution":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"Conv1D":{const s=p("stride",e,t,n),a=p("pad",e,t,n),o=p("dataFormat",e,t,n).toUpperCase(),i=p("dilation",e,t,n);return[r.conv1d(p("x",e,t,n),p("filter",e,t,n),s,a,o,i)]}case"Conv2D":{const s=p("strides",e,t,n),a=f(e,t,n),o=p("dataFormat",e,t,n).toUpperCase(),i=p("dilations",e,t,n);return[r.conv2d(p("x",e,t,n),p("filter",e,t,n),[s[1],s[2]],a,o,[i[1],i[2]])]}case"_FusedConv2D":{const{stride:s,pad:a,dataFormat:o,dilations:i,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:h}=Li(e,t,n);return[r.fused.conv2d({x:p("x",e,t,n),filter:p("filter",e,t,n),strides:[s[1],s[2]],pad:a,dataFormat:o,dilations:[i[1],i[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:h})]}case"FusedDepthwiseConv2dNative":{const{stride:s,pad:a,dataFormat:o,dilations:i,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:h}=Li(e,t,n);return[r.fused.depthwiseConv2d({x:p("x",e,t,n),filter:p("filter",e,t,n),strides:[s[1],s[2]],pad:a,dataFormat:o,dilations:[i[1],i[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:h})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const s=p("outputShape",e,t,n),a=p("strides",e,t,n),o=f(e,t,n);return[r.conv2dTranspose(p("x",e,t,n),p("filter",e,t,n),s,[a[1],a[2]],o)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const s=p("strides",e,t,n),a=f(e,t,n),o=p("dilations",e,t,n),i=p("dataFormat",e,t,n).toUpperCase();return[r.depthwiseConv2d(p("input",e,t,n),p("filter",e,t,n),[s[1],s[2]],a,i,[o[1],o[2]])]}case"Conv3D":{const s=p("strides",e,t,n),a=p("pad",e,t,n),o=p("dataFormat",e,t,n).toUpperCase(),i=p("dilations",e,t,n);return[r.conv3d(p("x",e,t,n),p("filter",e,t,n),[s[1],s[2],s[3]],a,o,[i[1],i[2],i[3]])]}case"AvgPool":{const s=p("strides",e,t,n),a=p("pad",e,t,n),o=p("kernelSize",e,t,n);return[r.avgPool(p("x",e,t,n),[o[1],o[2]],[s[1],s[2]],a)]}case"MaxPool":{const s=p("strides",e,t,n),a=p("pad",e,t,n),o=p("kernelSize",e,t,n);return[r.maxPool(p("x",e,t,n),[o[1],o[2]],[s[1],s[2]],a)]}case"MaxPoolWithArgmax":{const s=p("strides",e,t,n),a=p("pad",e,t,n),o=p("kernelSize",e,t,n),i=p("includeBatchInIndex",e,t,n),{result:u,indexes:l}=r.maxPoolWithArgmax(p("x",e,t,n),[o[1],o[2]],[s[1],s[2]],a,i);return[u,l]}case"AvgPool3D":{const s=p("strides",e,t,n),a=p("pad",e,t,n),o=p("kernelSize",e,t,n);return[r.avgPool3d(p("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],a)]}case"MaxPool3D":{const s=p("strides",e,t,n),a=p("pad",e,t,n),o=p("kernelSize",e,t,n);return[r.maxPool3d(p("x",e,t,n),[o[1],o[2],o[3]],[s[1],s[2],s[3]],a)]}case"Dilation2D":{const s=p("strides",e,t,n),a=p("pad",e,t,n),o=p("dilations",e,t,n),i=s[1],u=s[2],l=o[1],c=o[2];return[r.dilation2d(p("x",e,t,n),p("filter",e,t,n),[i,u],a,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"creation":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"Fill":{const s=p("shape",e,t,n),a=p("dtype",e,t,n),o=p("value",e,t,n);return[r.fill(s,o,a)]}case"LinSpace":{const s=p("start",e,t,n),a=p("stop",e,t,n),o=p("num",e,t,n);return[r.linspace(s,a,o)]}case"Multinomial":{const s=p("logits",e,t,n),a=p("numSamples",e,t,n),o=p("seed",e,t,n);return[r.multinomial(s,a,o)]}case"OneHot":{const s=p("indices",e,t,n),a=p("depth",e,t,n),o=p("onValue",e,t,n),i=p("offValue",e,t,n),u=p("dtype",e,t,n);return[r.oneHot(s,a,o,i,u)]}case"Ones":return[r.ones(p("shape",e,t,n),p("dtype",e,t,n))];case"OnesLike":return[r.onesLike(p("x",e,t,n))];case"RandomStandardNormal":return[r.randomStandardNormal(p("shape",e,t,n),p("dtype",e,t,n),p("seed",e,t,n))];case"RandomUniform":return[r.randomUniform(p("shape",e,t,n),p("minval",e,t,n),p("maxval",e,t,n),p("dtype",e,t,n))];case"RandomUniformInt":return[r.randomUniformInt(p("shape",e,t,n),p("minval",e,t,n),p("maxval",e,t,n),p("seed",e,t,n))];case"Range":{const s=p("start",e,t,n),a=p("stop",e,t,n),o=p("step",e,t,n);return[r.range(s,a,o,p("dtype",e,t,n))]}case"TruncatedNormal":{const s=p("shape",e,t,n),a=p("mean",e,t,n),o=p("stdDev",e,t,n),i=p("seed",e,t,n);return[r.truncatedNormal(s,a,o,p("dtype",e,t,n),i)]}case"Zeros":return[r.zeros(p("shape",e,t,n),p("dtype",e,t,n))];case"ZerosLike":return[r.zerosLike(p("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"dynamic":return(async(e,t,n,r,s=Di)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:r,scores:a,maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:p}=Bi(e,t,n),l=await s.image.nonMaxSuppressionWithScoreAsync(r,a,o,i,u,p);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:r,scores:a,maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=Bi(e,t,n),l=p("padToMaxOutputSize",e,t,n),c=await s.image.nonMaxSuppressionPaddedAsync(r,a,o,i,u,l);return[c.selectedIndices,c.