/*! For license information please see index.js.LICENSE.txt */
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t=s[e];if(-1===t){if(-1!==p)throw new Error(`only one output dimension may be -1, not both ${p} and ${e}`);p=e,l.push(1)}else{if(t<0)throw new Error(`size ${e} must be non-negative, not ${t}`);c*=t,l.push(t)}}if(-1!==p){if(c<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");const e=Math.trunc(o/c);if(c*e!==o)throw new Error(`Input to reshape is a SparseTensor with ${o}\n          dense values, but the requested shape requires a multiple of ${c}. inputShape=${r} outputShape= ${l}`);l[p]=e}const d=a.util.sizeFromShape(l);if(d!==o)throw new Error(`Input to reshape is a tensor with ${o} dense values, but the requested shape has ${d}. inputShape=${r} outputShape=${l}`);const h=r.length,m=[];if(h>0){m[h-1]=1;for(let e=h-2;e>=0;--e)m[e]=m[e+1]*r[e+1]}const f=[];if(u>0){f[u-1]=1;for(let e=u-2;e>=0;--e)f[e]=f[e+1]*l[e+1]}const g=a.util.getArrayFromDType(n,i*u);for(let t=0;t<i;++t){let n=0;for(let 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n=r.indexToLoc(t),a=new Array(e.rank);for(let t=0;t<a.length;t++)a[t]=n[t]%e.shape[t];const s=e.locToIndex(a);r.values[t]=e.values[s]}return r}const ut=(e,t)=>{const n=t.value-e.value;return 0===n?e.index-t.index:n};function lt(e,t,n=0,r=e.length-1){for(;r>n;){if(r-n>600){const a=r-n+1,s=t-n+1,o=Math.log(a),i=.5*Math.exp(2*o/3),u=.5*Math.sqrt(o*i*(a-i)/a)*Math.sign(s-a/2);lt(e,t,Math.max(n,Math.floor(t-s*i/a+u)),Math.min(r,Math.floor(t+(a-s)*i/a+u)))}const s=e[t];let o=n,i=r;for(a.util.swap(e,n,t),ut(e[r],s)>0&&a.util.swap(e,n,r);o<i;){for(a.util.swap(e,o,i),o++,i--;ut(e[o],s)<0;)o+=1;for(;ut(e[i],s)>0;)i-=1}0===ut(e[n],s)?a.util.swap(e,n,i):(i+=1,a.util.swap(e,i,r)),i<=t&&(n=i+1),t<=i&&(r=i-1)}}function ct(e,t,n,r,s){const o=t[t.length-1],[i,u]=[e.length/o,o],l=a.util.getTypedArrayFromDType(n,i*r),c=a.util.getTypedArrayFromDType("int32",i*r);for(let t=0;t<i;t++){const n=t*u,a=e.subarray(n,n+u);let o=new Array(a.length);a.forEach(((e,t)=>o[t]={value:e,index:t})),r<o.length&&(lt(o,r),o=o.slice(0,r)),s&&o.sort(ut);const i=t*r,p=l.subarray(i,i+r),d=c.subarray(i,i+r);for(let e=0;e<r;e++)p[e]=o[e].value,d[e]=o[e].index}const p=t.slice();return p[p.length-1]=r,[(0,a.buffer)(p,n,l),(0,a.buffer)(p,"int32",c)]}function pt(e,t,n,r){const s=a.util.parseAxisParam(t,n)[0],o=[1,n[0],1];for(let e=0;e<s;e++)o[0]*=n[e];o[1]=n[s];for(let e=s+1;e<n.length;e++)o[2]*=n[e];const i={},u=new Int32Array(n[s]),l=new a.TensorBuffer(o,r,e),c=[],p=1===o[0]&&1===o[2];for(let t=0;t<n[s];t++){let n;if(p)n=e[t].toString();else{const e=[];for(let n=0;n<o[0];n++)for(let r=0;r<o[2];r++)e.push(l.get(n,t,r));n=e.join(",")}if(void 0!==i[n])u[t]=i[n];else{const e=Object.keys(i).length;i[n]=e,u[t]=e,c.push(t)}}const d=o.slice();d[1]=Object.keys(i).length;const h=new a.TensorBuffer(d,r);c.forEach(((e,t)=>{for(let n=0;n<o[0];n++)for(let r=0;r<o[2];r++)h.set(l.get(n,e,r),n,t,r)}));const m=n.slice();return m[s]=d[1],{outputValues:h.values,outputShape:m,indices:u}}const dt="3.9.0";(0,a.registerBackend)("cpu",(()=>new i),1);const ht=A(a.Elu,(e=>e>=0?e:Math.exp(e)-1)),mt={kernelName:a.Elu,backendName:"cpu",kernelFunc:ht};function ft(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{alpha:i}=r;s([o],"leakyRelu");const u=a.util.sizeFromShape(o.shape),l=n.data.get(o.dataId).values,c=a.util.getTypedArrayFromDType("float32",u);for(let e=0;e<l.length;e++)c[e]=l[e]<0?i*l[e]:l[e];return n.makeTensorInfo(o.shape,"float32",c)}const gt={kernelName:a.LeakyRelu,backendName:"cpu",kernelFunc:ft},yt=c(((e,t)=>e<0?t*e:e));function bt(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t;s([r,a],"prelu");const o=n.data.get(r.dataId).values,i=n.data.get(a.dataId).values,[u,l]=yt(r.shape,a.shape,o,i,r.dtype);return n.makeTensorInfo(l,r.dtype,u)}const kt={kernelName:a.Prelu,backendName:"cpu",kernelFunc:bt},wt=A(a.Relu,(e=>Math.max(0,e))),vt={kernelName:a.Relu,backendName:"cpu",kernelFunc:wt},xt=A(a.Relu6,(e=>Math.min(Math.max(0,e),6))),Nt={kernelName:a.Relu6,backendName:"cpu",kernelFunc:xt};function St(e,t,n,r,a){if("linear"===n)return m({inputs:{x:t},backend:e});if("relu"===n)return wt({inputs:{x:t},backend:e});if("elu"===n)return ht({inputs:{x:t},backend:e});if("relu6"===n)return xt({inputs:{x:t},backend:e});if("prelu"===n)return bt({inputs:{x:t,alpha:r},backend:e});if("leakyrelu"===n)return ft({inputs:{x:t},backend:e,attrs:{alpha:a}});if("sigmoid"===n)return Ve({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function It(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{shape:o}=r,i=a.util.sizeFromShape(s.shape),u=a.util.inferFromImplicitShape(o,i),l=a.util.sizeFromShape(u);a.util.assert(i===l,(()=>`The new shape (${u}) has ${l} elements and the old shape (${s.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`)),n.incRef(s.dataId);const c=n.data.get(s.dataId);if(null!=c.complexTensorInfos){const e=c.complexTensorInfos.real,t=c.complexTensorInfos.imag;e.shape=u,t.shape=u}return{dataId:s.dataId,shape:u,dtype:s.dtype}}const Tt={kernelName:a.Reshape,backendName:"cpu",kernelFunc:It};function _t(e){const{inputs:t,backend:n,attrs:r}=e,{a:o,b:i}=t,{transposeA:u,transposeB:l}=r;s([o,i],"matMul");const c=o.shape.length,p=i.shape.length,d=u?o.shape[c-2]:o.shape[c-1],h=l?i.shape[p-1]:i.shape[p-2],m=u?o.shape[c-1]:o.shape[c-2],f=l?i.shape[p-2]:i.shape[p-1],g=o.shape.slice(0,-2),y=i.shape.slice(0,-2),b=a.util.sizeFromShape(g),k=a.util.sizeFromShape(y),w=b===k||1===b||1===k;a.util.assert(c>=2&&p>=2&&w,(()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${g}) and (${y}).`));const v=(b>k?o.shape.slice(0,-2):i.shape.slice(0,-2)).concat([m,f]);a.util.assert(d===h,(()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${o.shape} and ${i.shape} and transposeA=${u} and transposeB=${l} must match.`));const x=l?[k,f,h]:[k,h,f],N=It({inputs:{x:o},backend:n,attrs:{shape:u?[b,d,m]:[b,m,d]}}),S=It({inputs:{x:i},backend:n,attrs:{shape:x}}),I=u?N.shape[1]:N.shape[2],T=u?N.shape[2]:N.shape[1],_=l?S.shape[1]:S.shape[2],E=Math.max(b,k),A=n.data.get(N.dataId).values,M=n.data.get(S.dataId).values,$=a.util.computeStrides(N.shape),D=a.util.computeStrides(S.shape),[F,O,B]=u?[$[0],1,$[1]]:[$[0],$[1],1],[C,V,R]=l?[1,D[1],D[0]]:[D[1],1,D[0]],z=T*_,P=(0,a.buffer)([E,T,_],N.dtype),L=P.values,W=n.blockSize;for(let e=0;e<E;e++)for(let t=0;t<T;t+=W)for(let n=0;n<_;n+=W)for(let r=0;r<I;r+=W){const a=Math.min(t+W,T),s=Math.min(n+W,_),o=Math.min(r+W,I);for(let i=t;i<a;i++)for(let t=n;t<s;t++){let n=0;for(let a=r;a<o;a++){const r=Math.min(e,b-1)*F,s=Math.min(e,k-1)*R;n+=A[r+i*O+a*B]*M[a*C+t*V+s]}L[e*z+(i*_+t)]+=n}}return n.disposeIntermediateTensorInfo(N),n.disposeIntermediateTensorInfo(S),n.makeTensorInfo(v,P.dtype,P.values)}const Et={kernelName:a.BatchMatMul,backendName:"cpu",kernelFunc:_t};const At={kernelName:a._FusedMatMul,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a,b:s,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=r;let d,h,m;const f=[];d=_t({inputs:{a,b:s},attrs:{transposeA:u,transposeB:l},backend:n}),o&&(h=S({inputs:{a:d,b:o},backend:n}),f.push(d),d=h),c&&(m=St(n,d,c,i,p),f.push(d),d=m);for(const e of f)n.disposeIntermediateTensorInfo(e);return d}},Mt=A(a.Acos,(e=>Math.acos(e))),$t={kernelName:a.Acos,backendName:"cpu",kernelFunc:Mt},Dt=A(a.Acosh,(e=>Math.acosh(e))),Ft={kernelName:a.Acosh,backendName:"cpu",kernelFunc:Dt};const Ot={kernelName:a.AddN,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,r=t;s(t,"addN");const o=r.map((e=>n.data.get(e.dataId).values)),i=(0,a.buffer)(r[0].shape,r[0].dtype),u=i.values;for(let e=0;e<r.length;e++){const t=o[e];for(let e=0;e<u.length;e++)u[e]+=t[e]}return n.makeTensorInfo(i.shape,i.dtype,i.values)}};const Bt={kernelName:a.All,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{axis:i,keepDims:u}=r;s(o,"all");const l=a.util.parseAxisParam(i,o.shape);let c=l;const p=a.backend_util.getAxesPermutation(c,o.shape.length);let d=o;null!=p&&(d=Ee({inputs:{x:o},backend:n,attrs:{perm:p}}),c=a.backend_util.getInnerMostAxes(c.length,o.shape.length)),a.backend_util.assertAxesAreInnerMostDims("all",c,d.shape.length);const[h,m]=a.backend_util.computeOutAndReduceShapes(d.shape,c),f=a.util.sizeFromShape(m),g=a.util.makeZerosTypedArray(a.util.sizeFromShape(h),d.dtype),y=n.data.get(d.dataId).values;for(let e=0;e<g.length;++e){const t=e*f;let n=y[t];for(let e=0;e<f;++e){const r=y[t+e];n=n&&r}g[e]=n}null!=p&&n.disposeIntermediateTensorInfo(d);const b=n.makeTensorInfo(h,d.dtype,g);if(u){const e=It({inputs:{x:b},backend:n,attrs:{shape:a.backend_util.expandShapeToKeepDim(h,l)}});return n.disposeIntermediateTensorInfo(b),e}return b}};const Ct={kernelName:a.Any,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{axis:i,keepDims:u}=r;s(o,"any");const l=a.util.parseAxisParam(i,o.shape);let c=l;const p=a.backend_util.getAxesPermutation(c,o.shape.length);let d=o;null!=p&&(d=Ee({inputs:{x:o},backend:n,attrs:{perm:p}}),c=a.backend_util.getInnerMostAxes(c.length,o.shape.length)),a.backend_util.assertAxesAreInnerMostDims("any",c,d.shape.length);const[h,m]=a.backend_util.computeOutAndReduceShapes(d.shape,c),f=a.util.sizeFromShape(m),g=a.util.makeZerosTypedArray(a.util.sizeFromShape(h),d.dtype),y=n.data.get(d.dataId).values;for(let e=0;e<g.length;++e){const t=e*f;let n=y[t];for(let e=0;e<f;++e){const r=y[t+e];n=n||r}g[e]=n}null!=p&&n.disposeIntermediateTensorInfo(d);const b=n.makeTensorInfo(h,d.dtype,g);if(u){const e=It({inputs:{x:b},backend:n,attrs:{shape:a.backend_util.expandShapeToKeepDim(h,l)}});return n.disposeIntermediateTensorInfo(b),e}return b}};const Vt={kernelName:a.ArgMax,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{axis:i}=r;s(o,"argMax");let u=a.util.parseAxisParam(i,o.shape);const l=a.backend_util.getAxesPermutation(u,o.shape.length);let c=o;const p=[];null!=l&&(c=Ee({inputs:{x:o},backend:n,attrs:{perm:l}}),p.push(c),u=a.backend_util.getInnerMostAxes(u.length,c.shape.length)),u=[u[0]],a.backend_util.assertAxesAreInnerMostDims("argMax",u,c.shape.length);const[d,h]=a.backend_util.computeOutAndReduceShapes(c.shape,u),m=a.util.sizeFromShape(d),f=a.util.makeZerosTypedArray(m,"int32"),g=a.util.sizeFromShape(h),y=n.data.get(c.dataId).values;for(let e=0;e<f.length;++e){const t=e*g;let n=y[t],r=0;for(let e=0;e<g;++e){const a=y[t+e];a>n&&(n=a,r=e)}f[e]=r}return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(d,"int32",f)}};const Rt={kernelName:a.ArgMin,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{axis:i}=r;s(o,"argMin");let u=a.util.parseAxisParam(i,o.shape);const l=a.backend_util.getAxesPermutation(u,o.shape.length);let c=o;const p=[];null!