/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
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t=this;Dn(e).forEach((function(n){null!=n.disposeFunc&&n.disposeFunc(t.registry[e])}))},e.prototype.initializeBackend=function(e){var t=this,n=this.registryFactory[e];if(null==n)throw new Error("Cannot initialize backend "+e+", no registration found.");try{var r=n.factory();if(!r||r instanceof l||"function"!=typeof r.then)return this.registry[e]=r,{success:!0,asyncInit:!1};var a=++this.pendingBackendInitId,i=r.then((function(n){return!(a<t.pendingBackendInitId)&&(t.registry[e]=n,t.pendingBackendInit=null,!0)})).catch((function(n){return a<t.pendingBackendInitId||(t.pendingBackendInit=null,_n("Initialization of backend "+e+" failed"),_n(n.stack||n.message)),!1}));return this.pendingBackendInit=i,{success:i,asyncInit:!0}}catch(t){return _n("Initialization of backend "+e+" failed"),_n(t.stack||t.message),{success:!1,asyncInit:!1}}},e.prototype.removeBackend=function(e){if(!(e in this.registryFactory))throw new Error(e+" backend not found in 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'"+e+"'")},e.prototype.runKernelFunc=function(e){var t,n,r,a=this,i=[],o=this.isTapeOn(),s=this.state.numBytes,u=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;var l=Vr(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(Vr(e)){var c=e.kernelName,h=e.inputs,f=e.attrs;null==this.backendName&&this.backend;var d=Mn(c,this.backendName);p(null!=d,(function(){return"Cannot find registered kernel '"+c+"' for backend '"+a.backendName+"'"})),n=function(){var e=a.backend.numDataIds();r=d.kernelFunc({inputs:h,attrs:f,backend:a.backend});var t=Array.isArray(r)?r:[r];a.shouldCheckForMemLeaks()&&a.checkKernelForMemLeak(c,e,t);var n=t.map((function(e){return null!=e.rank?e:a.makeTensorFromTensorInfo(e)}));if(o){var s=a.getTensorsForGradient(c,h,n);i=a.saveTensorsForBackwardMode(s)}return n}}else{var v=e.forwardFunc,g=function(e){o&&(i=e.map((function(e){return a.keep(a.clone(e))})))};n=function(){var e=a.backend.numDataIds();r=a.tidy((function(){return v(a.backend,g)}));var t=Array.isArray(r)?r:[r];return a.shouldCheckForMemLeaks()&&a.checkKernelForMemLeak(l,e,t),t}}var m,y=e.inputs,b=e.attrs,w=Vr(e)?null:e.backwardsFunc;return this.scopedRun((function(){return a.state.kernelDepth++}),(function(){return a.state.kernelDepth--}),(function(){a.ENV.getBool("DEBUG")||a.state.profiling?(m=a.profiler.profileKernel(l,y,(function(){return n()})),a.ENV.getBool("DEBUG")&&a.profiler.logKernelProfile(m),t=m.outputs):t=n()})),o&&this.addTapeNode(l,y,t,w,i,b),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-u,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(y).map((function(e){return null!=y[e]?y[e].shape:null})),outputShapes:t.map((function(e){return e.shape})),kernelTimeMs:m.timeMs,extraInfo:m.extraInfo}),Array.isArray(r)?t:t[0]},e.prototype.saveTensorsForBackwardMode=function(e){var t=this,n=e.map((function(e){return t.keep(t.clone(e))}));return n},e.prototype.getTensorsForGradient=function(e,t,n){var r=Tn(e);if(null!=r){var a=r.inputsToSave||[],i=r.outputsToSave||[],o=void 0;r.saveAllInputs?(p(Array.isArray(t),(function(){return"saveAllInputs is true, expected inputs to be an array."})),o=Object.keys(t).map((function(e){return t[e]}))):o=a.map((function(e){return t[e]}));var s=n.filter((function(e,t){return i[t]}));return o.concat(s)}return[]},e.prototype.makeTensor=function(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;var a=e;"string"===n&&D(e[0])&&(a=e.map((function(e){return br(e)})));var i=r.write(a,t,n),o=new Rr(t,n,i,this.nextTensorId());if(this.trackTensor(o,r),"string"===n){var s=this.state.tensorInfo.get(i),u=T(a);this.state.numBytes+=u-s.bytes,s.bytes=u}return o},e.prototype.makeTensorFromDataId=function(e,t,n,r){var a={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(a,r)},e.prototype.makeTensorFromTensorInfo=function(e,t){var