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:r,scores:a,maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=Bi(e,t,n);return[await s.image.nonMaxSuppressionAsync(r,a,o,i,u)]}case"Where":{const r=s.cast(p("condition",e,t,n),"bool"),a=[await s.whereAsync(r)];return r.dispose(),a}case"ListDiff":return s.setdiff1dAsync(p("x",e,t,n),p("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r);case"evaluation":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"LowerBound":{const s=p("sortedSequence",e,t,n),a=p("values",e,t,n);return[r.lowerBound(s,a)]}case"TopKV2":{const s=p("x",e,t,n),a=p("k",e,t,n),o=p("sorted",e,t,n),i=r.topk(s,a,o);return[i.values,i.indices]}case"UpperBound":{const s=p("sortedSequence",e,t,n),a=p("values",e,t,n);return[r.upperBound(s,a)]}case"Unique":{const s=p("x",e,t,n),a=r.unique(s);return[a.values,a.indices]}case"UniqueV2":{const s=p("x",e,t,n),a=p("axis",e,t,n),o=r.unique(s,a);return[o.values,o.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"image":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"ResizeBilinear":{const s=p("images",e,t,n),a=p("size",e,t,n),o=p("alignCorners",e,t,n),i=p("halfPixelCenters",e,t,n);return[r.image.resizeBilinear(s,[a[0],a[1]],o,i)]}case"ResizeNearestNeighbor":{const s=p("images",e,t,n),a=p("size",e,t,n),o=p("alignCorners",e,t,n),i=p("halfPixelCenters",e,t,n);return[r.image.resizeNearestNeighbor(s,[a[0],a[1]],o,i)]}case"CropAndResize":{const s=p("image",e,t,n),a=p("boxes",e,t,n),o=p("boxInd",e,t,n),i=p("cropSize",e,t,n),u=p("method",e,t,n),l=p("extrapolationValue",e,t,n);return[r.image.cropAndResize(s,a,o,i,u,l)]}case"ImageProjectiveTransformV3":{const s=p("images",e,t,n),a=p("transforms",e,t,n),o=p("outputShape",e,t,n),i=p("fillValue",e,t,n),u=p("interpolation",e,t,n),l=p("fillMode",e,t,n);return[r.image.transform(s,a,u.toLowerCase(),l.toLowerCase(),i,o)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"graph":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const s=p("default",e,t,n);return[l(e.name,t,n)||s];case"Placeholder":return[l(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":case"Snapshot":return[y(p("x",e,t,n))];case"IdentityN":return p("x",e,t,n).map((e=>y(e)));case"Shape":return[r.tensor1d(p("x",e,t,n).shape,"int32")];case"ShapeN":return p("x",e,t,n).map((e=>r.tensor1d(e.shape)));case"Size":return[r.scalar(p("x",e,t,n).size,"int32")];case"Rank":return[r.scalar(p("x",e,t,n).rank,"int32")];case"NoOp":return[r.scalar(1)];case"Print":const a=p("x",e,t,n),o=p("data",e,t,n),i=p("message",e,t,n),u=p("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let e=0;e<o.length;e++)console.log(Array.prototype.slice.call(o[e].dataSync()).slice(0,u));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"logical":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"Equal":return[r.equal(p("a",e,t,n),p("b",e,t,n))];case"NotEqual":return[r.notEqual(p("a",e,t,n),p("b",e,t,n))];case"Greater":return[r.greater(p("a",e,t,n),p("b",e,t,n))];case"GreaterEqual":return[r.greaterEqual(p("a",e,t,n),p("b",e,t,n))];case"Less":return[r.less(p("a",e,t,n),p("b",e,t,n))];case"LessEqual":return[r.lessEqual(p("a",e,t,n),p("b",e,t,n))];case"LogicalAnd":return[r.logicalAnd(p("a",e,t,n),p("b",e,t,n))];case"LogicalNot":return[r.logicalNot(p("a",e,t,n))];case"LogicalOr":return[r.logicalOr(p("a",e,t,n),p("b",e,t,n))];case"Select":case"SelectV2":return[r.where(p("condition",e,t,n),p("a",e,t,n),p("b",e,t,n))];case"BitwiseAnd":return[r.bitwiseAnd(p("a",e,t,n),p("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"matrices":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[r.matMul(p("a",e,t,n),p("b",e,t,n),p("transposeA",e,t,n),p("transposeB",e,t,n))];case"Einsum":return[r.einsum(p("equation",e,t,n),...p("tensors",e,t,n))];case"Transpose":return[r.transpose(p("x",e,t,n),p("perm",e,t,n))];case"_FusedMatMul":const[s,a]=p("fusedOps",e,t,n),o="biasadd"===s,i="prelu"===a,u=p("numArgs",e,t,n),l=p("leakyreluAlpha",e,t,n);if(o){if(i&&2!==u)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&1!