=l&&(c=Ee({inputs:{x:o},backend:n,attrs:{perm:l}}),p.push(c),u=a.backend_util.getInnerMostAxes(u.length,c.shape.length)),u=[u[0]],a.backend_util.assertAxesAreInnerMostDims("argMin",u,c.shape.length);const[d,h]=a.backend_util.computeOutAndReduceShapes(c.shape,u),m=a.util.sizeFromShape(d),f=a.util.makeZerosTypedArray(m,"int32"),g=a.util.sizeFromShape(h),y=n.data.get(c.dataId).values;for(let e=0;e<f.length;++e){const t=e*g;let n=y[t],r=0;for(let e=0;e<g;++e){const a=y[t+e];a<n&&(n=a,r=e)}f[e]=r}return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.makeTensorInfo(d,"int32",f)}},zt=A(a.Asin,(e=>Math.asin(e))),Pt={kernelName:a.Asin,backendName:"cpu",kernelFunc:zt},Lt=A(a.Asinh,(e=>Math.asinh(e))),Wt={kernelName:a.Asinh,backendName:"cpu",kernelFunc:Lt},Ht=A(a.Atan,(e=>Math.atan(e))),qt={kernelName:a.Atan,backendName:"cpu",kernelFunc:Ht},Gt=c(((e,t)=>Math.atan2(e,t))),Ut=w(a.Atan2,Gt),jt={kernelName:a.Atan2,backendName:"cpu",kernelFunc:Ut},Kt=A(a.Atanh,(e=>Math.atanh(e))),Xt={kernelName:a.Atanh,backendName:"cpu",kernelFunc:Kt};function Zt(e,t,n,r,s,o){const i=s.strideHeight,u=s.strideWidth,l=s.dilationHeight,c=s.dilationWidth,p=s.effectiveFilterHeight,d=s.effectiveFilterWidth,h=s.padInfo.top,m=s.padInfo.left,f="max"===o?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,g=(0,a.buffer)(s.outShape,n),y=g.values,b=s.outShape[1]*s.outShape[2]*s.outShape[3],k=s.outShape[2]*s.outShape[3],w=s.outShape[3];for(let t=0;t<s.batchSize;++t){const n=t*b,a=t*r[0];for(let t=0;t<s.inChannels;++t)for(let g=0;g<s.outHeight;++g){const b=g*i-h,v=Math.max(0,b),x=Math.min(s.inHeight,p+b),N=n+g*k;for(let n=0;n<s.outWidth;++n){const i=n*u-m,p=Math.max(0,i),h=Math.min(s.inWidth,d+i);let g=f,b=0,k=0;for(let n=v;n<x;n+=l){const s=a+n*r[1];for(let n=p;n<h;n+=c){const a=e[s+n*r[2]+t];"max"===o&&a>g?g=a:"avg"===o&&(b+=a,k++)}if(isNaN(g))break}y[N+n*w+t]="avg"===o?b/k:g}}}return g}function Jt(e,t,n,r,s=!1,o=!1){const i=(0,a.buffer)(r.outShape,"int32"),u=r.strideHeight,l=r.strideWidth,c=r.dilationHeight,p=r.dilationWidth,d=r.effectiveFilterHeight,h=r.effectiveFilterWidth,m=r.padInfo.top,f=r.padInfo.left,g=(0,a.buffer)(t,n,e);for(let e=0;e<r.batchSize;++e)for(let t=0;t<r.inChannels;++t)for(let n=0;n<r.outHeight;++n){const a=n*u-m;let y=a;for(;y<0;)y+=c;const b=Math.min(r.inHeight,d+a);for(let u=0;u<r.outWidth;++u){const d=u*l-f;let m=d;for(;m<0;)m+=p;const k=Math.min(r.inWidth,h+d);let w=Number.NEGATIVE_INFINITY,v=-1;for(let n=y;n<b;n+=c){const i=n-a;for(let a=m;a<k;a+=p){const u=a-d,l=g.get(e,n,a,t);l>w&&(w=l,v=s?o?((e*r.inHeight+n)*r.inWidth+a)*r.inChannels+t:(n*r.inWidth+a)*r.inChannels+t:i*h+u)}}i.set(v,e,n,u,t)}}return i}function Qt(e,t,n,r,s,o){const i=s.strideDepth,u=s.strideHeight,l=s.strideWidth,c=s.dilationDepth,p=s.dilationHeight,d=s.dilationWidth,h=s.effectiveFilterDepth,m=s.effectiveFilterHeight,f=s.effectiveFilterWidth,g=s.padInfo.front,y=s.padInfo.top,b=s.padInfo.left,k="max"===o?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,w=(0,a.buffer)(s.outShape,n),v=w.values,x=s.outShape[1]*s.outShape[2]*s.outShape[3]*s.outShape[4],N=s.outShape[2]*s.outShape[3]*s.outShape[4],S=s.outShape[3]*s.outShape[4],I=s.outShape[4];for(let t=0;t<s.batchSize;++t){const n=t*x,a=t*r[0];for(let t=0;t<s.inChannels;++t)for(let w=0;w<s.outDepth;++w){const x=w*i-g;let T=x;for(;T<0;)T+=c;const _=Math.min(s.inDepth,h+x),E=n+w*N;for(let n=0;n<s.outHeight;++n){const i=n*u-y;let h=i;for(;h<0;)h+=p;const g=Math.min(s.inHeight,m+i),w=E+n*S;for(let n=0;n<s.outWidth;++n){const i=n*l-b;let u=i;for(;u<0;)u+=d;const m=Math.min(s.inWidth,f+i),y=w+n*I;let x=k,N=0,S=0;for(let n=T;n<_;n+=c){const s=a+n*r[1];for(let n=h;n<g;n+=p){const a=s+n*r[2];for(let n=u;n<m;n+=d){const s=e[a+n*r[3]+t];if("max"===o&&s>x?x=s:"avg"===o&&(N+=s,S++),isNaN(x))break}if(isNaN(x))break}if(isNaN(x))break}v[y+t]="avg"===o?N/S:x}}}}return w}const Yt={kernelName:a.AvgPool,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t;s(o,"avgPool");const{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=r;a.util.assert(a.backend_util.eitherStridesOrDilationsAreOne(u,1),(()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${u} and dilations '1'`));const p=a.backend_util.computePool2DInfo(o.shape,i,u,1,l,c);let d;if(1===p.filterWidth&&1===p.filterHeight&&a.util.arraysEqual(p.inShape,p.outShape))d=m({inputs:{x:o},backend:n});else{const e=n.data.get(o.dataId).values,t=a.util.computeStrides(o.shape),r=Zt(e,o.shape,o.dtype,t,p,"avg");d=n.makeTensorInfo(p.outShape,o.dtype,r.values)}return d}};const en={kernelName:a.AvgPool3D,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{filterSize:i,strides:u,pad:l,dimRoundingMode:c,dataFormat:p}=r;s(o,"avgPool3d");const d=a.backend_util.computePool3DInfo(o.shape,i,u,1,l,c,p),h=Qt(n.data.get(o.dataId).values,o.shape,o.dtype,a.util.computeStrides(o.shape),d,"avg");return n.makeTensorInfo(h.shape,"float32",h.values)}};const tn={kernelName:a.AvgPool3DGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:o,input:i}=t,{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=r;s([o,i],"avgPool3DGrad");const d=a.backend_util.computePool3DInfo(i.shape,u,l,1,c,p),h=d.strideDepth,m=d.strideHeight,f=d.strideWidth,g=d.filterDepth,y=d.filterHeight,b=d.filterWidth,k=d.dilationDepth,w=d.dilationHeight,v=d.dilationWidth,x=d.effectiveFilterDepth,N=d.effectiveFilterHeight,S=d.effectiveFilterWidth,I=x-1-d.padInfo.front,T=S-1-d.padInfo.left,_=N-1-d.padInfo.top,E=(0,a.buffer)(i.shape,"float32"),A=1/(g*y*b),M=n.bufferSync(o);for(let e=0;e<d.batchSize;++e)for(let t=0;t<d.inChannels;++t)for(let n=0;n<d.inDepth;++n)for(let r=0;r<d.inHeight;++r)for(let a=0;a<d.inWidth;++a){const s=n-I,o=r-_,i=a-T;let u=0;for(let n=0;n<x;n+=k){const r=(s+n)/h;if(!(r<0||r>=d.outDepth||Math.floor(r)!==r))for(let n=0;n<N;n+=w){const a=(o+n)/m;if(!(a<0||a>=d.outHeight||Math.floor(a)!==a))for(let n=0;n<S;n+=v){const s=(i+n)/f;if(s<0||s>=d.outWidth||Math.floor(s)!==s)continue;u+=M.get(e,r,a,s,t)}}}E.set(u*A,e,n,r,a,t)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}};const nn={kernelName:a.AvgPoolGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:o,input:i}=t,u=i;s([o,i],"avgPoolGrad");const{filterSize:l,strides:c,pad:p}=r,d=a.backend_util.computePool2DInfo(u.shape,l,c,1,p),h=d.strideHeight,m=d.strideWidth,f=d.filterHeight,g=d.filterWidth,y=d.dilationHeight,b=d.dilationWidth,k=d.effectiveFilterHeight,w=d.effectiveFilterWidth,v=w-1-d.padInfo.left,x=k-1-d.padInfo.top,N=(0,a.buffer)(u.shape,"float32"),S=1/(f*g),I=n.data.get(o.dataId).values,T=(0,a.buffer)(o.shape,"float32",I);for(let e=0;e<d.batchSize;++e)for(let t=0;t<d.inChannels;++t)for(let n=0;n<d.inHeight;++n)for(let r=0;r<d.inWidth;++r){const a=n-x,s=r-v;let o=0;for(let n=0;n<k;n+=y){const r=(a+n)/h;if(!(r<0||r>=d.outHeight||Math.floor(r)!==r))for(let n=0;n<w;n+=b){const a=(s+n)/m;if(a<0||a>=d.outWidth||Math.floor(a)!==a)continue;o+=T.get(e,r,a,t)}}N.set(o*S,e,n,r,t)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}};const rn={kernelName:a.FusedBatchNorm,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,scale:i,offset:u,mean:l,variance:c}=t;a.util.assert(l.shape.length===c.shape.length,(()=>"Batch normalization gradient requires mean and variance to have equal ranks.")),a.util.assert(null==u||l.shape.length===u.shape.length,(()=>"Batch normalization gradient requires mean and offset to have equal ranks.")),a.util.assert(null==i||l.shape.length===i.shape.length,(()=>"Batch normalization gradient requires mean and scale to have equal ranks.")),s([o,l,c,i,u],"batchNorm");let{varianceEpsilon:p}=r;null==p&&(p=.001);const d=n.data.get(o.dataId).values,h=n.data.get(l.dataId).values,m=n.data.get(c.dataId).values,f=i?n.data.get(i.dataId).values:new Float32Array([1]),g=u?n.data.get(u.dataId).values:new Float32Array([0]),y=new Float32Array(d.length),b=g.length,k=f.length,w=m.length,v=h.length;let x=0,N=0,S=0,I=0;for(let e=0;e<d.length;++e)y[e]=g[x++]+(d[e]-h[N++])*f[S++]/Math.sqrt(m[I++]+p),x>=b&&(x=0),N>=v&&(N=0),S>=k&&(S=0),I>=w&&(I=0);return n.makeTensorInfo(o.shape,o.dtype,y)}};const an={kernelName:a.BatchToSpaceND,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{blockShape:i,crops:u}=r;s([o],"batchToSpaceND");const l=i.reduce(((e,t)=>e*t)),c=a.backend_util.getReshaped(o.shape,i,l),p=a.backend_util.getPermuted(c.length,i.length),d=a.backend_util.getReshapedPermuted(o.shape,i,l),h=a.backend_util.getSliceBeginCoords(u,i.length),m=a.backend_util.getSliceSize(d,u,i.length),f=It({inputs:{x:o},backend:n,attrs:{shape:c}}),g=Ee({inputs:{x:f},backend:n,attrs:{perm:p}}),y=It({inputs:{x:g},backend:n,attrs:{shape:d}}),b=Pe({inputs:{x:y},backend:n,attrs:{begin:h,size:m}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(y),b}};const sn={kernelName:a.Bincount,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:o}=r,i=T(n.data.get(a.dataId).values,n.data.get(s.dataId).values,s.dtype,s.shape,o);return n.makeTensorInfo([o],s.dtype,i)}};const on={kernelName:a.BroadcastArgs,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:s}=t,o=n.data.get(r.dataId).values,i=n.data.get(s.dataId).values,u=a.backend_util.assertAndGetBroadcastShape(Array.from(o),Array.from(i));return n.makeTensorInfo([u.length],"int32",Int32Array.from(u))}},un=A(a.ClipByValue,((e,t)=>{const n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e})),ln={kernelName:a.ClipByValue,backendName:"cpu",kernelFunc:un},cn={kernelName:a.ComplexAbs,backendName:"cpu",kernelFunc:e=>{const{x:t}=e.inputs,n=e.backend,r=new Float32Array(a.util.sizeFromShape(t.shape)),s=n.data.get(t.dataId),o=s.complexTensorInfos.real,i=s.complexTensorInfos.imag,u=n.data.get(o.dataId).values,l=n.data.get(i.dataId).values;for(let e=0;e<u.length;e++){const t=u[e],n=l[e];r[e]=Math.hypot(t,n)}return n.makeOutput(r,t.shape,"float32")}};function pn(e){const{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}const dn={kernelName:a.Imag,backendName:"cpu",kernelFunc:pn};function hn(e){const{inputs:t,backend:n,attrs:r}=e,{axis:s}=r,o=a.util.parseAxisParam(s,t[0].shape)[0];let i=a.backend_util.computeOutShape(t.map((e=>e.shape)),o);if(0===a.util.sizeFromShape(i))return n.makeTensorInfo(i,t[0].dtype,[]);const u=t.filter((e=>a.util.sizeFromShape(e.shape)>0));if(1===u.length)return m({inputs:{x:u[0]},backend:n});const l=u.map((e=>e.shape));if(a.backend_util.assertParamsConsistent(l,o),"complex64"===u[0].dtype){const e=u.map((e=>g({inputs:{input:e},backend:n}))),t=u.map((e=>pn({inputs:{input:e},backend:n}))),r=hn({inputs:e,backend:n,attrs:{axis:o}}),a=hn({inputs:t,backend:n,attrs:{axis:o}}),s=p({inputs:{real:r,imag:a},backend:n});return e.forEach((e=>n.disposeIntermediateTensorInfo(e))),t.forEach((e=>n.disposeIntermediateTensorInfo(e))),n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),s}const c=u.map((e=>{const t=a.util.sizeFromShape(e.shape.slice(o));return It({inputs:{x:e},backend:n,attrs:{shape:[-1,t]}})})),d=c.map((e=>({vals:n.data.get(e.dataId).values,shape:e.shape})));i=a.backend_util.computeOutShape(c.map((e=>e.shape)),1);const h=1===c[0].shape[0],f=O(d,i,t[0].dtype,h),y=a.backend_util.computeOutShape(u.map((e=>e.shape)),o),b=n.makeTensorInfo(y,t[0].dtype,f);return c.forEach((e=>n.disposeIntermediateTensorInfo(e))),b}const mn={kernelName:a.Concat,backendName:"cpu",kernelFunc:hn};function fn(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,filter:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d}=r;s([o,i],"conv2d");const h=a.backend_util.convertConv2DDataFormat(c),m=a.backend_util.computeConv2DInfo(o.shape,i.shape,u,p,l,d,!1,h),f=m.filterHeight,g=m.filterWidth,y=m.dilationHeight,b=m.dilationWidth,k=m.padInfo.left,w=m.padInfo.top,v="channelsLast"===m.dataFormat,x=new