n=e.dataId,r=e.shape,a=e.dtype,i=new Rr(r,a,n,this.nextTensorId());return this.trackTensor(i,t),i},e.prototype.makeVariable=function(e,t,n,r){void 0===t&&(t=!0),n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));var a=new Lr(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[a.name])throw new Error("Variable with name "+a.name+" was already registered");return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a},e.prototype.trackTensor=function(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;var n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*M(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 Lr||this.track(e)},e.prototype.incRef=function(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)},e.prototype.removeDataId=function(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)},e.prototype.disposeTensor=function(e){if(this.state.tensorInfo.has(e.dataId)){var 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){var n=e.size*M(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}},e.prototype.disposeVariables=function(){for(var e in this.state.registeredVariables){var t=this.state.registeredVariables[e];this.disposeVariable(t)}},e.prototype.disposeVariable=function(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]},e.prototype.memory=function(){var 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},e.prototype.profile=function(e){return r(this,void 0,void 0,(function(){var t,n,r,o,u,l,c,h,f,d,p;return a(this,(function(a){switch(a.label){case 0:return this.state.profiling=!0,t=this.state.numBytes,n=this.state.numTensors,this.state.activeProfile.kernels=[],r=this.state.activeProfile,[4,e()];case 1:r.result=a.sent(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max.apply(Math,s(this.state.activeProfile.kernels.map((function(e){return e.totalBytesSnapshot})))),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n,a.label=2;case 2:a.trys.push([2,8,9,10]),o=i(this.state.activeProfile.kernels),u=o.next(),a.label=3;case 3:return u.done?[3,7]:(l=u.value,c=l,[4,l.kernelTimeMs]);case 4:return c.kernelTimeMs=a.sent(),h=l,[4,l.extraInfo];case 5:h.extraInfo=a.sent(),a.label=6;case 6:return u=o.next(),[3,3];case 7:return[3,10];case 8:return f=a.sent(),d={error:f},[3,10];case 9:try{u&&!u.done&&(p=o.return)&&p.call(o)}finally{if(d)throw d.error}return[7];case 10:return[2,this.state.activeProfile]}}))}))},e.prototype.isTapeOn=function(){return this.state.gradientDepth>0&&0===this.state.kernelDepth},e.prototype.addTapeNode=function(e,t,n,r,a,i){var o=this,s={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},u=Tn(e);null!=u&&(r=u.gradFunc),null!=r&&(s.gradient=function(e){return e=e.map((function(e,t){if(null==e){var r=n[t],a=q(r.size,r.dtype);return o.makeTensor(a,r.shape,r.dtype)}return e})),r(e.length>1?e:e[0],a,i)}),this.state.activeTape.push(s)},e.prototype.keep=function(e){return e.kept=!0,e},e.prototype.startTape=function(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++},e.prototype.endTape=function(){this.state.gradientDepth--},e.prototype.startScope=function(e){var t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t},e.prototype.endScope=function(e){for(var t=this,n=Gr(e),r=new Set(n.map((function(e){return e.id}))),a=0;a<this.state.activeScope.track.length;a++){var i=this.state.activeScope.track[a];i.kept||r.has(i.id)||i.dispose()}var o=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],n.forEach((function(e){e.kept||e.scopeId!==o.id||t.track(e)}))},e.prototype.gradients=function(e,t,n,r){var a=this;if(void 0===r&&(r=!1),p(t.length>0,(function(){return"gradients() received an empty list of xs."})),null!=n&&"float32"!==n.dtype)throw new Error("dy must have 'float32' dtype, but has '"+n.dtype+"'");var o=this.scopedRun((function(){return a.startTape()}),(function(){return a.endTape()}),(function(){return a.tidy("forward",e)}));p(o instanceof Rr,(function(){return"The result y returned by f() must be a tensor."