==u)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[c,h]=p("args",e,t,n);return[r.fused.matMul({a:p("a",e,t,n),b:p("b",e,t,n),transposeA:p("transposeA",e,t,n),transposeB:p("transposeB",e,t,n),bias:c,activation:a,preluActivationWeights:h,leakyreluAlpha:l})];case"MatrixBandPart":return[r.linalg.bandPart(p("a",e,t,n),p("numLower",e,t,n),p("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"normalization":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(p("x",e,t,n),p("axis",e,t,n),p("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[r.batchNorm(p("x",e,t,n),p("mean",e,t,n),p("variance",e,t,n),p("offset",e,t,n),p("scale",e,t,n),p("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(p("x",e,t,n),p("radius",e,t,n),p("bias",e,t,n),p("alpha",e,t,n),p("beta",e,t,n))];case"Softmax":return[r.softmax(p("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(p("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"ragged":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"RaggedGather":{const{outputNestedSplits:s,outputDenseValues:a}=r.raggedGather(p("paramsNestedSplits",e,t,n),p("paramsDenseValues",e,t,n),p("indices",e,t,n),p("outputRaggedRank",e,t,n));return s.concat(a)}case"RaggedRange":{const{rtNestedSplits:s,rtDenseValues:a}=r.raggedRange(p("starts",e,t,n),p("limits",e,t,n),p("splits",e,t,n));return[s,a]}case"RaggedTensorToTensor":return[r.raggedTensorToTensor(p("shape",e,t,n),p("values",e,t,n),p("defaultValue",e,t,n),p("rowPartitionTensors",e,t,n),p("rowPartitionTypes",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"reduction":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"Max":{const s=p("axis",e,t,n),a=p("keepDims",e,t,n);return[r.max(p("x",e,t,n),s,a)]}case"Mean":{const s=p("axis",e,t,n),a=p("keepDims",e,t,n);return[r.mean(p("x",e,t,n),s,a)]}case"Min":{const s=p("axis",e,t,n),a=p("keepDims",e,t,n);return[r.min(p("x",e,t,n),s,a)]}case"Sum":{const s=p("axis",e,t,n),a=p("keepDims",e,t,n);return[r.sum(p("x",e,t,n),s,a)]}case"All":{const s=p("axis",e,t,n),a=p("keepDims",e,t,n);return[r.all(p("x",e,t,n),s,a)]}case"Any":{const s=p("axis",e,t,n),a=p("keepDims",e,t,n);return[r.any(p("x",e,t,n),s,a)]}case"ArgMax":{const s=p("axis",e,t,n);return[r.argMax(p("x",e,t,n),s)]}case"ArgMin":{const s=p("axis",e,t,n);return[r.argMin(p("x",e,t,n),s)]}case"Prod":{const s=p("axis",e,t,n),a=p("keepDims",e,t,n);return[r.prod(p("x",e,t,n),s,a)]}case"Cumprod":{const s=p("axis",e,t,n),a=p("exclusive",e,t,n),o=p("reverse",e,t,n);return[r.cumprod(p("x",e,t,n),s,a,o)]}case"Cumsum":{const s=p("axis",e,t,n),a=p("exclusive",e,t,n),o=p("reverse",e,t,n);return[r.cumsum(p("x",e,t,n),s,a,o)]}case"Bincount":const s=p("x",e,t,n),a=p("weights",e,t,n),o=p("size",e,t,n);return[r.bincount(s,a,o)];case"DenseBincount":{const s=p("x",e,t,n),a=p("weights",e,t,n),o=p("size",e,t,n),i=p("binaryOutput",e,t,n);return[r.denseBincount(s,a,o,i)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"slice_join":return o((()=>((e,n,r,s=Di)=>{switch(e.op){case"ConcatV2":case"Concat":{const t=p("n",e,n,r),a=p("axis",e,n,r);let o=p("tensors",e,n,r);return o=o.slice(0,t),[s.concat(o,a)]}case"Gather":{const t=p("x",e,n,r),a=p("indices",e,n,r);return[s.gather(t,s.cast(a,"int32"),0)]}case"GatherV2":{const t=p("axis",e,n,r),a=p("batchDims",e,n,r),o=p("x",e,n,r),i=p("indices",e,n,r);return[s.gather(o,s.cast(i,"int32"),t,a)]}case"Reverse":{const t=p("dims",e,n,r),a=[];for(let e=0;e<t.length;e++)t[e]&&a.push(e);const o=p("x",e,n,r);return[s.reverse(o,a)]}case"ReverseV2":{const t=p("axis",e,n,r),a=p("x",e,n,r);return[s.reverse(a,t)]}case"Slice":{const t=p("begin",e,n,r),a=p("size",e,n,r);return[s.slice(p("x",e,n,r),t,a)]}case"StridedSlice":{const t=p("begin",e,n,r),a=p("end",e,n,r),o=p("strides",e,n,r),i=p("beginMask",e,n,r),u=p("endMask",e,n,r),l=p("ellipsisMask",e,n,r),c=p("newAxisMask",e,n,r),h=p("shrinkAxisMask",e,n,r),d=p("x",e,n,r);return[s.stridedSlice(d,t,a,o,i,u,l,c,h)]}case"Pack":return t.tidy((()=>{const a=p("axis",e,n,r),o=p("tensors",e,n,r),i=o[0].shape,u=s.squeeze(o[0]).shape,l=o.map((e=>{const n=t.util.arraysEqual(e.shape,i);if(!n&&!t.util.arraysEqual(s.squeeze(e).shape,u))throw new Error("the input tensors shape does not match");return n?e:s.reshape(e,i)}));return[s.stack(l,a)]}));case"Unpack":{const t=p("axis",e,n,r),a=p("tensor",e,n,r);return s.unstack(a,t)}case"Tile":{const t=p("reps",e,n,r);return[s.tile(p("x",e,n,r),t)]}case"Split":case"SplitV":{const t=p("axis",e,n,r),a=p("numOrSizeSplits",e,n,r),o=p("x",e,n,r);return s.split(o,a,t)}case"ScatterNd":{const t=p("indices",e,n,r),a=p("values",e,n,r),o=p("shape",e,n,r);return[s.scatterND(t,a,o)]}case"GatherNd":{const t=p("x",e,n,r),a=p("indices",e,n,r);return[s.gatherND(t,a)]}case"SparseToDense":{const t=p("sparseIndices",e,n,r),a=p("outputShape",e,n,r),o=p("sparseValues",e,n,r),i=p("defaultValue",e,n,r);return[s.sparseToDense(t,o,a,o.dtype===i.dtype?i:s.cast(i,o.dtype))]}case"TensorScatterUpdate":{const