a.TensorBuffer(m.outShape,o.dtype),N=a.util.computeStrides(o.shape),S=a.util.computeStrides(i.shape),I=N[0],T=v?N[1]:N[2],_=v?N[2]:1,E=v?1:N[1],A=x.strides[0],M=v?x.strides[1]:x.strides[2],$=v?x.strides[2]:1,D=v?1:x.strides[1],F=n.data.get(o.dataId).values,O=n.data.get(i.dataId).values,B=x.values;for(let e=0;e<m.batchSize;++e){const t=e*I,n=e*A;for(let e=0;e<m.outHeight;++e){const r=n+e*M,a=e*m.strideHeight-w;for(let e=0;e<f;++e){const n=a+e*y;if(n<0||n>=m.inHeight)continue;const s=e*S[0],o=t+n*T;for(let e=0;e<m.outWidth;++e){const t=r+e*$,n=e*m.strideWidth-k;for(let e=0;e<g;++e){const r=n+e*b;if(r<0||r>=m.inWidth)continue;const a=o+r*_;let i=s+e*S[1];for(let e=0;e<m.inChannels;++e){const n=F[a+e*E];for(let e=0;e<m.outChannels;++e)B[t+e*D]+=n*O[i+e];i+=m.outChannels}}}}}}return n.makeTensorInfo(x.shape,x.dtype,B)}const gn={kernelName:a.Conv2D,backendName:"cpu",kernelFunc:fn};const yn={kernelName:a.Conv2DBackpropFilter,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,dy:i}=t,{strides:u,pad:l,dataFormat:c,dimRoundingMode:p,filterShape:d}=r;s([o,i],"conv2dBackpropFilter");const h=a.backend_util.convertConv2DDataFormat(c),m=a.backend_util.computeConv2DInfo(o.shape,d,u,1,l,p,!1,h),{strideHeight:f,strideWidth:g,filterHeight:y,filterWidth:b}=m,k="channelsLast"===m.dataFormat,w=new a.TensorBuffer(m.filterShape,"float32"),v=m.padInfo.left,x=m.padInfo.top,N=n.data.get(o.dataId).values,S=n.data.get(i.dataId).values,I=new a.TensorBuffer(o.shape,o.dtype,N),T=new a.TensorBuffer(i.shape,i.dtype,S);for(let e=0;e<y;++e){const t=Math.max(0,Math.ceil((x-e)/f)),n=Math.min(m.outHeight,(m.inHeight+x-e)/f);for(let r=0;r<b;++r){const a=Math.max(0,Math.ceil((v-r)/g)),s=Math.min(m.outWidth,(m.inWidth+v-r)/g);for(let o=0;o<m.inChannels;++o)for(let i=0;i<m.outChannels;++i){let u=0;for(let l=0;l<m.batchSize;++l)for(let c=t;c<n;++c){const t=e+c*f-x;for(let e=a;e<s;++e){const n=r+e*g-v;u+=k?I.get(l,t,n,o)*T.get(l,c,e,i):I.get(l,o,t,n)*T.get(l,i,c,e)}}w.set(u,e,r,o,i)}}}return n.makeTensorInfo(w.shape,w.dtype,w.values)}};const bn={kernelName:a.Conv2DBackpropInput,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:o,filter:i}=t,{inputShape:u,strides:l,pad:c,dataFormat:p,dimRoundingMode:d}=r;s([o,i],"conv2dBackpropInput");const h=a.util.computeStrides(i.shape),m=a.util.computeStrides(o.shape);let f=a.backend_util.convertConv2DDataFormat(p);const g=a.backend_util.computeConv2DInfo(u,i.shape,l,1,c,d,!1,f),y=new a.TensorBuffer(g.inShape,"float32"),b=y.values,k=n.data.get(o.dataId).values,w=n.data.get(i.dataId).values,[v,x,N]=h,{batchSize:S,filterHeight:I,filterWidth:T,inChannels:_,inHeight:E,inWidth:A,outChannels:M,outHeight:$,outWidth:D,strideHeight:F,strideWidth:O}=g;f=g.dataFormat;const B=I-1-g.padInfo.top,C=T-1-g.padInfo.left,V="channelsLast"===f,R=y.strides[0],z=V?y.strides[1]:y.strides[2],P=V?y.strides[2]:1,L=V?1:y.strides[1],W=m[0],H=V?m[1]:m[2],q=V?m[2]:1,G=V?1:m[1];for(let e=0;e<S;++e)for(let t=0;t<_;++t)for(let n=0;n<E;++n){const r=n-B,a=Math.max(0,Math.ceil(r/F)),s=Math.min($,(I+r)/F);for(let o=0;o<A;++o){const i=o-C,u=Math.max(0,Math.ceil(i/O)),l=Math.min(D,(T+i)/O);let c=0;for(let n=a;n<s;++n){const a=n*F-r;for(let r=u;r<l;++r){const s=W*e+H*n+q*r,o=v*(I-1-a)+x*(T-1-(r*O-i))+N*t;for(let e=0;e<M;++e){c+=k[s+G*e]*w[o+e]}}}b[R*e+z*n+P*o+L*t]=c}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}};const kn={kernelName:a.Conv3D,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,filter:i}=t,{strides:u,pad:l,dilations:c}=r;s([o,i],"conv3d");const p=a.backend_util.computeConv3DInfo(o.shape,i.shape,u,c,l),{filterDepth:d,filterHeight:h,filterWidth:m,dilationDepth:f,dilationHeight:g,dilationWidth:y,padInfo:b}=p,k=b.front,w=b.left,v=b.top,x=new a.TensorBuffer(p.outShape,o.dtype),N=n.data.get(o.dataId).values,S=n.data.get(i.dataId).values,I=x.values,T=a.util.computeStrides(o.shape),_=a.util.computeStrides(i.shape);for(let e=0;e<p.batchSize;++e){const t=e*T[0],n=e*x.strides[0];for(let e=0;e<p.outDepth;++e){const r=n+e*x.strides[1],a=e*p.strideDepth-k;for(let e=0;e<d;++e){const n=a+e*f;if(n<0||n>=p.inDepth)continue;const s=e*_[0],o=t+n*T[1];for(let e=0;e<p.outHeight;++e){const t=r+e*x.strides[2],n=e*p.strideHeight-v;for(let e=0;e<h;++e){const r=n+e*g;if(r<0||r>=p.inHeight)continue;const a=s+e*_[1],i=o+r*T[2];for(let e=0;e<p.outWidth;++e){const n=t+e*p.outChannels,r=e*p.strideWidth-w;for(let e=0;e<m;++e){const t=r+e*y;if(t<0||t>=p.inWidth)continue;const s=a+e*_[2],o=i+t*p.inChannels;let u=s;for(let e=0;e<p.inChannels;++e){const t=N[o+e];for(let e=0;e<p.outChannels;++e)I[n+e]+=t*S[u+e];u+=p.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}};const wn={kernelName:a.Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,dy:i}=t,{strides:u,pad:l,filterShape:c}=r;s([o,i],"conv3dBackpropFilterV2");const p=a.util.computeStrides(o.shape),d=a.util.computeStrides(i.shape),h=a.backend_util.computeConv3DInfo(o.shape,c,u,1,l),m=h.strideDepth,f=h.strideHeight,g=h.strideWidth,y=h.filterDepth,b=h.filterHeight,k=h.filterWidth,w=new a.TensorBuffer(h.filterShape,"float32"),v=w.values,[x,N,S,I]=w.strides,T=n.data.get(i.dataId).values,[_,E,A,M]=d,$=n.data.get(o.dataId).values,[D,F,O,B]=p,C=h.padInfo.front,V=h.padInfo.left,R=h.padInfo.top;for(let e=0;e<y;++e){const t=Math.max(0,Math.ceil((C-e)/m)),n=Math.min(h.outDepth,(h.inDepth+C-e)/m),r=e*x;for(let a=0;a<b;++a){const s=Math.max(0,Math.ceil((R-a)/f)),o=Math.min(h.outHeight,(h.inHeight+R-a)/f),i=a*N+r;for(let r=0;r<k;++r){const u=Math.max(0,Math.ceil((V-r)/g)),l=Math.min(h.outWidth,(h.inWidth+V-r)/g),c=r*S+i;for(let i=0;i<h.inChannels;++i){const p=i*I+c;for(let c=0;c<h.outChannels;++c){let d=0;for(let p=0;p<h.batchSize;++p){const h=p*D,y=p*_;for(let p=t;p<n;++p){const t=(e+p*m-C)*F+h,n=p*E+y;for(let e=s;e<o;++e){const s=(a+e*f-R)*O+t,o=e*A+n;for(let e=u;e<l;++e){const t=e*M+o;d+=$[(r+e*g-V)*B+s+i]*T[t+c]}}}}v[p+c]=d}}}}}return n.makeTensorInfo(w.shape,w.dtype,w.values)}};const vn={kernelName:a.Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:o,filter:i}=t,{pad:u,strides:l,inputShape:c}=r;s([o],"conv3dBackpropInputV2");const p=a.util.computeStrides(o.shape),d=a.util.computeStrides(i.shape),h=a.backend_util.computeConv3DInfo(c,i.shape,l,1,u),m=new a.TensorBuffer(h.inShape,"float32"),f=m.values,[g,y,b,k]=m.strides,w=n.data.get(o.dataId).values,[v,x,N,S]=p,I=n.data.get(i.dataId).values,[T,_,E,A]=d,{batchSize:M,filterDepth:$,filterHeight:D,filterWidth:F,inChannels:O,inDepth:B,inHeight:C,inWidth:V,outChannels:R,outDepth:z,outHeight:P,outWidth:L,strideDepth:W,strideHeight:H,strideWidth:q}=h,G=$-1-h.padInfo.front,U=D-1-h.padInfo.top,j=F-1-h.padInfo.left;for(let e=0;e<M;++e)for(let t=0;t<O;++t)for(let n=0;n<B;++n){const r=n-G,a=Math.max(0,Math.ceil(r/W)),s=Math.min(z,($+r)/W);for(let o=0;o<C;++o){const i=o-U,u=Math.max(0,Math.ceil(i/H)),l=Math.min(P,(D+i)/H);for(let c=0;c<V;++c){const p=c-j,d=Math.max(0,Math.ceil(p/q)),h=Math.min(L,(F+p)/q);let m=0;for(let n=a;n<s;++n){const a=n*W-r;for(let r=u;r<l;++r){const s=r*H-i;for(let o=d;o<h;++o){const i=v*e+x*n+N*r+S*o,u=T*($-1-a)+_*(D-1-s)+E*(F-1-(o*q-p))+A*t;for(let e=0;e<R;++e){m+=w[i+e]*I[u+e]}}}}f[g*e+y*n+b*o+k*c+t]=m}}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}},xn=A(a.Cos,(e=>Math.cos(e))),Nn={kernelName:a.Cos,backendName:"cpu",kernelFunc:xn},Sn=A(a.Cosh,(e=>Math.cosh(e))),In={kernelName:a.Cosh,backendName:"cpu",kernelFunc:Sn};const Tn={kernelName:a.CropAndResize,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:s,boxes:o,boxInd:i}=t,{cropSize:u,method:l,extrapolationValue:c}=r,[p,d,h,m]=s.shape,f=o.shape[0],[g,y]=u,b=(0,a.buffer)([f,g,y,m],"float32"),k=n.data.get(o.dataId).values,w=n.data.get(i.dataId).values,v=n.data.get(s.dataId).values,x=a.util.computeStrides(s.shape),N=a.util.computeStrides(b.shape);for(let e=0;e<f;e++){const t=4*e,n=k[t],r=k[t+1],a=k[t+2],s=k[t+3],o=w[e];if(o>=p)continue;const i=g>1?(a-n)*(d-1)/(g-1):0,u=y>1?(s-r)*(h-1)/(y-1):0;for(let t=0;t<g;t++){const p=g>1?n*(d-1)+t*i:.5*(n+a)*(d-1);if(p<0||p>d-1)for(let n=0;n<y;n++)for(let r=0;r<m;r++){const a=r+n*N[2]+t*N[1]+e*N[0];b.values[a]=c}else if("bilinear"===l){const n=Math.floor(p),a=Math.ceil(p),i=p-n;for(let l=0;l<y;l++){const p=y>1?r*(h-1)+l*u:.5*(r+s)*(h-1);if(p<0||p>h-1){for(let n=0;n<m;n++){const r=n+l*N[2]+t*N[1]+e*N[0];b.values[r]=c}continue}const d=Math.floor(p),f=Math.ceil(p),g=p-d;for(let r=0;r<m;r++){let s=r+d*x[2]+n*x[1]+o*x[0];const u=v[s];s=r+f*x[2]+n*x[1]+o*x[0];const c=v[s];s=r+d*x[2]+a*x[1]+o*x[0];const p=v[s];s=r+f*x[2]+a*x[1]+o*x[0];const h=u+(c-u)*g,m=p+(v[s]-p)*g;s=r+l*N[2]+t*N[1]+e*N[0],b.values[s]=h+(m-h)*i}}}else for(let n=0;n<y;++n){const a=y>1?r*(h-1)+n*u:.5*(r+s)*(h-1);if(a<0||a>h-1){for(let r=0;r<m;r++){const a=r+n*N[2]+t*N[1]+e*N[0];b.values[a]=c}continue}const i=Math.round(a),l=Math.round(p);for(let r=0;r<m;r++){const a=r+i*x[2]+l*x[1]+o*x[0],s=r+n*N[2]+t*N[1]+e*N[0];b.values[s]=v[a]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}};const _n={kernelName:a.Cumsum,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{axis:i,exclusive:u,reverse:l}=r;s(o,"cumsum");const c=a.backend_util.getAxesPermutation([i],o.shape.length);let p=o;null!=c&&(p=Ee({inputs:{x:o},backend:n,attrs:{perm:c}}));const d=a.backend_util.getInnerMostAxes(1,o.shape.length)[0];if(d!==p.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${p.shape.length-1} but got axis=${d}`);const h=(0,a.upcastType)(p.dtype,"int32"),m=a.util.makeZerosTypedArray(a.util.sizeFromShape(p.shape),h),f=n.data.get(p.dataId).values,g=p.shape[p.shape.length-1],y=l?(e,t)=>e+g-t-1:(e,t)=>e+t;for(let e=0;e<f.length;e+=g)for(let t=0;t<g;t++){const n=y(e,t);if(0===t)m[n]=u?0:f[n];else{const r=y(e,t-1);m[n]=u?f[r]+m[r]:f[n]+m[r]}}const b=n.makeTensorInfo(p.shape,h,m);if(null!=c){const e=Ee({inputs:{x:b},backend:n,attrs:{perm:a.backend_util.getUndoAxesPermutation(c)}});return n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(p),e}return b}};const En={kernelName:a.DenseBincount,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:o,binaryOutput:i}=r;if(1===a.shape.length){const e=T(n.data.get(a.dataId).values,n.data.get(s.dataId).values,s.dtype,s.shape,o);return n.makeTensorInfo([o],s.dtype,e)}if(2===a.shape.length){const e=_(n.bufferSync(a),n.bufferSync(s),o,i);return n.makeTensorInfo(e.shape,s.dtype,e.