}));var s=function(e,t,n){for(var r={},a={},i=0;i<t.length;i++)r[t[i].id]=!0;for(i=0;i<e.length;i++){var o=(p=e[i]).inputs;for(var s in o){for(var u=o[s],l=!1,c=0;c<t.length;c++)if(r[u.id]){p.outputs.forEach((function(e){return r[e.id]=!0})),l=!0,a[p.id]=!0;break}if(l)break}}var h={};h[n.id]=!0;var f={};for(i=e.length-1;i>=0;i--)for(o=(p=e[i]).inputs,c=0;c<p.outputs.length;c++)if(h[p.outputs[c].id]){for(var s in o)h[o[s].id]=!0,f[p.id]=!0;break}var d=[];for(i=0;i<e.length;i++){var p;if(a[(p=e[i]).id]&&f[p.id]){var v={};for(var s in p.inputs){var g=p.inputs[s];r[g.id]&&(v[s]=g)}var m=Object.assign({},p);m.inputs=v,m.outputs=p.outputs,d.push(m)}}return d}(this.state.activeTape,t,o);if(!r&&0===s.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",(function(){var e,r,u={};u[o.id]=null==n?(e=o.shape,r=W(y(e),"float32"),Xr.makeTensor(r,e,"float32")):n,function(e,t,n,r){for(var a=function(a){var i=t[a],o=[];if(i.outputs.forEach((function(t){var n=e[t.id];null!=n?o.push(n):o.push(null)})),null==i.gradient)throw new Error("Cannot compute gradient: gradient function not found for "+i.kernelName+".");var s=i.gradient(o),u=function(t){if(!(t in s))throw new Error("Cannot backprop through input "+t+". Available gradients found: "+Object.keys(s)+".");var a=n((function(){return s[t]()}));if("float32"!==a.dtype)throw new Error("Error in gradient for op "+i.kernelName+". The gradient of input "+t+" must have 'float32' dtype, but has '"+a.dtype+"'");var o=i.inputs[t];if(!b(a.shape,o.shape))throw new Error("Error in gradient for op "+i.kernelName+". The gradient of input '"+t+"' has shape '"+a.shape+"', which does not match the shape of the input '"+o.shape+"'");if(null==e[o.id])e[o.id]=a;else{var u=e[o.id];e[o.id]=r(u,a),u.dispose()}};for(var l in i.inputs)u(l)},i=t.length-1;i>=0;i--)a(i)}(u,s,(function(e){return a.tidy(e)}),Qr);var l=t.map((function(e){return u[e.id]}));return 0===a.state.gradientDepth&&(a.state.activeTape.forEach((function(e){var t,n;try{for(var r=i(e.saved),a=r.next();!a.done;a=r.next()){a.value.dispose()}}catch(e){t={error:e}}finally{try{a&&!a.done&&(n=r.return)&&n.call(r)}finally{if(t)throw t.error}}})),a.state.activeTape=null),{value:o,grads:l}}))},e.prototype.customGrad=function(e){var t=this;return p(C(e),(function(){return"The f passed in customGrad(f) must be a function."})),function(){for(var n,r=[],a=0;a<arguments.length;a++)r[a]=arguments[a];p(r.every((function(e){return e instanceof Rr})),(function(){return"The args passed in customGrad(f)(x1, x2,...) must all be tensors"}));var i={};r.forEach((function(e,t){i[t]=e}));var o=function(t,a){return p((n=e.apply(void 0,s(r,[a]))).value instanceof Rr,(function(){return"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"})),p(C(n.gradFunc),(function(){return"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."})),n.value},u=function(e,t){var a=n.gradFunc(e,t),i=Array.isArray(a)?a:[a];p(i.length===r.length,(function(){return"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(...)."})),p(i.every((function(e){return e instanceof Rr})),(function(){return"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."}));var o={};return i.forEach((function(e,t){o[t]=function(){return e}})),o};return t.runKernelFunc({forwardFunc:o,backwardsFunc:u,inputs:i})}},e.prototype.readSync=function(e){return this.state.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.state.tensorInfo.get(e).backend.read(e)},e.prototype.readToGPU=function(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)},e.prototype.time=function(e){return r(this,void 0,void 0,(function(){var t,n;return a(this,(function(r){switch(r.label){case 0:return t=yr(),[4,this.backend.time(e)];case 1:return(n=r.sent()).wallMs=yr()-t,[2,n]}}))}))},e.prototype.track=function(e){return null!