t=p("indices",e,n,r),a=p("values",e,n,r),o=p("tensor",e,n,r);return[s.tensorScatterUpdate(o,t,a)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"sparse":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"SparseFillEmptyRows":{const{outputIndices:s,outputValues:a,emptyRowIndicator:o,reverseIndexMap:i}=r.sparse.sparseFillEmptyRows(p("indices",e,t,n),p("values",e,t,n),p("denseShape",e,t,n),p("defaultValue",e,t,n));return[s,a,o,i]}case"SparseReshape":{const{outputIndices:s,outputShape:a}=r.sparse.sparseReshape(p("inputIndices",e,t,n),p("inputShape",e,t,n),p("newShape",e,t,n));return[s,a]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(p("data",e,t,n),p("indices",e,t,n),p("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(p("data",e,t,n),p("indices",e,t,n),p("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"spectral":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"FFT":return[r.fft(p("x",e,t,n))];case"IFFT":return[r.ifft(p("x",e,t,n))];case"RFFT":return[r.rfft(p("x",e,t,n))];case"IRFFT":return[r.irfft(p("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"string":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"StaticRegexReplace":return[r.string.staticRegexReplace(p("input",e,t,n),p("pattern",e,t,n),p("rewrite",e,t,n),p("replaceGlobal",e,t,n))];case"StringNGrams":{const{nGrams:s,nGramsSplits:a}=r.string.stringNGrams(p("data",e,t,n),p("dataSplits",e,t,n),p("separator",e,t,n),p("nGramWidths",e,t,n),p("leftPad",e,t,n),p("rightPad",e,t,n),p("padWidth",e,t,n),p("preserveShortSequences",e,t,n));return[s,a]}case"StringSplit":{const{indices:s,values:a,shape:o}=r.string.stringSplit(p("input",e,t,n),p("delimiter",e,t,n),p("skipEmpty",e,t,n));return[s,a,o]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(p("input",e,t,n),p("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"transformation":return o((()=>((e,t,n,r=Di)=>{switch(e.op){case"Cast":return[r.cast(p("x",e,t,n),p("dtype",e,t,n))];case"ExpandDims":{const s=p("axis",e,t,n);return[r.expandDims(p("x",e,t,n),s)]}case"Squeeze":{const s=p("axis",e,t,n);return[r.squeeze(p("x",e,t,n),s)]}case"Reshape":return[r.reshape(p("x",e,t,n),p("shape",e,t,n))];case"EnsureShape":return[r.ensureShape(p("x",e,t,n),p("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(p("x",e,t,n),p("padding",e,t,n),p("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(p("x",e,t,n),p("padding",e,t,n),p("constantValue",e,t,n))];case"SpaceToBatchND":{const s=p("blockShape",e,t,n),a=p("paddings",e,t,n);return[r.spaceToBatchND(p("x",e,t,n),s,a)]}case"BatchToSpaceND":{const s=p("blockShape",e,t,n),a=p("crops",e,t,n);return[r.batchToSpaceND(p("x",e,t,n),s,a)]}case"DepthToSpace":{const s=p("blockSize",e,t,n),a=p("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(p("x",e,t,n),s,a)]}case"BroadcastTo":return[r.broadcastTo(p("x",e,t,n),p("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(p("s0",e,t,n),p("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r)));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{const s=r.getHashTableHandleByName(e.name);if(null!=s)return[s];{const s=p("keyDType",e,t,n),a=p("valueDType",e,t,n),o=new Pi(s,a);return r.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{const s=p("tableHandle",e,t,n,r),a=p("keys",e,t,n),o=p("values",e,t,n),i=r.getHashTableById(s.id);return[await i.import(a,o)]}case"LookupTableFind":case"LookupTableFindV2":{const s=p("tableHandle",e,t,n,r),a=p("keys",e,t,n),o=p("defaultValue",e,t,n),i=r.getHashTableById(s.id);return[await i.find(a,o)]}case"LookupTableSize":case"LookupTableSizeV2":{const s=p("tableHandle",e,t,n,r);return[r.getHashTableById(s.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,n,r,a);case"custom":const s=u(e.op);if(s&&s.customExecutor)return s.customExecutor(new Y(e,n,r));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.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,n,r);return s.util.isPromise(i)?i.then((e=>[].concat(e))):[].concat(i)}class qi{constructor(e={},t={},n={},r={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.parseNodeNameCache=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(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const 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((e=>0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.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))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const 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))}}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(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function Ui(e,t,n,r){const s=new Set,a=[];let o=null,i=null;const u=new Set,p=new Set(Object.keys(e).map((e=>m(e)[0])));r=r||[];const l=new Set(r.map((e=>m(e.name)[0]))),c=[...t];for(;c.length>0;){const e=c.pop();(Qi(e)||Yi(e)||Xi(e))&&null==o&&(o=e,i=o.children.map((e=>e.name)).filter((e=>s.has(e)))),s.add(e.name),null==n[e.name]&&(p.has(e.name)||l.has(e.name)||(0!