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}};const An={kernelName:a.DepthToSpace,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{blockSize:o,dataFormat:i}=r;a.util.assert("NHWC"===i,(()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`)),a.util.assert(o>1,(()=>`blockSize should be > 1 for depthToSpace, but was: ${o}`));const u=s.shape[0],l=s.shape[1],c=s.shape[2],p=s.shape[3],d=l*o,h=c*o,m=p/(o*o),f=n.data.get(s.dataId).values,g=new Float32Array(u*d*h*m);let y=0;for(let e=0;e<u;++e)for(let t=0;t<d;++t){const n=Math.floor(t/o),r=t%o;for(let t=0;t<h;++t){const a=Math.floor(t/o),s=(r*o+t%o)*m;for(let t=0;t<m;++t){const r=t+s+p*(a+c*(n+l*e));g[y++]=f[r]}}}return n.makeTensorInfo([u,d,h,m],s.dtype,g)}};function Mn(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,filter:i}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p}=r;s([o,i],"depthwiseConv2DNative");const d=a.util.computeStrides(o.shape),h=a.util.computeStrides(i.shape);let m=c;null==m&&(m=[1,1]),a.util.assert(a.backend_util.eitherStridesOrDilationsAreOne(u,m),(()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`));const f=a.backend_util.computeConv2DInfo(o.shape,i.shape,u,m,l,p,!0),{filterHeight:g,filterWidth:y,dilationHeight:b,dilationWidth:k,padInfo:w}=f,v=w.left,x=w.top,N=f.outChannels/f.inChannels,S=new a.TensorBuffer(f.outShape,o.dtype),I=n.data.get(o.dataId).values,T=n.data.get(i.dataId).values,_=S.values;for(let e=0;e<f.batchSize;++e){const t=e*d[0],n=e*S.strides[0];for(let e=0;e<f.outHeight;++e){const r=n+e*S.strides[1],a=e*f.strideHeight-x;for(let e=0;e<g;++e){const n=a+e*b;if(n<0||n>=f.inHeight)continue;const s=e*h[0],o=t+n*d[1];for(let e=0;e<f.outWidth;++e){const t=r+e*S.strides[2],n=e*f.strideWidth-v;for(let e=0;e<y;++e){const r=n+e*k;if(r<0||r>=f.inWidth)continue;const a=s+e*h[1],i=o+r*f.inChannels;let u=t,l=a;for(let e=0;e<f.inChannels;++e){const t=I[i+e];for(let e=0;e<N;++e)_[u+e]+=t*T[l+e];u+=N,l+=N}}}}}}return n.makeTensorInfo(S.shape,S.dtype,S.values)}const $n={kernelName:a.DepthwiseConv2dNative,backendName:"cpu",kernelFunc:Mn};const Dn={kernelName:a.DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,dy:i}=t,{strides:u,dilations:l,pad:c,dimRoundingMode:p,filterShape:d}=r;s([o,i],"depthwiseConv2dNativeBackpropFilter");const h=a.backend_util.computeConv2DInfo(o.shape,d,u,l,c,p,!0),{strideHeight:m,strideWidth:f,filterHeight:g,filterWidth:y}=h,b=new a.TensorBuffer(h.filterShape,"float32"),k=h.padInfo.left,w=h.padInfo.top,v=h.outChannels/h.inChannels,x=n.data.get(o.dataId).values,N=new a.TensorBuffer(o.shape,o.dtype,x),S=n.data.get(i.dataId).values,I=new a.TensorBuffer(i.shape,i.dtype,S);for(let e=0;e<g;++e){const t=Math.max(0,Math.ceil((w-e)/m)),n=Math.min(h.outHeight,(h.inHeight+w-e)/m);for(let r=0;r<y;++r){const a=Math.max(0,Math.ceil((k-r)/f)),s=Math.min(h.outWidth,(h.inWidth+k-r)/f);for(let o=0;o<h.outChannels;++o){const i=Math.trunc(o/v),u=o%v;let l=0;for(let u=0;u<h.batchSize;++u)for(let c=t;c<n;++c){const t=e+c*m-w;for(let e=a;e<s;++e){const n=r+e*f-k;l+=N.get(u,t,n,i)*I.get(u,c,e,o)}}b.set(l,e,r,i,u)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}};const Fn={kernelName:a.DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:o,filter:i}=t,{strides:u,dilations:l,pad:c,dimRoundingMode:p,inputShape:d}=r;s([o,i],"depthwiseConv2DNativeBackpropInput");const h=a.util.computeStrides(o.shape),m=a.util.computeStrides(i.shape),f=a.backend_util.computeConv2DInfo(d,i.shape,u,l,c,p,!0),g=new a.TensorBuffer(f.inShape,"float32"),y=g.values,[b,k,w]=g.strides,v=n.data.get(o.dataId).values,[x,N,S]=h,I=n.data.get(i.dataId).values,[T,_,E]=m,{batchSize:A,filterHeight:M,filterWidth:$,inChannels:D,inHeight:F,inWidth:O,outChannels:B,outHeight:C,outWidth:V,strideHeight:R,strideWidth:z}=f,P=M-1-f.padInfo.top,L=$-1-f.padInfo.left,W=B/D;for(let e=0;e<A;++e)for(let t=0;t<D;++t)for(let n=0;n<F;++n){const r=n-P,a=Math.max(0,Math.ceil(r/R)),s=Math.min(C,(M+r)/R);for(let o=0;o<O;++o){const i=o-L,u=Math.max(0,Math.ceil(i/z)),l=Math.min(V,($+i)/z);let c=0;for(let n=a;n<s;++n){const a=n*R-r;for(let r=u;r<l;++r){const s=x*e+N*n+S*r,o=T*(M-1-a)+_*($-1-(r*z-i))+E*t;for(let e=0;e<W;++e){c+=v[s+(t*W+e)]*I[o+e]}}}y[b*e+k*n+w*o+t]=c}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}};const On={kernelName:a.Diag,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,s=a.util.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,i=(0,a.buffer)([s,s],r.dtype),u=i.values;for(let e=0;e<o.length;e++)u[e*s+e]=o[e];const l=[...r.shape,...r.shape];return n.makeTensorInfo(l,i.dtype,i.values)}},Bn={kernelName:a.Dilation2D,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:s}=e,{strides:o,pad:i,dilations:u}=n,l=t,c=l.data.get(r.dataId).values,p=r.shape.length,d=l.data.get(s.dataId).values,h=s.shape.length,{batchSize:m,inHeight:f,inWidth:g,inChannels:y,outHeight:b,outWidth:k,padInfo:w,strideHeight:v,strideWidth:x,filterHeight:N,filterWidth:S,dilationHeight:I,dilationWidth:T,outShape:_}=a.backend_util.computeDilation2DInfo(r.shape,s.shape,o,i,"NHWC",u),E=a.util.sizeFromShape(_),A=_.length,M=a.util.getArrayFromDType(r.dtype,E);for(let e=0;e<m;++e)for(let t=0;t<b;++t){const n=t*v-w.top;for(let o=0;o<k;++o){const i=o*x-w.left;for(let u=0;u<y;++u){let l=Number.MIN_SAFE_INTEGER;for(let t=0;t<N;++t){const o=n+t*I;if(o>=0&&o<f)for(let n=0;n<S;++n){const m=i+n*T;if(m>=0&&m<g){const i=a.util.locToIndex([e,o,m,u],p,a.util.computeStrides(r.shape)),f=a.util.locToIndex([t,n,u],h,a.util.computeStrides(s.shape)),g=c[i]+d[f];g>l&&(l=g)}}}M[a.util.locToIndex([e,t,o,u],A,a.util.computeStrides(_))]=l}}}return{dataId:l.write(a.util.toTypedArray(M,r.dtype),_,r.dtype),shape:_,dtype:r.dtype}}},Cn={kernelName:a.Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:s,dy:o}=e,{strides:i,pad:u,dilations:l}=n,c=t,p=a.util.toNestedArray(r.shape,c.data.get(r.dataId).values),d=a.util.toNestedArray(s.shape,c.data.get(s.dataId).values),{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:k,strideHeight:w,strideWidth:v,filterHeight:x,filterWidth:N,dilationHeight:S,dilationWidth:I,outShape:T}=a.backend_util.computeDilation2DInfo(r.shape,s.shape,i,u,"NHWC",l);a.util.assert(o.rank===T.length,(()=>`Error in ${a.Dilation2DBackpropFilter}, dy must have the same rank as output ${T.length}, but got ${o.rank}`));const _=a.util.toNestedArray(T,c.data.get(o.dataId).values),E=a.util.makeZerosNestedTypedArray(s.shape,s.dtype);for(let e=0;e<h;++e)for(let t=0;t<y;++t){const n=t*w-k.top;for(let r=0;r<b;++r){const a=r*v-k.left;for(let s=0;s<g;++s){let o=Number.MIN_SAFE_INTEGER,i=0,u=0;for(let t=0;t<x;++t){const r=n+t*S;if(r>=0&&r<m)for(let n=0;n<N;++n){const l=a+n*I;if(l>=0&&l<f){const a=p[e][r][l][s]+d[t][n][s];a>o&&(o=a,i=t,u=n)}}}E[i][u][s]+=_[e][t][r][s]}}}return{dataId:c.write(a.util.toTypedArray(E,r.dtype),s.shape,s.dtype),shape:s.shape,dtype:s.dtype}}},Vn={kernelName:a.Dilation2DBackpropInput,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:s,dy:o}=e,{strides:i,pad:u,dilations:l}=n,c=t,p=a.util.toNestedArray(r.shape,c.data.get(r.dataId).values),d=a.util.toNestedArray(s.shape,c.data.get(s.dataId).values),{batchSize:h,inHeight:m,inWidth:f,inChannels:g,outHeight:y,outWidth:b,padInfo:k,strideHeight:w,strideWidth:v,filterHeight:x,filterWidth:N,dilationHeight:S,dilationWidth:I,outShape:T}=a.backend_util.computeDilation2DInfo(r.shape,s.shape,i,u,"NHWC",l);a.util.assert(o.rank===T.length,(()=>`Error in ${a.Dilation2DBackpropInput}, dy must have the same rank as output ${T.length}, but got ${o.rank}`));const _=a.util.toNestedArray(T,c.data.get(o.dataId).values),E=a.util.makeZerosNestedTypedArray(r.shape,r.dtype);for(let e=0;e<h;++e)for(let t=0;t<y;++t){const n=t*w-k.top;for(let r=0;r<b;++r){const a=r*v-k.left;for(let s=0;s<g;++s){let o=Number.MIN_SAFE_INTEGER,i=n<0?0:n,u=a<0?0:a;for(let t=0;t<x;++t){const r=n+t*S;if(r>=0&&r<m)for(let n=0;n<N;++n){const l=a+n*I;if(l>=0&&l<f){const a=p[e][r][l][s]+d[t][n][s];a>o&&(o=a,i=r,u=l)}}}E[e][i][u][s]+=_[e][t][r][s]}}}return{dataId:c.write(a.util.toTypedArray(E,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};function Rn(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{axis:i,keepDims:u}=r;let l;s(o,"sum"),l="bool"===o.dtype?b({inputs:{x:o},backend:n,attrs:{dtype:"int32"}}):m({inputs:{x:o},backend:n});const c=l.shape.length,p=a.util.parseAxisParam(i,l.shape),d=a.backend_util.getAxesPermutation(p,c);let f=p,g=l;null!=d&&(g=Ee({inputs:{x:l},backend:n,attrs:{perm:d}}),f=a.backend_util.getInnerMostAxes(f.length,c)),a.backend_util.assertAxesAreInnerMostDims("sum",f,g.shape.length);const[y,k]=a.backend_util.computeOutAndReduceShapes(g.shape,f);let w=h(n,y,a.backend_util.upcastType(g.dtype,"int32"));const v=a.util.sizeFromShape(k),x=n.data.get(w.dataId).values,N=n.data.get(g.dataId).values;for(let e=0;e<x.length;++e){const t=e*v;let n=0;for(let e=0;e<v;++e)n+=N[t+e];x[e]=n}if(u){const e=w;w=It({inputs:{x:w},backend:n,attrs:{shape:a.backend_util.expandShapeToKeepDim(w.shape,p)}}),n.disposeIntermediateTensorInfo(e)}return n.disposeIntermediateTensorInfo(l),null!=d&&n.disposeIntermediateTensorInfo(g),w}const zn={kernelName:a.Sum,backendName:"cpu",kernelFunc:Rn};const Pn={kernelName:a.Einsum,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{equation:s}=r,o=t,{allDims:i,summedDims:u,idDims:l}=a.backend_util.decodeEinsumEquation(s,o.length);a.backend_util.checkEinsumDimSizes(i.length,l,o);const{path:c,steps:p}=a.backend_util.getEinsumComputePath(u,l),d=p.length;let h=null,m=i.length;const f=[];for(let e=0;e<d;++e){for(const t of p[e]){const{permutationIndices:e,expandDims:r}=a.backend_util.getEinsumPermutation(m,l[t]);let s;a.backend_util.isIdentityPermutation(e)?s=o[t]:(s=Ee({inputs:{x:o[t]},backend:n,attrs:{perm:e}}),f.push(s));const i=s.shape.slice();for(let e=0;e<r.length;++e)i.splice(r[e],0,1);a.util.arraysEqual(s.shape,i)||(s=It({inputs:{x:s},backend:n,attrs:{shape:i}}),f.push(s)),null===h?h=s:(h=we({inputs:{a:s,b:h},backend:n}),f.push(h))}e<d-1&&(c[e]>=0&&(h=Rn({inputs:{x:h},backend:n,attrs:{axis:c[e]-(i.length-m),keepDims:!1}}),f.push(h)),m--)}for(const e of f)e!==h&&n.disposeIntermediateTensorInfo(e);return h}};const Ln={kernelName:a.EluGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{dy:r,y:o}=t;s([r,o],"eluGrad");const i=new Float32Array(a.util.sizeFromShape(o.shape)),u=n.data.get(o.dataId).values,l=n.data.get(r.dataId).values;for(let e=0;e<u.length;++e){const t=u[e];i[e]=t>=1?l[e]:l[e]*(t+1)}return n.makeTensorInfo(o.shape,"float32",i)}},Wn=a.backend_util.ERF_P,Hn=a.backend_util.ERF_A1,qn=a.backend_util.ERF_A2,Gn=a.backend_util.ERF_A3,Un=a.backend_util.ERF_A4,jn=a.backend_util.ERF_A5,Kn=A(a.Erf,(e=>{const t=Math.sign(e),n=Math.abs(e),r=1/(1+Wn*n);return t*(1-((((jn*r+Un)*r+Gn)*r+qn)*r+Hn)*r*Math.exp(-n*n))})),Xn={kernelName:a.Erf,backendName:"cpu",kernelFunc:Kn};function Zn(e){const{inputs:t,backend:n,attrs:r}=e,{input:s}=t,{dim:o}=r,i=s.shape.length,u=s.shape.slice();let l=o;return o<0&&(a.util.assert(-(i+1)<=o,(()=>`Axis must be in the interval [${-(i+1)}, ${i}]`)),l=i+o+1),u.splice(l,0,1),It({inputs:{x:s},backend:n,attrs:{shape:u}})}const Jn={kernelName:a.ExpandDims,backendName:"cpu",kernelFunc:Zn},Qn=c(((e,t)=>e/t)),Yn=w(a.RealDiv,Qn),er={kernelName:a.RealDiv,backendName:"cpu",kernelFunc:Yn};function tr(e,t,n){const r=e.shape,s=r[0],o=r[1],i=n.data.get(e.dataId),u=i.complexTensorInfos.real,l=i.complexTensorInfos.imag,c=[s,o],d=a.util.sizeFromShape(c),h=a.util.getTypedArrayFromDType("float32",d),m=a.util.getTypedArrayFromDType("float32",d);for(let e=0;e<s;e++){const r=Pe({inputs:{x:u},backend:n,attrs:{begin:[e,0],size:[1,o]}}),s=Pe({inputs:{x:l},backend:n,attrs:{begin:[e,0],size:[1,o]}}),i=p({inputs:{real:r,imag:s},backend:n}),{real:c,imag:d}=nr(i,t,n),f=a.backend_util.mergeRealAndImagArrays(c,d);for(let t=0;t<o;t++){const n=a.backend_util.getComplexWithIndex(f,t);h[e*o+t]=n.real,m[e*o+t]=n.imag}n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i)}const f=n.makeTensorInfo(c,"float32",h),g=n.makeTensorInfo(c,"float32",m),y=p({inputs:{real:f,imag:g},backend:n});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),y}function nr(e,t,n){const r=a.util.sizeFromShape(e.shape),s=n.data.get(e.dataId),o=n.data.get(s.complexTensorInfos.real.dataId).values,i=n.data.get(s.complexTensorInfos.imag.dataId).values;if(0==((u=r)&u-1)){const