=this.state.activeScope&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e},Object.defineProperty(e.prototype,"registeredVariables",{get:function(){return this.state.registeredVariables},enumerable:!0,configurable:!0}),e.prototype.reset=function(){for(var e in this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new 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r=ia(e,"x","batchToSpaceND"),a=t.reduce((function(e,t){return e*t}));p(r.rank>=1+t.length,(function(){return"input rank is "+r.rank+" but should be > than blockShape.length "+t.length})),p(n.length===t.length,(function(){return"crops.length is "+n.length+" but should be equal to blockShape.length  "+t.length})),p(r.shape[0]%a==0,(function(){return"input tensor batch is "+r.shape[0]+" but is not divisible by the product of the elements of blockShape "+t.join(" * ")+" === "+a}));var i={x:r},o={blockShape:t,crops:n};return Xr.runKernel(fe,i,o)}});var Vo=ua({batchNorm_:function(e,t,n,r,a,i){null==i&&(i=.001);var o,s,u=ia(e,"x","batchNorm"),l=ia(t,"mean","batchNorm"),c=ia(n,"variance","batchNorm");null!=a&&(o=ia(a,"scale","batchNorm")),null!=r&&(s=ia(r,"offset","batchNorm")),p(l.rank===c.rank,(function(){return"Batch normalization gradient requires mean and variance to have equal ranks."})),p(null==s||l.rank===s.rank,(function(){return"Batch normalization gradient requires mean and offset 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u=t.accumulatedFirstMoment[i].variable,l=t.accumulatedSecondMoment[i].variable,c=so(co(u,t.beta1),co(s,1-t.beta1)),h=so(co(l,t.beta2),co(Os(s),1-t.beta2)),f=lo(c,r),d=lo(h,a);u.assign(c),l.assign(h);var p=so(co(lo(f,so(Ps(d),t.epsilon)),-t.learningRate),o);o.assign(p)}})),t.accBeta1.assign(co(t.accBeta1,t.beta1)),t.accBeta2.assign(co(t.accBeta2,t.beta2))})),this.incrementIterations()},t.prototype.dispose=function(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&ki(this.accumulatedFirstMoment.map((function(e){return e.variable}))),null!=this.accumulatedSecondMoment&&ki(this.accumulatedSecondMoment.map((function(e){return e.variable})))},t.prototype.getWeights=function(){return r(this,void 0,void 0,(function(){var e;return a(this,(function(t){switch(t.label){case 0:return e=s(this.accumulatedFirstMoment,this.accumulatedSecondMoment),[4,this.saveIterations()];case 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0===a&&(a=null),void 0===i&&(i=0);var o=e.call(this)||this;return o.learningRate=t,o.beta1=n,o.beta2=r,o.epsilon=a,o.decay=i,o.accumulatedFirstMoment=[],o.accumulatedWeightedInfNorm=[],wi((function(){o.iteration=Cs(0).variable(),o.accBeta1=Cs(n).variable()})),null==a&&(o.epsilon=Xr.backend.epsilon()),o}return n(t,e),t.prototype.applyGradients=function(e){var t=this,n=Array.isArray(e)?e.map((function(e){return e.name})):Object.keys(e);wi((function(){var r=fu(1,t.accBeta1),a=lo(-t.learningRate,so(co(t.iteration,t.decay),1));n.forEach((function(n,i){var o=Xr.registeredVariables[n];null==t.accumulatedFirstMoment[i]&&(t.accumulatedFirstMoment[i]={originalName:n+"/m",variable:Es(o).variable(false)}),null==t.accumulatedWeightedInfNorm[i]&&(t.accumulatedWeightedInfNorm[i]={originalName:n+"/v",variable:Es(o).variable(false)});var s=Array.isArray(e)?e[i].tensor:e[n];if(null!=s){var u=t.accumulatedFirstMoment[i].variable,l=t.accumulatedWeightedInfNorm[i].variable,c=so(co(u,t.beta1),co(s,1-t.beta1)),h=co(l,t.beta2),f=ho(s),d=Su(h,f);u.assign(c),l.assign(d);var p=so(co(lo(a,r),lo(c,so(d,t.epsilon))),o);o.assign(p)}})),t.iteration.assign(so(t.iteration,1)),t.accBeta1.assign(co(t.accBeta1,t.beta1))})),this.incrementIterations()},t.prototype.dispose=function(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&ki(this.accumulatedFirstMoment.map((function(e){return e.variable}))),null!=this.accumulatedWeightedInfNorm&&ki(this.accumulatedWeightedInfNorm.map((function(e){return e.variable})))},t.prototype.getWeights=function(){return r(this,void 0,void 0,(function(){return a(this,(function(e){throw new Error("getWeights() is not implemented for Adamax yet.")