==e.inputs.length?e.inputs.forEach((e=>{u.has(e.name)||(u.add(e.name),c.push(e))})):a.push(e.name)))}return{inputs:e,outputs:t,usedNodes:s,missingInputs:a,dynamicNode:o,syncInputs:i}}function ji(e,t){const{usedNodes:n,inputs:r}=t,s=Object.keys(r).map((e=>m(e)[0])).map((t=>e.nodes[t])),a=e.initNodes||[],o=e=>n.has("string"==typeof e?e:e.name);function i(e){return[...new Map(e.map((e=>[e.name,e]))).values()]}const u=i([...s,...e.weights,...a]).filter(o),p=i([...u,...Object.values(e.nodes)]).filter(o),l=new Map(p.map((e=>[e.name,e]))),c={};for(const e of p){c[e.name]=c[e.name]||0;for(const t of e.children)o(t)||(c[t.name]=Number.POSITIVE_INFINITY),c[t.name]=(c[t.name]||0)+1}const h=Object.entries(c).filter((([,e])=>0===e)).map((([e])=>e)),d=[...h];for(;h.length>0;){const e=h.pop(),t=l.get(e);for(const e of t.children.filter(o))0==--c[e.name]&&(d.push(e.name),h.push(e.name))}const f=function(e,t){const n=new Map(e.map((e=>[e.name,e]))),r=t.map((e=>e.name)),s=new Set(r);for(;r.length>0;){const e=r.pop(),t=n.get(e);for(const e of t.children)n.has(e.name)&&!s.has(e.name)&&(s.add(e.name),r.push(e.name))}return e.filter((e=>s.has(e.name)))}(d.map((e=>l.get(e))),u);return function(e,t){const n=new Map(e.map(((e,t)=>[e.name,t]))),r=new Set(t.map((e=>e.name))),s=e=>r.has("string"==typeof e?e:e.name),a=new Set(e.map((e=>e.name))),o=e=>a.has("string"==typeof e?e:e.name);for(const t of e){for(const e of t.children.filter(o)){if(!n.has(e.name))throw new Wi(`Child ${e.name} of node ${t.name} is unreachable.`);if(n.get(t.name)>n.get(e.name))throw new Wi(`Node ${t.name} is scheduled to run after its child ${e.name}.`)}if(!s(t))for(const e of t.inputs){if(!n.has(e.name))throw new Wi(`Input ${e.name} of node ${t.name} is unreachable.`);if(n.get(e.name)>n.get(t.name))throw new Wi(`Node ${t.name} is scheduled to run before its input ${e.name}.`)}}}(f,u),f}class Wi extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}}const Gi=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),Hi=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),Zi=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function Qi(e){return Gi.has(e.op)}function Yi(e){return Hi.has(e.op)}function Xi(e){return Zi.has(e.op)}class Ji{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map((t=>e[t].map((e=>e.id))));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get outputs(){return this._outputs.map((e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0})))}get inputNodes(){return this._inputs.map((e=>e.signatureKey||e.name))}get outputNodes(){return this._outputs.map((e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t}))}get functions(){return Object.keys(this._functions).reduce(((e,t)=>(e[t]=this._functions[t].signature,e)),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach((t=>{this._functionExecutorMap[t]=new Ji(e.functions[t],this)}))}getCompilationKey(e,t){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(e,t){const n=Ui(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:s,syncInputs:a}=n;if(null!=s)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(r.length>0){const n=t.map((e=>e.name)),s=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${s}]. Missing the following inputs: [${r}]`)}const o=ji(this.graph,n),i=function(e){const t=new Map(e.map(((e,t)=>[e.name,t]))),n=Number.MAX_SAFE_INTEGER,r=e.map(((e,t)=>Qi(e)?n:t)),s=e=>{const n=r[t.get(e.name)];return null==n?-1:n},a=e.map(((e,t)=>e.children.map(s).reduce(((e,t)=>Math.max(e,t)),r[t]))),o=new Map;for(let t=0;t<e.length;++t){const r=a[t];if(r===n)continue;const s=e[t],i=e[r];o.has(i.name)||o.set(i.name,[]),o.get(i.name).push(s)}return o}(o);return{orderedNodes:o,nodeLiveUntilMap:i}}cloneAndKeepTensor(e){if(null==e)return null;const n=e.clone();return t.keep(n),n}cloneTensorList(e){if(!e)return null;const t=e.map((e=>this.cloneAndKeepTensor(e)));return t}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map((([e,t])=>[e,this.cloneTensorList(t)])))}execute(e,n){this.disposeIntermediateTensors(),e=this.mapInputs(e);const r=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),n=this.mapOutputs(n),this.checkOutputs(n);const s=r.map((e=>this.graph.nodes[m(e)[0]])),a=n.map((e=>m(e)[0])),o=new Set(a);let i=a.map((e=>this.graph.nodes[e]));0===i.length&&(i=this._outputs);const u=this.getCompilationKey(s,i);let p=this.compiledMap.get(u);null==p&&(p=this.compile(e,i),this.compiledMap.set(u,p));try{this.keepIntermediateTensors=t.env().