s=rr(o,i,r,t,n),u=[e.shape[0],e.shape[1]];if(t){const e=n.makeTensorInfo(u,"float32",s.real),t=n.makeTensorInfo(u,"float32",s.imag),o=n.makeTensorInfo([],"float32",a.util.createScalarValue(r,"float32")),i=m({inputs:{x:o},backend:n}),l=er.kernelFunc({inputs:{a:e,b:o},backend:n}),c=er.kernelFunc({inputs:{a:t,b:i},backend:n}),p=n.data.get(l.dataId).values,d=n.data.get(c.dataId).values;return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),{real:p,imag:d}}return s}{const e=function(e,t,n){const r=new Float32Array(2*t);for(let s=0;s<t;s++){let o=0,i=0;for(let r=0;r<t;r++){const u=a.backend_util.exponent(s*r,t,n),l=a.backend_util.getComplexWithIndex(e,r);o+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}n&&(o/=t,i/=t),a.backend_util.assignToTypedArray(r,o,i,s)}return r}(a.backend_util.mergeRealAndImagArrays(o,i),r,t);return a.backend_util.splitRealAndImagArrays(e)}var u}function rr(e,t,n,r,s){if(1===n)return{real:e,imag:t};const o=a.backend_util.mergeRealAndImagArrays(e,t),i=n/2,u=a.backend_util.complexWithEvenIndex(o),l=u.real,c=u.imag,d=[l.length],h=s.makeTensorInfo(d,"float32",l),m=s.makeTensorInfo(d,"float32",c),f=p({inputs:{real:h,imag:m},backend:s}),y=a.backend_util.complexWithOddIndex(o),b=y.real,k=y.imag,w=[b.length],v=s.makeTensorInfo(w,"float32",b),x=s.makeTensorInfo(w,"float32",k),N=p({inputs:{real:v,imag:x},backend:s}),I=rr(l,c,i,r,s),T=I.real,_=I.imag,E=[T.length],A=s.makeTensorInfo(E,"float32",T),M=s.makeTensorInfo(E,"float32",_),$=p({inputs:{real:A,imag:M},backend:s}),D=rr(b,k,i,r,s),F=D.real,O=D.imag,B=[F.length],C=s.makeTensorInfo(B,"float32",F),V=s.makeTensorInfo(B,"float32",O),R=p({inputs:{real:C,imag:V},backend:s}),z=a.backend_util.exponents(n,r),P=[z.real.length],L=s.makeTensorInfo(P,"float32",z.real),W=s.makeTensorInfo(P,"float32",z.imag),H=p({inputs:{real:L,imag:W},backend:s}),q=we({inputs:{a:H,b:R},backend:s}),G=S({inputs:{a:$,b:q},backend:s}),U=st({inputs:{a:$,b:q},backend:s}),j=g({inputs:{input:G},backend:s}),K=g({inputs:{input:U},backend:s}),X=pn({inputs:{input:G},backend:s}),Z=pn({inputs:{input:U},backend:s}),J=hn({inputs:[j,K],backend:s,attrs:{axis:0}}),Q=hn({inputs:[X,Z],backend:s,attrs:{axis:0}}),Y=s.data.get(J.dataId).values,ee=s.data.get(Q.dataId).values;return s.disposeIntermediateTensorInfo(h),s.disposeIntermediateTensorInfo(m),s.disposeIntermediateTensorInfo(f),s.disposeIntermediateTensorInfo(v),s.disposeIntermediateTensorInfo(x),s.disposeIntermediateTensorInfo(N),s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(M),s.disposeIntermediateTensorInfo($),s.disposeIntermediateTensorInfo(C),s.disposeIntermediateTensorInfo(V),s.disposeIntermediateTensorInfo(R),s.disposeIntermediateTensorInfo(L),s.disposeIntermediateTensorInfo(W),s.disposeIntermediateTensorInfo(H),s.disposeIntermediateTensorInfo(q),s.disposeIntermediateTensorInfo(G),s.disposeIntermediateTensorInfo(U),s.disposeIntermediateTensorInfo(j),s.disposeIntermediateTensorInfo(X),s.disposeIntermediateTensorInfo(K),s.disposeIntermediateTensorInfo(Z),s.disposeIntermediateTensorInfo(J),s.disposeIntermediateTensorInfo(Q),{real:Y,imag:ee}}const ar={kernelName:a.FFT,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t,s=a.util.sizeFromShape(r.shape),o=r.shape[r.shape.length-1],i=It({inputs:{x:r},backend:n,attrs:{shape:[s/o,o]}}),u=tr(i,!1,n),l=It({inputs:{x:u},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}};function sr(e){const{backend:t,attrs:n}=e,{shape:r,value:s,dtype:o}=n,i=o||a.util.inferDtype(s),u=a.util.getArrayFromDType(i,a.util.sizeFromShape(r));return function(e,t,n){e.fill(t)}(u,s),t.makeTensorInfo(r,i,u)}const or={kernelName:a.Fill,backendName:"cpu",kernelFunc:sr};const ir={kernelName:a.FlipLeftRight,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:r}=e,s=n,o=a.util.getTypedArrayFromDType(r.dtype,a.util.sizeFromShape(r.shape)),[i,u,l,c]=r.shape,p=s.data.get(r.dataId).values;for(let e=0;e<i;e++){const t=e*l*u*c;for(let e=0;e<u;e++){const n=e*(l*c);for(let e=0;e<l;e++){const r=e*c;for(let a=0;a<c;a++){const s=Math.round(l-e-1),i=t+n+r+a;let u=p[i];if(s>=0&&s<l){u=p[t+n+s*c+a]}o[i]=u}}}}return{dataId:s.write(o,r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},ur=c(((e,t)=>Math.floor(e/t))),lr=w(a.FloorDiv,ur,null,"int32"),cr={kernelName:a.FloorDiv,backendName:"cpu",kernelFunc:lr};const pr={kernelName:a.FusedConv2D,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=r;let f=fn({inputs:{x:a,filter:s},backend:n,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d}});if(o){const e=f;f=S({inputs:{a:f,b:o},backend:n}),n.disposeIntermediateTensorInfo(e)}if(h){const e=f;f=St(n,f,h,i,m),n.disposeIntermediateTensorInfo(e)}return f}};const dr={kernelName:a.FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:m}=r;let f=Mn({inputs:{x:a,filter:s},backend:n,attrs:{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d}});if(o){const e=f;f=S({inputs:{a:f,b:o},backend:n}),n.disposeIntermediateTensorInfo(e)}if(h){const e=f;f=St(n,f,h,i,m),n.disposeIntermediateTensorInfo(e)}return f}};const hr={kernelName:a.GatherNd,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:s}=t,o=a.util.sizeFromShape(r.shape),i=s.shape,u=i[i.length-1],[l,c,p,d]=a.backend_util.prepareAndValidate(r,s);if(0===c)return n.makeTensorInfo(l,r.dtype,[]);const h=j(n.data.get(s.dataId).values,n.bufferSync(r),r.dtype,c,u,p,d,r.shape,o);return n.makeTensorInfo(l,r.dtype,h.values)}};const mr={kernelName:a.GatherV2,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,indices:i}=t,{axis:u,batchDims:l}=r;s([o,i],"gatherV2");let c=l;null==l&&(c=0);const p=a.util.sizeFromShape(i.shape),d=a.util.parseAxisParam(u,o.shape)[0],h=a.backend_util.segment_util.collectGatherOpShapeInfo(o,i,d,c),m=It({inputs:{x:o},backend:n,attrs:{shape:[h.batchSize,h.outerSize,h.dimSize,h.sliceSize]}}),f=It({inputs:{x:i},backend:n,attrs:{shape:[h.batchSize,p/h.batchSize]}}),g=[h.batchSize,h.outerSize,p/h.batchSize,h.sliceSize],y=n.bufferSync(f),b=K(n.bufferSync(m),y,g);return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.makeTensorInfo(h.outputShape,b.dtype,b.values)}};const fr={kernelName:a.IFFT,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t,s=a.util.sizeFromShape(r.shape),o=r.shape[r.shape.length-1],i=It({inputs:{x:r},backend:n,attrs:{shape:[s/o,o]}}),u=tr(i,!0,n),l=It({inputs:{x:u},backend:n,attrs:{shape:r.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}},gr=A(a.IsFinite,(e=>Number.isFinite(e)?1:0),"bool"),yr={kernelName:a.IsFinite,backendName:"cpu",kernelFunc:gr},br=A(a.IsInf,(e=>Math.abs(e)===1/0?1:0),"bool"),kr={kernelName:a.IsInf,backendName:"cpu",kernelFunc:br},wr=A(a.IsNan,(e=>Number.isNaN(e)?1:0),"bool"),vr={kernelName:a.IsNan,backendName:"cpu",kernelFunc:wr};const xr={kernelName:a.LinSpace,backendName:"cpu",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,o=ie(r,a,s);return t.makeTensorInfo([o.length],"float32",o)}},Nr=A(a.Log1p,(e=>Math.log1p(e))),Sr={kernelName:a.Log1p,backendName:"cpu",kernelFunc:Nr},Ir=c(((e,t)=>e&&t)),Tr=w(a.LogicalAnd,Ir,null,"bool"),_r={kernelName:a.LogicalAnd,backendName:"cpu",kernelFunc:Tr},Er=A(a.LogicalNot,(e=>e?0:1),"bool"),Ar={kernelName:a.LogicalNot,backendName:"cpu",kernelFunc:Er},Mr=c(((e,t)=>e||t)),$r=w(a.LogicalOr,Mr,null,"bool"),Dr={kernelName:a.LogicalOr,backendName:"cpu",kernelFunc:$r};const Fr={kernelName:a.LRN,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=r;s(o,"LRN");const p=o.shape[3],d=p-1,h=n.data.get(o.dataId).values,m=a.util.sizeFromShape(o.shape),f=new Float32Array(m);function g(e){const t=e%p;let n=e-t+Math.max(0,t-i);const r=e-t+Math.min(t+i,d);let a=0;for(;n<=r;n++){const e=h[n];a+=e*e}return a}for(let e=0;e<m;e++){const t=g(e),n=h[e]*Math.pow(u+l*t,-c);f[e]=n}return n.makeTensorInfo(o.shape,o.dtype,f)}};const Or={kernelName:a.LRNGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o,y:i,dy:u}=t,{depthRadius:l,bias:c,alpha:p,beta:d}=r;s(u,"LRNGrad");const h=a.util.sizeFromShape(u.shape),m=u.shape[3],f=n.data.get(u.dataId).values,g=n.data.get(o.dataId).values,y=n.data.get(i.dataId).values,b=new Float32Array(h),k=h;for(let e=0;e<k;e++){const t=e%m,n=e-t+Math.max(0,t-l),r=e-t+Math.min(m,t+l+1);let a=0;for(let e=n;e<r;e++)a+=Math.pow(g[e],2);a=p*a+c;for(let t=n;t<r;t++){let n=-2*p*d*g[t]*y[e]/a;e===t&&(n+=Math.pow(a,-d)),n*=f[e],b[t]+=n}}return n.makeTensorInfo(u.shape,o.dtype,b)}};function Br(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{reductionIndices:i,keepDims:u}=r,l=n;let c=o.shape;const p=c.length,d=a.util.parseAxisParam(i,c);let h=d;const m=a.backend_util.getAxesPermutation(h,p);let f=l.data.get(o.dataId).values;if(null!=m){const e=new Array(p);for(let t=0;t<e.length;t++)e[t]=c[m[t]];f=_e(f,c,o.dtype,m,e),h=a.backend_util.getInnerMostAxes(h.length,p),c=e}s(o,"max"),a.backend_util.assertAxesAreInnerMostDims("max",h,p);const[g,y]=a.backend_util.computeOutAndReduceShapes(c,h),b=pe(f,a.util.sizeFromShape(y),g,o.dtype),k=l.write(b,g,o.dtype);let w=g;if(u){w=a.backend_util.expandShapeToKeepDim(g,d)}return{dataId:k,shape:w,dtype:o.dtype}}const Cr={kernelName:a.Max,backendName:"cpu",kernelFunc:Br};const Vr={kernelName:a.MaxPool,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t;s(o,"maxPool");const{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=r;a.util.assert(a.backend_util.eitherStridesOrDilationsAreOne(u,1),(()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${u} and dilations '1'`));const p=a.backend_util.computePool2DInfo(o.shape,i,u,1,l,c);let d;if(1===p.filterWidth&&1===p.filterHeight&&a.util.arraysEqual(p.inShape,p.outShape))d=m({inputs:{x:o},backend:n});else{const e=n.data.get(o.dataId).values,t=a.util.computeStrides(o.shape),r=Zt(e,o.shape,o.dtype,t,p,"max");d=n.makeTensorInfo(p.outShape,o.dtype,r.values)}return d}};const Rr={kernelName:a.MaxPool3D,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{filterSize:i,strides:u,pad:l,dimRoundingMode:c,dataFormat:p}=r;s(o,"maxPool3d");const d=a.backend_util.computePool3DInfo(o.shape,i,u,1,l,c,p),h=Qt(n.data.get(o.dataId).values,o.shape,o.dtype,a.util.computeStrides(o.shape),d,"max");return n.makeTensorInfo(h.shape,"float32",h.values)}};const zr={kernelName:a.MaxPool3DGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:o,input:i}=t,{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=r;s([o,i],"maxPool3DGrad");const d=a.backend_util.computePool3DInfo(i.shape,u,l,1,c,p),h=function(e,t){const n=(0,a.buffer)(t.outShape,"int32"),r=t.strideDepth,s=t.strideHeight,o=t.strideWidth,i=t.dilationDepth,u=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,p=t.effectiveFilterHeight,d=t.effectiveFilterWidth,h=t.padInfo.front,m=t.padInfo.top,f=t.padInfo.left;for(let a=0;a<t.batchSize;++a)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){const b=y*r-h;let k=b;for(;k<0;)k+=i;const w=Math.min(t.inDepth,c+b);for(let r=0;r<t.outHeight;++r){const c=r*s-m;let h=c;for(;h<0;)h+=u;const v=Math.min(t.inHeight,p+c);for(let s=0;s<t.outWidth;++s){const m=s*o-f;let x=m;for(;x<0;)x+=l;const N=Math.min(t.inWidth,d+m);let S=Number.NEGATIVE_INFINITY,I=-1;for(let t=k;t<w;t+=i){const n=t-b;for(let r=h;r<v;r+=u){const s=r-c;for(let o=x;o<N;o+=l){const i=o-m,u=e.get(a,t,r,o,g);u>=S&&(S=u,I=n*p*d+s*p+i)}}}n.set(I,a,y,r,s,g)}}}return n}(n.bufferSync(i),d),m=d.strideDepth,f=d.strideHeight,g=d.strideWidth,y=d.dilationDepth,b=d.dilationHeight,k=d.dilationWidth,w=d.effectiveFilterDepth,v=d.effectiveFilterHeight,x=d.effectiveFilterWidth,N=w-1-d.padInfo.front,S=x-1-d.padInfo.left,I=v-1-d.padInfo.top,T=(0,a.buffer)(i.shape,"float32"),_=n.bufferSync(o);for(let e=0;e<d.batchSize;++e)for(let t=0;t<d.inChannels;++t)for(let n=0;n<d.inDepth;++n)for(let r=0;r<d.inHeight;++r)for(let a=0;a<d.inWidth;++a){const s=n-N,o=r-I,i=a-S;let u=0;for(let n=0;n<w;n+=y){const r=(s+n)/m;if(!