}))}))},t.prototype.setWeights=function(e){return r(this,void 0,void 0,(function(){return a(this,(function(e){throw new Error("setWeights() is not implemented for Adamax 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e=t.sent(),!1,this.accumulations=e.map((function(e){return{originalName:e.name,variable:e.tensor.variable(false)}})),[2]}}))}))},t.prototype.getConfig=function(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t}(Ah);_h.className="Momentum",to(_h);var Ih=function(e){function t(t,n,r,a,i){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===a&&(a=null),void 0===i&&(i=!1);var o=e.call(this)||this;if(o.learningRate=t,o.decay=n,o.momentum=r,o.epsilon=a,o.accumulatedMeanSquares=[],o.accumulatedMoments=[],o.accumulatedMeanGrads=[],o.centered=i,null==a&&(o.epsilon=Xr.backend.epsilon()),null==t)throw new Error("learningRate for RMSPropOptimizer must be defined.");return o}return n(t,e),t.prototype.applyGradients=function(e){var t=this;(Array.isArray(e)?e.map((function(e){return e.name})):Object.keys(e)).forEach((function(n,r){var a=Xr.registeredVariables[n],i=!1;null==t.accumulatedMeanSquares[r]&&(t.accumulatedMeanSquares[r]={originalName:n+"/rms",variable:wi((function(){return Es(a).variable(i)}))}),null==t.accumulatedMoments[r]&&(t.accumulatedMoments[r]={originalName:n+"/momentum",variable:wi((function(){return Es(a).variable(i)}))}),null==t.accumulatedMeanGrads[r]&&t.centered&&(t.accumulatedMeanGrads[r]={originalName:n+"/mg",variable:wi((function(){return Es(a).variable(i)}))});var o=Array.isArray(e)?e[r].tensor:e[n];if(null!=o){var s=t.accumulatedMeanSquares[r].variable,u=t.accumulatedMoments[r].variable;wi((function(){var e=so(co(s,t.decay),co(Os(o),1-t.decay));if(t.centered){var n=t.accumulatedMeanGrads[r].variable,i=so(co(n,t.decay),co(o,1-t.decay)),l=lo(co(o,t.learningRate),Ps(fu(e,so(Os(i),t.epsilon)))),c=so(co(u,t.momentum),l);s.assign(e),n.assign(i),u.assign(c);var h=fu(a,c);a.assign(h)}else{var f=so(co(s,t.decay),co(Os(o),1-t.decay));c=so(co(u,t.momentum),lo(co(o,t.learningRate),Ps(so(f,t.epsilon))));s.assign(f),u.assign(c);h=fu(a,c);a.assign(h)}}))}})),this.incrementIterations()},t.prototype.dispose=function(){null!=this.accumulatedMeanSquares&&ki(this.accumulatedMeanSquares.map((function(e){return e.variable}))),null!=this.accumulatedMeanGrads&&this.centered&&ki(this.accumulatedMeanGrads.map((function(e){return e.variable}))),null!=this.accumulatedMoments&&ki(this.accumulatedMoments.map((function(e){return e.variable})))},t.prototype.getWeights=function(){return r(this,void 0,void 0,(function(){var e;return a(this,(function(t){switch(t.label){case 0:return e=s(this.accumulatedMeanSquares,this.accumulatedMoments),this.centered&&e.push.apply(e,s(this.accumulatedMeanGrads)),[4,this.saveIterations()];case 1:return[2,[t.sent()].concat(e.map((function(e){return{name:e.originalName,tensor:e.variable}})))]}}))}))},t.prototype.setWeights=function(e){return r(this,void 0,void 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valueAndGrads(f) must be a function"})),function(t,n){p(Array.isArray(t)&&t.every((function(e){return e instanceof Rr})),(function(){return"The args passed in valueAndGrads(f)(args) must be array of tensors"})),p(null==n||n instanceof Rr,(function(){return"The dy passed in valueAndGrads(f)(args, dy) must be a tensor"}));var r=Xr.gradients((function(){return e.apply(void 0,s(t))}),t,n);return null!=n&&v(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),lu(r.grads),r}},e.variable=function(e,t,n,r){return void 0===t&&(t=!0),Xr.makeVariable(e,t,n,r)},e.variableGrads=su,e.version_core="3.19.0",e.where=xs,e.whereAsync=Xl,e.zeros=_u,e.zerosLike=Es,Object.defineProperty(e,"__esModule",{value:!0})}));
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