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}const c={},h={};return t.tidy((()=>{const r=new qi(this.weightMap,c,h,this.functionExecutorMap,this.parseNodeNameCache),s=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach((t=>{const[n,a]=m(t,r),o=[];o[a]=e[t],s[n]=o,this.keepIntermediateTensors&&(this.clonedTensorsMap[n]=this.cloneTensorList(o))}));const a=this.getFrozenTensorIds(s),{orderedNodes:i,nodeLiveUntilMap:u}=p;for(const e of i){if(s[e.name])continue;const n=Ki(e,s,r,this._resourceManager);if(t.util.isPromise(n))throw new Error(`The execution of the op '${e.op}' returned a promise. Please use model.executeAsync() instead.`);s[e.name]=n,this.keepIntermediateTensors&&(this.clonedTensorsMap[e.name]=this.cloneTensorList(n)),this.checkTensorForDisposalWithNodeLiveUntilInfo(e,s,r,a,o,u.get(e.name))}return null==this.parent&&r.dispose(a),n.map((e=>l(e,s,r)))}))}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map((t=>e[t])).map((e=>e.map((e=>e.id)))));return new Set(t)}checkTensorForDisposal(e,t,n,r,s,a,o){if(!Qi(t)&&!a.has(e)){for(const r of n[e])null!=r&&(o[r.id]=(o[r.id]||0)+t.children.length);for(const e of t.inputs){if(Qi(e))continue;const t=c(e.name,n,r);if(null!=t)for(const e of t){if(!e||e.kept||s.has(e.id))continue;const t=o[e.id];1===t?(e.dispose(),delete o[e.id]):null!=t&&o[e.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,n,r,s,a){function o(e){return Qi(e)||s.has(e.name)}if(!Qi(e)&&null!=a)for(const e of a){if(o(e))continue;const s=c(e.name,t,n);for(const e of s)!e||e.kept||r.has(e.id)||e.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach((e=>{for(const t of e)t&&!t.isDisposed&&t.dispose()})),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,n,r=!1,s={},a={}){this.disposeIntermediateTensors(),r||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),n=this.mapOutputs(n),this.checkOutputs(n));try{this.keepIntermediateTensors=t.env().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(e){this.keepIntermediateTensors=!1,console.warn(e.message)}const o=new qi(this.weightMap,s,a,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));const i=await this.executeWithControlFlow(e,o,n,r),u=n.map((e=>l(e,i,o))),p=u.map((e=>e.id)),c=Object.keys(e).map((t=>e[t].id)),h=new Set([...p,...c,...this.weightIds]);return Object.values(i).forEach((e=>{e.forEach((e=>{!e||e.isDisposed||h.has(e.id)||e.dispose()}))})),null==this.parent&&o.dispose(h),u}async executeFunctionAsync(e,t,n){const r=e.reduce(((e,t,n)=>(e[this.inputs[n].name]=t,e)),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const s=Object.keys(e),a=s.map((e=>this.graph.nodes[m(e)[0]])),o=n.map((e=>m(e)[0])),i=new Set(o);let u=o.map((e=>this.graph.nodes[e]));0===u.length&&(u=this._outputs);const{usedNodes:p,missingInputs:c,dynamicNode:h,syncInputs:d}=Ui(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),y=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=m(t),s=[];s[r]=e[t],y[n]=s}));const g={},b=this.getFrozenTensorIds(y),x={};for(;f.length>0;){const e=this.processStack(a,f,t,y,x,b,i,g,p);await Promise.all(e)}null!=h||r||console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const N=u.filter((e=>!Qi(e)&&!l(e.name,y,t))).map((e=>e.name));if(N.length>0){let e="";throw null!=h&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${N}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${e}`)}return y}processStack(e,n,r,s,a,o,i,u,l){const c=[];for(;n.length>0;){const e=n.pop();r.currentContext=e.contexts;let d="";if("Enter"===e.node.op&&p("isConstant",e.node,s,r)&&([d]=h(e.node.name,r)),null==s[e.node.name]){const p=Ki(e.node,s,r,this._resourceManager);d||([d]=h(e.node.name,r));const m=r.currentContext;t.util.isPromise(p)?c.push(p.then((t=>(s[d]=t,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(t)),r.currentContext=m,this.checkTensorForDisposal(d,e.node,s,r,o,i,u),this.processChildNodes(e.node,n,r,s,a,l),t)))):(s[d]=p,this.keepIntermediateTensors&&(this.clonedTensorsMap[d]=this.cloneTensorList(p)),this.checkTensorForDisposal(d,e.node,s,r,o,i,u),this.processChildNodes(e.node,n,r,s,a,l))}else this.processChildNodes(e.node,n,r,s,a,l)}return c}processChildNodes(e,t,n,r,s,a){e.children.forEach((e=>{const[o]=h(e.name,n);!s[o]&&a.