(r<0||r>=d.outDepth||Math.floor(r)!==r))for(let a=0;a<v;a+=b){const s=(o+a)/f;if(!(s<0||s>=d.outHeight||Math.floor(s)!==s))for(let o=0;o<x;o+=k){const l=(i+o)/g;if(l<0||l>=d.outWidth||Math.floor(l)!==l)continue;const c=w*v*x-1-h.get(e,r,s,l,t)===n*v*x+a*x+o?1:0;if(0===c)continue;u+=_.get(e,r,s,l,t)*c}}}T.set(u,e,n,r,a,t)}return n.makeTensorInfo(T.shape,T.dtype,T.values)}};const Pr={kernelName:a.MaxPoolGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:o,input:i,output:u}=t,l=i;s([i,u],"maxPoolGrad");const{filterSize:c,strides:p,pad:d,dimRoundingMode:h}=r,m=a.backend_util.computePool2DInfo(l.shape,c,p,1,d,h),f=n.data.get(l.dataId).values,g=(0,a.buffer)(m.outShape,l.dtype,Jt(f,l.shape,l.dtype,m).values),y=m.strideHeight,b=m.strideWidth,k=m.dilationHeight,w=m.dilationWidth,v=m.effectiveFilterHeight,x=m.effectiveFilterWidth,N=x-1-m.padInfo.left,S=v-1-m.padInfo.top,I=(0,a.buffer)(l.shape,"float32"),T=n.data.get(o.dataId).values,_=(0,a.buffer)(o.shape,"float32",T);for(let e=0;e<m.batchSize;++e)for(let t=0;t<m.inChannels;++t)for(let n=0;n<m.inHeight;++n)for(let r=0;r<m.inWidth;++r){const a=n-S,s=r-N;let o=0;for(let n=0;n<v;n+=k){const r=(a+n)/y;if(!(r<0||r>=m.outHeight||Math.floor(r)!==r))for(let a=0;a<x;a+=w){const i=(s+a)/b;if(i<0||i>=m.outWidth||Math.floor(i)!==i)continue;const u=v*x-1-g.get(e,r,i,t)===n*x+a?1:0;if(0===u)continue;o+=_.get(e,r,i,t)*u}}I.set(o,e,n,r,t)}return n.makeTensorInfo(I.shape,I.dtype,I.values)}};const Lr={kernelName:a.MaxPoolWithArgmax,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:o,strides:i,pad:u,includeBatchInIndex:l}=t,c=n;s(r,"MaxPoolWithArgmax");const p=c.data.get(r.dataId).values,d=a.backend_util.computePool2DInfo(r.shape,o,i,[1,1],u),[h,m]=function(e,t,n,r,s){const o=Zt(e,0,n,a.util.computeStrides(t),s,"max"),i=Jt(e,t,n,s,!0,r);return[o.values,i.values]}(p,r.shape,r.dtype,l,d),f=c.write(h,d.outShape,r.dtype),g=c.write(m,d.outShape,r.dtype);return[{dataId:f,shape:d.outShape,dtype:r.dtype},{dataId:g,shape:d.outShape,dtype:"int32"}]}};const Wr={kernelName:a.Mean,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:s}=t,{axis:o,keepDims:i}=r,u=a.util.parseAxisParam(o,s.shape),l=a.backend_util.computeOutAndReduceShapes(s.shape,u)[1],c=a.util.sizeFromShape(l),p=[],d=n.makeTensorInfo([],"float32",new Float32Array([c]));p.push(d);const h=b({inputs:{x:s},backend:n,attrs:{dtype:"float32"}});p.push(h);const m=Yn({inputs:{a:h,b:d},backend:n});p.push(m);const f=Rn({inputs:{x:m},backend:n,attrs:{axis:o,keepDims:i}});return p.forEach((e=>n.disposeIntermediateTensorInfo(e))),f}};const Hr={kernelName:a.Min,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{axis:i,keepDims:u}=r;s(o,"min");const l=a.util.parseAxisParam(i,o.shape);let c=l;const p=a.backend_util.getAxesPermutation(c,o.shape.length);let d=o;null!=p&&(d=Ee({inputs:{x:o},backend:n,attrs:{perm:p}}),c=a.backend_util.getInnerMostAxes(c.length,o.shape.length)),a.backend_util.assertAxesAreInnerMostDims("min",c,d.shape.length);const[h,m]=a.backend_util.computeOutAndReduceShapes(d.shape,c),f=a.util.sizeFromShape(m),g=a.util.makeZerosTypedArray(a.util.sizeFromShape(h),d.dtype),y=n.data.get(d.dataId).values;for(let e=0;e<g.length;++e){const t=e*f;let n=y[t];for(let e=0;e<f;++e){const r=y[t+e];(Number.isNaN(r)||r<n)&&(n=r)}g[e]=n}null!=p&&n.disposeIntermediateTensorInfo(d);const b=n.makeTensorInfo(h,d.dtype,g);if(u){const e=It({inputs:{x:b},backend:n,attrs:{shape:a.backend_util.expandShapeToKeepDim(h,l)}});return n.disposeIntermediateTensorInfo(b),e}return b}};const qr={kernelName:a.MirrorPad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:o}=t,{paddings:i,mode:u}=r;s(o,"mirrorPad");const l=i.map(((e,t)=>e[0]+o.shape[t]+e[1])),c=i.map((e=>e[0])),p=i.map(((e,t)=>e[0]+o.shape[t])),d="reflect"===u?0:1,h=n.data.get(o.dataId).values,m=o.shape.length,f=a.util.computeStrides(o.shape),g=a.util.sizeFromShape(l),y=l.length,b=a.util.computeStrides(l),k=a.util.getTypedArrayFromDType(o.dtype,g);for(let e=0;e<g;e++){let t=a.util.indexToLoc(e,y,b);for(let e=0;e<y;e++)t[e]<c[e]?t[e]=2*c[e]-t[e]-d:t[e]>=p[e]&&(t[e]=2*(p[e]-1)-t[e]+d);t=t.map(((e,t)=>e-c[t]));const n=a.util.locToIndex(t,m,f);k[e]=h[n]}return{dataId:n.write(k,l,o.dtype),shape:l,dtype:o.dtype}}},Gr=c(((e,t)=>{const n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t})),Ur=w(a.Mod,Gr),jr={kernelName:a.Mod,backendName:"cpu",kernelFunc:Ur};var Kr=n(6377);function Xr(e){const{inputs:t,backend:n,attrs:r}=e,{logits:s}=t,{dim:o}=r,i=s.shape.length;let u=o;if(-1===u&&(u=i-1),u!==i-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${u}`);const l=a.util.parseAxisParam([u],s.shape),c=Br({inputs:{x:s},backend:n,attrs:{reductionIndices:l,keepDims:!1}}),p=a.backend_util.expandShapeToKeepDim(c.shape,l),d=It({inputs:{x:c},backend:n,attrs:{shape:p}}),h=st({inputs:{a:s,b:d},backend:n}),m=z({inputs:{x:h},backend:n}),f=Rn({inputs:{x:m},backend:n,attrs:{axis:l,keepDims:!1}}),g=It({inputs:{x:f},backend:n,attrs:{shape:p}}),y=Yn({inputs:{a:m,b:g},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),y}const Zr={kernelName:a.Softmax,backendName:"cpu",kernelFunc:Xr};const Jr={kernelName:a.Multinomial,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:o}=t,{numSamples:i,seed:u,normalized:l}=r;s(o,"multinomial");const c=l?o:Xr({inputs:{logits:o},backend:n,attrs:{dim:-1}}),p=c.shape[0],d=c.shape[1],h=n.data.get(c.dataId).values,m=[p,i],f=a.util.makeZerosTypedArray(a.util.sizeFromShape(m),"int32");for(let e=0;e<p;++e){const t=e*d,n=new Float32Array(d-1);n[0]=h[t];for(let e=1;e<n.length;++e)n[e]=n[e-1]+h[t+e];const r=Kr.alea(u.toString()),a=e*i;for(let e=0;e<i;++e){const t=r();f[a+e]=n.length;for(let r=0;r<n.length;r++)if(t<n[r]){f[a+e]=r;break}}}return l||n.disposeIntermediateTensorInfo(c),n.makeTensorInfo(m,"int32",f)}},Qr=a.kernel_impls.nonMaxSuppressionV3Impl;const Yr={kernelName:a.NonMaxSuppressionV3,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:o}=t,{maxOutputSize:i,iouThreshold:u,scoreThreshold:l}=r;s(a,"NonMaxSuppression");const c=n.data.get(a.dataId).values,p=n.data.get(o.dataId).values,{selectedIndices:d}=Qr(c,p,i,u,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}},ea=a.kernel_impls.nonMaxSuppressionV4Impl;const ta={kernelName:a.NonMaxSuppressionV4,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:o}=t,{maxOutputSize:i,iouThreshold:u,scoreThreshold:l,padToMaxOutputSize:c}=r;s(a,"NonMaxSuppressionPadded");const p=n.data.get(a.dataId).values,d=n.data.get(o.dataId).values,{selectedIndices:h,validOutputs:m}=ea(p,d,i,u,l,c);return[n.makeTensorInfo([h.length],"int32",new Int32Array(h)),n.makeTensorInfo([],"int32",new Int32Array([m]))]}},na=a.kernel_impls.nonMaxSuppressionV5Impl;const ra={kernelName:a.NonMaxSuppressionV5,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:o}=t,{maxOutputSize:i,iouThreshold:u,scoreThreshold:l,softNmsSigma:c}=r;s(a,"NonMaxSuppressionWithScore");const p=n.data.get(a.dataId).values,d=n.data.get(o.dataId).values,h=i,m=u,f=l,g=c,{selectedIndices:y,selectedScores:b}=na(p,d,h,m,f,g);return[n.makeTensorInfo([y.length],"int32",new Int32Array(y)),n.makeTensorInfo([b.length],"float32",new 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oa={kernelName:a.ZerosLike,backendName:"cpu",kernelFunc:sa};const ia={kernelName:a.OnesLike,backendName:"cpu",kernelFunc:function e(t){const{inputs:n,backend:r}=t,{x:a}=n;if("string"===a.dtype)throw new Error("onesLike is not supported for string tensors");if("complex64"===a.dtype){const t=g({inputs:{input:a},backend:r}),n=e({inputs:{x:t},backend:r}),s=pn({inputs:{input:a},backend:r}),o=sa({inputs:{x:s},backend:r}),i=p({inputs:{real:n,imag:o},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(o),i}return sr({backend:r,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}};function ua(e){const{inputs:t,backend:n,attrs:r}=e,{axis:s}=r;if(1===t.length)return Zn({inputs:{input:t[0]},backend:n,attrs:{dim:s}});const o=t[0].shape,i=t[0].dtype;t.forEach((e=>{a.util.assertShapesMatch(o,e.shape,"All tensors passed to stack must have matching shapes"),a.util.assert(i===e.dtype,(()=>"All tensors 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t=a.util.indexToLoc(e,h,m).map(((e,t)=>e+c[t]));b[a.util.locToIndex(t,g,y)]=p[e]}return{dataId:n.write(b,l,o.dtype),shape:l,dtype:o.dtype}}},pa=c(((e,t)=>Math.pow(e,t))),da=w(a.Pow,pa),ha={kernelName:a.Pow,backendName:"cpu",kernelFunc:da};const ma={kernelName:a.Range,backendName:"cpu",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:a,dtype:s,step:o}=n,i=De(r,a,o,s);return t.makeTensorInfo([i.length],s,i)}},fa=A(a.Reciprocal,(e=>1/e)),ga={kernelName:a.Reciprocal,backendName:"cpu",kernelFunc:fa};const ya={kernelName:a.ResizeBilinear,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:o}=t,{alignCorners:i,halfPixelCenters:u,size:l}=r;s(o,"resizeBilinear");const c=a.util.computeStrides(o.shape),[p,d]=l,[h,m,f,g]=o.shape,y=n.data.get(o.dataId).values,b=new Float32Array(a.util.sizeFromShape([h,p,d,g])),k=[i&&p>1?m-1:m,i&&d>1?f-1:f],w=[i&&p>1?p-1:p,i&&d>1?d-1:d];let v=0;const x=k[0]/w[0],N=k[1]/w[1];for(let e=0;e<h;e++)for(let t=0;t<p;t++){let n;n=u?x*(t+.5)-.5:x*t;const r=Math.max(0,Math.floor(n)),a=n-r,s=Math.min(m-1,Math.ceil(n)),o=e*c[0]+r*c[1],i=e*c[0]+s*c[1];for(let e=0;e<d;e++){let t;t=u?N*(e+.5)-.5:N*e;const n=Math.max(0,Math.floor(t)),r=t-n,s=Math.min(f-1,Math.ceil(t)),l=o+n*c[2],p=i+n*c[2],d=o+s*c[2],h=i+s*c[2];for(let e=0;e<g;e++){const t=y[l+e],n=y[p+e],s=t+(y[d+e]-t)*r,o=s+(n+(y[h+e]-n)*r-s)*a;b[v++]=o}}}return n.makeTensorInfo([h,p,d,g],"float32",b)}};const ba={kernelName:a.ResizeBilinearGrad,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:o,dy:i}=t,{alignCorners:u}=r;s([i,o],"resizeBilinearGrad");const l=a.util.computeStrides(o.shape),[c,p,d,h]=o.shape,[,m,f]=i.shape,g=new Float32Array(c*p*d*h),y=[u&&m>1?p-1:p,u&&f>1?d-1:d],b=[u&&m>1?m-1:m,u&&f>1?f-1:f],k=y[0]/b[0],w=y[1]/b[1],v=n.data.get(i.dataId).values;let x=0;for(let e=0;e<c;e++){const t=e*l[0];for(let e=0;e<m;e++){const n=e*k,r=Math.floor(n),a=Math.min(Math.ceil(n),p-1),s=t+r*l[1],o=t+a*l[1],i=n-r,u=1-i;for(let 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u=r.shape.length,l=n.data.get(r.dataId).values,c=n.data.get(o.dataId).values,p=n.data.get(i.dataId).values,d=(0,a.upcastType)(o.dtype,i.dtype),h=a.util.makeZerosTypedArray(a.util.sizeFromShape(o.shape),d);let m=0;const f=0===u||u>1||1===o.shape.length?1:a.util.sizeFromShape(o.shape.slice(1));for(let e=0;e<l.length;e++)for(let t=0;t<f;t++)1===l[e]?h[m++]=c[e]:h[m++]=p[e];return n.makeTensorInfo(o.shape,d,h)}},Ea=a.backend_util.SELU_SCALEALPHA,Aa=a.backend_util.SELU_SCALE,Ma=A(a.Selu,(e=>e>=0?Aa*e:Ea*(Math.exp(e)-1))),$a={kernelName:a.Selu,backendName:"cpu",kernelFunc:Ma},Da=A(a.Sign,(e=>e<0?-1:e>0?1:0)),Fa={kernelName:a.Sign,backendName:"cpu",kernelFunc:Da},Oa=A(a.Sin,(e=>Math.sin(e))),Ba={kernelName:a.Sin,backendName:"cpu",kernelFunc:Oa},Ca=A(a.Sinh,(e=>Math.sinh(e))),Va={kernelName:a.Sinh,backendName:"cpu",kernelFunc:Ca},Ra=Math.log(1.1920928955078125e-7)+2,za=A(a.Softplus,(e=>{const t=e>-Ra,n=e<Ra,r=Math.exp(e);let a;return 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Wa={kernelName:a.SparseFillEmptyRows,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:a,denseShape:s,defaultValue:o}=t;if(1!==s.shape.length)throw new Error(`Dense shape must be a vector, saw:\n        ${s.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n        ${r.shape}`);if(1!==a.shape.length)throw new Error(`Values must be a vector, saw:\n        ${a.shape}`);if(0!==o.shape.length)throw new Error(`Default value must be a scalar, saw:\n        ${o.shape}`);const i=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=n.data.get(s.dataId).values,c=n.data.get(o.dataId).values[0],[p,d,h,m,f]=We(i,r.shape,r.dtype,u,a.dtype,l,c);return[n.makeTensorInfo(d,r.dtype,p),n.makeTensorInfo([d[0]],a.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map((e=>Number(e))))),n.makeTensorInfo([f.length],r.dtype,new Int32Array(f))]}};const Ha={kernelName:a.SparseReshape,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:s}=t;if(2!==r.shape.length)throw new Error(`Input indices should be a matrix but received shape\n        ${r.shape}`);if(1!==a.shape.length)throw new Error(`Input shape should be a vector but received shape\n        ${a.shape}`);if(1!==s.shape.length)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);const o=Array.from(n.data.get(a.dataId).values),i=n.data.get(r.dataId).values,u=Array.from(n.data.get(s.dataId).values),[l,c,p]=He(i,r.shape,r.dtype,o,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}};const qa={kernelName:a.SparseSegmentMean,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:s}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.shape.length)throw new 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implemented`)}})(e,t,n)));case"dynamic":return(async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{const{boxes:r,scores:a,maxOutputSize:s,iouThreshold:o,scoreThreshold:i,softNmsSigma:u}=Qt(e,t,n),l=await Kt.BHj.nonMaxSuppressionWithScoreAsync(r,a,s,o,i,u);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:r,scores:a,maxOutputSize:s,iouThreshold:o,scoreThreshold:i}=Qt(e,t,n),u=A("padToMaxOutputSize",e,t,n),l=await Kt.BHj.nonMaxSuppressionPaddedAsync(r,a,s,o,i,u);return[l.selectedIndices,l.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{const{boxes:r,scores:a,maxOutputSize:s,iouThreshold:o,scoreThreshold:i}=Qt(e,t,n);return[await Kt.BHj.nonMaxSuppressionAsync(r,a,s,o,i)]}case"Where":{const r=Xt.p(A("condition",e,t,n),"bool"),a=[await Zt.i(r)];return r.dispose(),a}case"ListDiff":return Jt.O(A("x",e,t,n),A("y",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n);case"evaluation":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"TopKV2":{const r=A("x",e,t,n),a=A("k",e,t,n),s=A("sorted",e,t,n),o=Yt.h(r,a,s);return[o.values,o.indices]}case"Unique":{const r=A("x",e,t,n),a=en.T(r);return[a.values,a.indices]}case"UniqueV2":{const r=A("x",e,t,n),a=A("axis",e,t,n),s=en.T(r,a);return[s.values,s.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"image":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"ResizeBilinear":{const r=A("images",e,t,n),a=A("size",e,t,n),s=A("alignCorners",e,t,n),o=A("halfPixelCenters",e,t,n);return[Kt.BHj.resizeBilinear(r,[a[0],a[1]],s,o)]}case"ResizeNearestNeighbor":{const r=A("images",e,t,n),a=A("size",e,t,n),s=A("alignCorners",e,t,n),o=A("halfPixelCenters",e,t,n);return[Kt.BHj.resizeNearestNeighbor(r,[a[0],a[1]],s,o)]}case"CropAndResize":{const r=A("image",e,t,n),a=A("boxes",e,t,n),s=A("boxInd",e,t,n),o=A("cropSize",e,t,n),i=A("method",e,t,n),u=A("extrapolationValue",e,t,n);return[Kt.BHj.cropAndResize(r,a,s,o,i,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"graph":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"Const":return t[e.name];case"PlaceholderWithDefault":const r=A("default",e,t,n);return[M(e.name,t,n)||r];case"Placeholder":return[M(e.name,t,n)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":return[B(A("x",e,t,n))];case"IdentityN":return A("x",e,t,n).map((e=>B(e)));case"Snapshot":return[B(A("x",e,t,n))];case"Shape":return[tn.R(A("x",e,t,n).shape,"int32")];case"ShapeN":return A("x",e,t,n).map((e=>tn.R(e.shape)));case"Size":return[nn.i(A("x",e,t,n).size,"int32")];case"Rank":return[nn.i(A("x",e,t,n).rank,"int32")];case"NoOp":return[nn.i(1)];case"Print":const a=A("x",e,t,n),s=A("data",e,t,n),o=A("message",e,t,n),i=A("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let e=0;e<s.length;e++)console.log(Array.prototype.slice.call(s[e].dataSync()).slice(0,i));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"logical":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"Equal":return[an.D(A("a",e,t,n),A("b",e,t,n))];case"NotEqual":return[sn.Q(A("a",e,t,n),A("b",e,t,n))];case"Greater":return[on.p(A("a",e,t,n),A("b",e,t,n))];case"GreaterEqual":return[un.b(A("a",e,t,n),A("b",e,t,n))];case"Less":return[ln.d(A("a",e,t,n),A("b",e,t,n))];case"LessEqual":return[cn.z(A("a",e,t,n),A("b",e,t,n))];case"LogicalAnd":return[pn.H(A("a",e,t,n),A("b",e,t,n))];case"LogicalNot":return[dn.h(A("a",e,t,n))];case"LogicalOr":return[hn.K(A("a",e,t,n),A("b",e,t,n))];case"Select":case"SelectV2":return[mn.a(A("condition",e,t,n),A("a",e,t,n),A("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"matrices":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[fn.O(A("a",e,t,n),A("b",e,t,n),A("transposeA",e,t,n),A("transposeB",e,t,n))];case"Einsum":return[gn.W(A("equation",e,t,n),...A("tensors",e,t,n))];case"Transpose":return[yn.p(A("x",e,t,n),A("perm",e,t,n))];case"_FusedMatMul":const[r,a]=A("fusedOps",e,t,n),s="biasadd"===r,o="prelu"===a,i=A("numArgs",e,t,n),u=A("leakyreluAlpha",e,t,n);if(s){if(o&&2!==i)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&1!==i)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[l,c]=A("args",e,t,n);return[Tt.matMul({a:A("a",e,t,n),b:A("b",e,t,n),transposeA:A("transposeA",e,t,n),transposeB:A("transposeB",e,t,n),bias:l,activation:a,preluActivationWeights:c,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"normalization":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[bn.t(A("x",e,t,n),A("mean",e,t,n),A("variance",e,t,n),A("offset",e,t,n),A("scale",e,t,n),A("epsilon",e,t,n))];case"LRN":return[kn.G(A("x",e,t,n),A("radius",e,t,n),A("bias",e,t,n),A("alpha",e,t,n),A("beta",e,t,n))];case"Softmax":return[wn.X(A("x",e,t,n))];case"LogSoftmax":return[vn.C(A("x",e,t,n))];case"SparseToDense":return[xn.e(A("sparseIndices",e,t,n),A("outputShape",e,t,n),A("sparseValues",e,t,n),A("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"reduction":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"Max":{const r=A("axis",e,t,n),a=A("keepDims",e,t,n);return[Nn.F(A("x",e,t,n),r,a)]}case"Mean":{const r=A("axis",e,t,n),a=A("keepDims",e,t,n);return[Sn.J(A("x",e,t,n),r,a)]}case"Min":{const r=A("axis",e,t,n),a=A("keepDims",e,t,n);return[In.V(A("x",e,t,n),r,a)]}case"Sum":{const r=A("axis",e,t,n),a=A("keepDims",e,t,n);return[Tn.S(A("x",e,t,n),r,a)]}case"All":{const r=A("axis",e,t,n),a=A("keepDims",e,t,n);return[_n.$(A("x",e,t,n),r,a)]}case"Any":{const r=A("axis",e,t,n),a=A("keepDims",e,t,n);return[En.Y(A("x",e,t,n),r,a)]}case"ArgMax":{const r=A("axis",e,t,n);return[An.N(A("x",e,t,n),r)]}case"ArgMin":{const r=A("axis",e,t,n);return[Mn.v(A("x",e,t,n),r)]}case"Prod":{const r=A("axis",e,t,n),a=A("keepDims",e,t,n);return[ht.W(A("x",e,t,n),r,a)]}case"Cumsum":{const r=A("axis",e,t,n),a=A("exclusive",e,t,n),s=A("reverse",e,t,n);return[$n.z(A("x",e,t,n),r,a,s)]}case"Bincount":const r=A("x",e,t,n),a=A("weights",e,t,n),s=A("size",e,t,n);return[Dn.y(r,a,s)];case"DenseBincount":{const r=A("x",e,t,n),a=A("weights",e,t,n),s=A("size",e,t,n),o=A("binaryOutput",e,t,n);return[Fn.p(r,a,s,o)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"slice_join":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{const r=A("n",e,t,n),a=A("axis",e,t,n);let s=A("tensors",e,t,n);return s=s.slice(0,r),[On.z(s,a)]}case"Gather":{const r=A("x",e,t,n),a=A("indices",e,t,n);return[Bn.I(r,Xt.p(a,"int32"),0)]}case"GatherV2":{const r=A("axis",e,t,n),a=A("batchDims",e,t,n),s=A("x",e,t,n),o=A("indices",e,t,n);return[Bn.I(s,Xt.p(o,"int32"),r,a)]}case"Reverse":{const r=A("dims",e,t,n),a=[];for(let e=0;e<r.length;e++)r[e]&&a.push(e);const s=A("x",e,t,n);return[Cn.G(s,a)]}case"ReverseV2":{const r=A("axis",e,t,n),a=A("x",e,t,n);return[Cn.G(a,r)]}case"Slice":{const r=A("begin",e,t,n),a=A("size",e,t,n);return[Vn.t(A("x",e,t,n),r,a)]}case"StridedSlice":{const r=A("begin",e,t,n),a=A("end",e,t,n),s=A("strides",e,t,n),o=A("beginMask",e,t,n),i=A("endMask",e,t,n),u=A("ellipsisMask",e,t,n),l=A("newAxisMask",e,t,n),c=A("shrinkAxisMask",e,t,n),p=A("x",e,t,n);return[Rn.N(p,r,a,s,o,i,u,l,c)]}case"Pack":return(0,S.tidy)((()=>{const r=A("axis",e,t,n),a=A("tensors",e,t,n),s=a[0].shape,o=zn.L(a[0]).shape,i=a.map((e=>{const t=S.util.arraysEqual(e.shape,s);if(!t&&!S.util.arraysEqual(zn.L(e).shape,o))throw new Error("the input tensors shape does not match");return t?e:Pn.X(e,s)}));return[Ln.k(i,r)]}));case"Unpack":{const r=A("axis",e,t,n),a=A("tensor",e,t,n);return Wn.H(a,r)}case"Tile":{const r=A("reps",e,t,n);return[Hn.G(A("x",e,t,n),r)]}case"Split":case"SplitV":{const r=A("axis",e,t,n),a=A("numOrSizeSplits",e,t,n),s=A("x",e,t,n);return qn.V(s,a,r)}case"ScatterNd":{const r=A("indices",e,t,n),a=A("values",e,t,n),s=A("shape",e,t,n);return[Gn.s(r,a,s)]}case"GatherNd":{const r=A("x",e,t,n),a=A("indices",e,t,n);return[Un.d(r,a)]}case"SparseToDense":{const r=A("sparseIndices",e,t,n),a=A("outputShape",e,t,n),s=A("sparseValues",e,t,n),o=A("defaultValue",e,t,n);return[xn.e(r,s,a,s.dtype===o.dtype?o:Xt.p(o,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"sparse":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{const{outputIndices:r,outputValues:a,emptyRowIndicator:s,reverseIndexMap:o}=Kt.rVs.sparseFillEmptyRows(A("indices",e,t,n),A("values",e,t,n),A("denseShape",e,t,n),A("defaultValue",e,t,n));return[r,a,s,o]}case"SparseReshape":{const{outputIndices:r,outputShape:a}=Kt.rVs.sparseReshape(A("inputIndices",e,t,n),A("inputShape",e,t,n),A("newShape",e,t,n));return[r,a]}case"SparseSegmentMean":return[Kt.rVs.sparseSegmentMean(A("data",e,t,n),A("indices",e,t,n),A("segmentIds",e,t,n))];case"SparseSegmentSum":return[Kt.rVs.sparseSegmentSum(A("data",e,t,n),A("indices",e,t,n),A("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"spectral":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"FFT":return[jn.k(A("x",e,t,n))];case"IFFT":return[Kn.S(A("x",e,t,n))];case"RFFT":return[Xn.Q(A("x",e,t,n))];case"IRFFT":return[Zn.w(A("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"string":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"StringNGrams":{const{nGrams:r,nGramsSplits:a}=Kt.Z_8.stringNGrams(A("data",e,t,n),A("dataSplits",e,t,n),A("separator",e,t,n),A("nGramWidths",e,t,n),A("leftPad",e,t,n),A("rightPad",e,t,n),A("padWidth",e,t,n),A("preserveShortSequences",e,t,n));return[r,a]}case"StringSplit":{const{indices:r,values:a,shape:s}=Kt.Z_8.stringSplit(A("input",e,t,n),A("delimiter",e,t,n),A("skipEmpty",e,t,n));return[r,a,s]}case"StringToHashBucketFast":return[Kt.Z_8.stringToHashBucketFast(A("input",e,t,n),A("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"transformation":return