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!l(e,r,n)))&&(s[o]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!l(e,r,n)))&&(s[o]=!0,t.push({contexts:n.currentContext,node:e})))}))}dispose(){Object.keys(this.weightMap).forEach((e=>this.weightMap[e].forEach((e=>e.dispose()))))}checkInputShapeAndType(e){Object.keys(e).forEach((n=>{const r=e[n],[s]=m(n),a=this.graph.nodes[s];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value,n=e.length===r.shape.length&&r.shape.every(((t,n)=>-1===e[n]||e[n]===t));t.util.assert(n,(()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${r.shape}]`))}a.attrParams.dtype&&a.attrParams.dtype.value&&t.util.assert(r.dtype===a.attrParams.dtype.value,(()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${r.dtype}`))}))}mapInputs(e){var t,n;const r={};for(const s in e){const a=null===(n=null===(t=this._signature)||void 0===t?void 0:t.inputs)||void 0===n?void 0:n[s];null!=a?r[a.name]=e[s]:r[s]=e[s]}return r}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=m(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>{var t,n;const r=null===(n=null===(t=this._signature)||void 0===t?void 0:t.outputs)||void 0===n?void 0:n[e];return null!=r?r.name:e}),{})}checkOutputs(e){e.forEach((e=>{const[t]=m(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class eu{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(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}const tu={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8};async function nu(e,t){const n=ne(e.shape);let r;if("quantization"in e){const t=e.quantization;r=tu[t.dtype]}else{if("string"===e.dtype){let e=0;for(let r=0;r<n;r++)e+=4+new Uint32Array(await t(e,e+4))[0];return e}r=tu[e.dtype]}return n*r}function ru(e,t){const n=e.name,r=e.dtype,s=e.shape,a=ne(s);let o,i=0;if("quantization"in e){const s=e.quantization;if("uint8"===s.dtype||"uint16"===s.dtype){if(!("min"in s)||!("scale"in s))throw new Error(`Weight ${e.name} with quantization ${s.dtype} doesn't have corresponding metadata min and scale.`)}else{if("float16"!==s.dtype)throw new Error(`Weight ${e.name} has unknown quantization dtype ${s.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);if("float32"!==r)throw new Error(`Weight ${e.name} is quantized with ${s.dtype} which only supports weights of type float32 not ${r}.`)}const u=tu[s.dtype],p="uint8"===s.dtype?new Uint8Array(t):new Uint16Array(t);if("float32"===r)if("uint8"===s.dtype||"uint16"===s.dtype){o=new Float32Array(p.length);for(let e=0;e<p.length;e++){const t=p[e];o[e]=t*s.scale+s.min}}else{if("float16"!==s.dtype)throw new Error(`Unsupported quantization type ${s.dtype} for weight type float32.`);{const e=function(){const e=function(){const e=e=>{let t=e<<13,n=0;for(;0==(8388608&t);)n-=8388608,t<<=1;return t&=-8388609,n+=947912704,t|n},t=new Uint32Array(2048);t[0]=0;for(let n=1;n<1024;n++)t[n]=e(n);for(let e=1024;e<2048;e++)t[e]=939524096+(e-1024<<13);return t}(),t=function(){const e=new Uint32Array(64);e[0]=0,e[31]=1199570944,e[32]=2147483648,e[63]=3347054592;for(let t=1;t<31;t++)e[t]=t<<23;for(let t=33;t<63;t++)e[t]=2147483648+(t-32<<23);return e}(),n=function(){const e=new Uint32Array(64);for(let t=0;t<64;t++)e[t]=1024;return e[0]=e[32]=0,e}();return r=>{const s=new ArrayBuffer(4*r.length),a=new Uint32Array(s);for(let s=0;s<r.length;s++){const o=r[s],i=e[n[o>>10]+(1023&o)]+t[o>>10];a[s]=i}return new Float32Array(s)}}();o=e(p)}}else{if("int32"!==r)throw new Error(`Unsupported dtype in weight '${n}': ${r}`);if("uint8"!==s.dtype&&"uint16"!==s.dtype)throw new Error(`Unsupported quantization type ${s.dtype} for weight type int32.`);o=new Int32Array(p.length);for(let e=0;e<p.length;e++){const t=p[e];o[e]=Math.round(t*s.scale+s.min)}}i+=a*u}else if("string"===r){const n=ne(e.shape);o=[];for(let e=0;e<n;e++){const e=new Uint32Array(t.slice(i,i+4))[0];i+=4;const n=new Uint8Array(t.slice(i,i+e));o.push(n),i+=e}}else{const e=tu[r];if("float32"===r)o=new Float32Array(t);else if("int32"===r)o=new Int32Array(t);else{if("bool"!==r){if("complex64"===r){o=new Float32Array(t);const e=new Float32Array(o.length/2),n=new Float32Array(o.length/2);for(let t=0;t<e.length;t++)e[t]=o[2*t],n[t]=o[2*t+1];const r=uo(e,s,"float32"),a=uo(n,s,"float32"),i=Jn(r,a);return r.dispose(),a.dispose(),i}throw new Error(`Unsupported dtype in weight '${n}': ${r}`)}o=new Uint8Array(t)}i+=a*e}return uo(o,s,r)}async function su(e,t,n){let r=new Uint8Array(t);for(;r.byteLength<n;){const{done:t,value:s}=await e.read();if(t&&null==s){const e=n-r.byteLength;throw new Error(`Reader is done but ${e} bytes are still expected`)}const a=new Uint8Array(r.length+s.byteLength);a.set(r,0),a.set(new Uint8Array(s),r.length),r=a}return r.buffer}class au{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,n={},r=t.io){this.modelUrl=e,this.loadOptions=n,this.version="n/a",this.io=r,null==n&&(this.loadOptions={}),this.resourceManager=new eu}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{const t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=this.handler.load();return t.util.isPromise(e)?e.then((e=>null==e.getWeightStream?this.loadSync(e):this.loadStreaming(e))):this.loadSync(e)}loadSync(e){const t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}async loadStreaming(e){if(null==e.getWeightStream)throw new Error("Model artifacts missing streamWeights function");const t=await async function(e,t){const n={},r=e.getReader();let s=new ArrayBuffer(0);for(const e of t){const t=await nu(e,(async(e,t)=>(s=await su(r,s,t),s.slice(e,t))));s=await su(r,s,t);const a=s.slice(0,t);s=s.slice(t);const o=ru(e,a);if(n[e.name]=o,"webgpu"===Qt.backendName){const e=Qt.backend;"uploadToGPU"in e&&ne(o.shape)>=ke().get("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD")&&e.uploadToGPU(o.dataId)}}return n}(e.getWeightStream(),e.weightSpecs);return this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;const n=this.artifacts.modelTopology;let r=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){const e=this.artifacts.userDefinedMetadata;null!=e.signature&&(r=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}if(this.signature=r,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Ji(R.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=R.Instance.transformGraph(e.modelInitializer);this.initializer=new Ji(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if("string"==typeof e){const t=this.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(t.length>1)throw new Error(`Found more than one (${t.length}) save handlers for URL '${e}'`);e=t[0]}if(null==e.save)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){const n=e instanceof t.Tensor?[e]:e,r={};return n.forEach(((e,t)=>r[this.structuredOutputKeys[t]]=e)),r}return e}predict(e,t){const n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}async predictAsync(e,t){const n=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(n)}normalizeInputs(e){var n;if(!(e instanceof t.Tensor||Array.isArray(e))){const t=null===(n=this.signature)||void 0===n?void 0:n.inputs;if(null!=t)for(const n in t){const r=t[n];null!=r.resourceId&&(e[n]=this.resourceIdToCapturedInput[r.resourceId])}return e}e=Array.isArray(e)?e:[e];const r=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+r!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-r} non-resource placeholders, while there are ${e.length} input tensors provided.`);let s=0;return this.inputNodes.reduce(((t,n)=>{var r,a,o;const i=null===(o=null===(a=null===(r=this.signature)||void 0===r?void 0:r.inputs)||void 0===a?void 0:a[n])||void 0===o?void 0:o.resourceId;return t[n]=null!=i?this.resourceIdToCapturedInput[i]:e[s++],t}),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}async executeInitializerGraphAsync(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){const t=this.initializerSignature.outputs,n=Object.keys(t);for(let r=0;r<n.length;r++){const s=t[n[r]];this.resourceIdToCapturedInput[s.resourceId]=e[r]}}}execute(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&(this.initializer.dispose(),this.resourceIdToCapturedInput&&t.dispose(this.resourceIdToCapturedInput)),this.resourceManager.dispose()}}e.GraphModel=au,e.deregisterOp=function(e){delete i[e]},e.loadGraphModel=async function(e,n={},r=t.io){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==n&&(n={}),n.fromTFHub&&"string"==typeof e&&(e=function(e){e.endsWith("/")||(e+="/");return`${e}model.json?tfjs-format=file`}(e));const s=new au(e,n,r);return await s.load(),s},e.loadGraphModelSync=function(e){if(null==e)throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");let n;if(e instanceof Array){const[r,s]=e;if(!r)throw new Error("modelJSON must be the first element of the array");if(!(s&&s instanceof ArrayBuffer))throw new Error("An ArrayBuffer of weights must be the second element of the array");if(!("modelTopology"in r))throw new Error("Model JSON is missing 'modelTopology'");if(!("weightsManifest"in r))throw new Error("Model JSON is missing 'weightsManifest'");const a=t.io.getWeightSpecs(r.weightsManifest),o=t.io.getModelArtifactsForJSONSync(r,a,s);n=t.io.fromMemorySync(o)}else if("load"in e)n=e;else{if(!("modelTopology"in e&&"weightSpecs"in e&&"weightData"in e))throw new Error("Unknown model format");n=t.io.fromMemorySync(e)}const r=new au(n);return r.load(),r},e.registerOp=function(e,t){const n={tfOpName:e,category:"custom",inputs:[],attrs:[],customExecutor:t};i[e]=n},e.version_converter="4.15.0"}));
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