S.tidy((()=>((e,t,n)=>{switch(e.op){case"Cast":return[Xt.p(A("x",e,t,n),A("dtype",e,t,n))];case"ExpandDims":{const r=A("axis",e,t,n);return[Jn.d(A("x",e,t,n),r)]}case"Squeeze":{const r=A("axis",e,t,n);return[zn.L(A("x",e,t,n),r)]}case"Reshape":return[Pn.X(A("x",e,t,n),A("shape",e,t,n))];case"MirrorPad":return[Qn.V(A("x",e,t,n),A("padding",e,t,n),A("mode",e,t,n))];case"PadV2":case"Pad":return[Yn.v(A("x",e,t,n),A("padding",e,t,n),A("constantValue",e,t,n))];case"SpaceToBatchND":{const r=A("blockShape",e,t,n),a=A("paddings",e,t,n);return[er.f(A("x",e,t,n),r,a)]}case"BatchToSpaceND":{const r=A("blockShape",e,t,n),a=A("crops",e,t,n);return[tr.E(A("x",e,t,n),r,a)]}case"DepthToSpace":{const r=A("blockSize",e,t,n),a=A("dataFormat",e,t,n).toUpperCase();return[nr.n(A("x",e,t,n),r,a)]}case"BroadcastTo":return[rr.U(A("x",e,t,n),A("shape",e,t,n))];case"BroadcastArgs":return[ar.X(A("s0",e,t,n),A("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n)));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{const a=A("keyDType",e,t,n),s=A("valueDType",e,t,n),o=new rn(a,s);return r.addHashTable(e.name,o),[o.handle]}case"LookupTableImport":case"LookupTableImportV2":{const a=A("tableHandle",e,t,n,r),s=A("keys",e,t,n),o=A("values",e,t,n),i=r.getHashTableById(a.id);return[await i.import(s,o)]}case"LookupTableFind":case"LookupTableFindV2":{const a=A("tableHandle",e,t,n,r),s=A("keys",e,t,n),o=A("defaultValue",e,t,n),i=r.getHashTableById(a.id);return[await i.find(s,o)]}case"LookupTableSize":case"LookupTableSizeV2":{const a=A("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,r);case"custom":const a=_(e.op);if(a&&a.customExecutor)return a.customExecutor(new ge(e,t,n));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,t,n);return S.util.isPromise(a)?a.then((e=>[].concat(e))):[].concat(a)}class or{constructor(e={},t={},n={},r={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,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 ir(e,t,n,r){const a=new Set,s=[];let o=null,i=null;const u=new Set,l=Object.keys(e).map((e=>F(e)[0]));let c=[];null!=r&&(c=r.map((e=>F(e.name)[0])));const p=[...t];for(;p.length>0;){const e=p.pop();(pr(e)||dr(e)||hr(e))&&null==o&&(o=e,i=o.children.map((e=>e.name)).filter((e=>a.has(e)))),a.add(e.name),null==n[e.name]&&(-1===l.indexOf(e.name)&&-1===c.indexOf(e.name)&&(0!==e.inputs.length?e.inputs.forEach((e=>{u.has(e.name)||(u.add(e.name),p.push(e))})):s.push(e.name)))}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:o,syncInputs:i}}const ur=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],lr=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],cr=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function pr(e){return ur.indexOf(e.op)>=0}function dr(e){return lr.indexOf(e.op)>=0}function hr(e){return cr.indexOf(e.op)>=0}class mr{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},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 mr(e.functions[t],this)}))}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){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)),{})}getCompilationKey(e,t){const n=e.map((e=>e.name)).sort(),r=t.map((e=>e.name)).sort();return n.join(this.SEPERATOR)+"--"+r.join(this.SEPERATOR)}compile(e,t){const n=ir(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(null!=a)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){const n=t.map((e=>e.name)),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${a}]. Missing the following inputs: [${r}]`)}return function(e,t,n){const{usedNodes:r,inputs:a}=n,s=[],o=Object.keys(a).map((e=>F(e)[0])).map((t=>e.nodes[t])),i=e.initNodes;o.forEach((e=>{r.has(e.name)&&s.push(e)})),e.weights.forEach((e=>{r.has(e.name)&&s.push(e)})),null!=i&&i.forEach((e=>{r.has(e.name)&&s.push(e)}));const u=new Set,l=[];for(;s.length>0;){const e=s.pop();u.add(e.name),t[e.name]||l.push(e),e.children.forEach((e=>{!u.has(e.name)&&r.has(e.name)&&e.inputs.every((e=>u.has(e.name)))&&s.push(e)}))}return l}(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const r=n.map((e=>this.graph.nodes[F(e)[0]])),a=t.map((e=>F(e)[0]));let s=a.map((e=>this.graph.nodes[e]));0===s.length&&(s=this._outputs);const o=this.getCompilationKey(r,s);let i=this.compiledMap.get(o);null==i&&(i=this.compile(e,s),this.compiledMap.set(o,i));const u={},l={};return(0,S.tidy)((()=>{const n=new or(this.weightMap,u,l,this.functionExecutorMap),r=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,a]=F(t),s=[];s[a]=e[t],r[n]=s}));const s=this.getFrozenTensorIds(r),o={};for(let e=0;e<i.length;e++){const t=i[e];if(!r[t.name]){const e=sr(t,r,n,this._resourceManager);if(S.util.isPromise(e))throw new Error(`The execution of the op '${t.op}' returned a promise. Please use model.executeAsync() instead.`);r[t.name]=e,this.checkTensorForDisposal(t.name,t,r,n,s,a,o)}}return null==this.parent&&n.dispose(s),t.map((e=>M(e,r,n)))}))}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,a,s,o){"control"!==t.category&&-1===s.indexOf(e)&&(n[e].forEach((e=>{null!=e&&(o[e.id]=(o[e.id]||0)+t.children.length)})),t.inputs.forEach((e=>{if("control"!==e.category){const t=function(e,t,n){return t[D(e,n.currentContextId)]}(e.name,n,r);null!=t&&t.forEach((e=>{if(e&&!e.kept&&!a.has(e.id)){const t=o[e.id];1===t?(e.dispose(),delete o[e.id]):null!=t&&o[e.id]--}}))}})))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,r={},a={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));const s=new or(this.weightMap,r,a,this.functionExecutorMap),o=await this.executeWithControlFlow(e,s,t,n),i=t.map((e=>M(e,o,s))),u=i.map((e=>e.id)),l=Object.keys(e).map((t=>e[t].id)),c=new Set([...u,...l,...this.weightIds]);return Object.keys(o).forEach((e=>{o[e].forEach((e=>{!e||e.kept||e.isDisposed||c.has(e.id)||e.dispose()}))})),null==this.parent&&s.dispose(c),i}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 a=Object.keys(e),s=a.map((e=>this.graph.nodes[F(e)[0]])),o=n.map((e=>F(e)[0]));let i=o.map((e=>this.graph.nodes[e]));0===i.length&&(i=this._outputs);const{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=ir(e,i,this.weightMap,this._initNodes),d=[...s,...this.graph.weights,...this._initNodes||[]].map((e=>({node:e,contexts:t.currentContext}))),h=Object.assign({},this.weightMap);Object.keys(e).forEach((t=>{const[n,r]=F(t),a=[];a[r]=e[t],h[n]=a}));const m={},f=this.getFrozenTensorIds(h),g={};for(;d.length>0;){const e=this.processStack(s,d,t,h,g,f,o,m,u);await Promise.all(e)}null!=c||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 y=i.filter((e=>!pr(e)&&!M(e.name,h,t))).map((e=>e.name));if(y.length>0){let e="";throw null!=c&&(e=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${l}]. ${e}`)}return h}processStack(e,t,n,r,a,s,o,i,u){const l=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&A("isConstant",e.node,r,n)&&([c]=$(e.node.name,n)),null==r[e.node.name]){const p=sr(e.node,r,n,this._resourceManager);c||([c]=$(e.node.name,n));const d=n.currentContext;S.util.isPromise(p)?l.push(p.then((l=>(r[c]=l,n.currentContext=d,this.checkTensorForDisposal(c,e.node,r,n,s,o,i),this.processChildNodes(e.node,t,n,r,a,u),l)))):(r[c]=p,this.checkTensorForDisposal(c,e.node,r,n,s,o,i),this.processChildNodes(e.node,t,n,r,a,u))}else this.processChildNodes(e.node,t,n,r,a,u)}return l}processChildNodes(e,t,n,r,a,s){e.children.forEach((e=>{const[o]=$(e.name,n);!a[o]&&s.has(e.name)&&("Merge"===e.op?e.inputNames.some((e=>!!M(e,r,n)))&&(a[o]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every((e=>!!M(e,r,n)))&&(a[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((t=>{const n=e[t],[r]=F(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value,t=e.length===n.shape.length&&n.shape.every(((t,n)=>-1===e[n]||e[n]===t));S.util.assert(t,(()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`))}a.attrParams.dtype&&a.attrParams.dtype.value&&S.util.assert(n.dtype===a.attrParams.dtype.value,(()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`))}))}mapInputs(e){const t={};for(const n in e)if(null!=this._signature&&null!=this._signature.inputs&&null!=this._signature.inputs[n]){t[this._signature.inputs[n].name]=e[n]}else t[n]=e[n];return t}checkInputs(e){const t=Object.keys(e).filter((e=>{const[t]=F(e);return null==this.graph.nodes[t]}));if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map((e=>{if(null!=this._signature&&null!=this._signature.outputs&&null!=this._signature.outputs[e]){return this._signature.outputs[e].name}return e}),{})}checkOutputs(e){e.forEach((e=>{const[t]=F(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)}))}}class fr{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}class gr{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",null==t&&(this.loadOptions={}),this.resourceManager=new fr}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=S.io.browserHTTPRequest(e,this.loadOptions);else{const t=S.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(S.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;const t=this.artifacts.modelTopology;let n;n=null!=this.artifacts.userDefinedMetadata&&null!=this.artifacts.userDefinedMetadata.signature?this.artifacts.userDefinedMetadata.signature:this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;const r=S.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new mr(te.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(r),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=te.Instance.transformGraph(e.modelInitializer);this.initializer=new mr(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if("string"==typeof e){const t=S.io.getSaveHandlers(e);if(0===t.length)throw new Error(`Cannot find any save handlers for URL '${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)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof S.Tensor||Array.isArray(e)))return e;if((e=Array.isArray(e)?e:[e]).length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce(((t,n,r)=>(t[n]=e[r],t)),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce(((t,n)=>(t[n]=[e[n]],t)),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}}async function yr(e,t={}){if(null==e)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");null==t&&(t={}),t.fromTFHub&&null==e.load&&(e.endsWith("/")||(e+="/"),e=`${e}model.json?tfjs-format=file`);const n=new gr(e,t);return await n.load(),n}const br="3.9.0"},8713:(e,t,n)=>{"use strict";n.d(t,{JL:()=>r,Zu:()=>a});class r{constructor(e,t){this.backend=e,this.dataMover=t,this.data=new WeakMap,this.dataIdsCount=0}get(e){return this.data.has(e)||this.dataMover.moveData(this.backend,e),this.data.get(e)}set(e,t){this.dataIdsCount++,this.data.set(e,t)}has(e){return this.data.has(e)}delete(e){return this.dataIdsCount--,this.data.delete(e)}numDataIds(){return this.dataIdsCount}}class a{refCount(e){return s("refCount")}incRef(e){return s("incRef")}timerAvailable(){return!0}time(e){return s("time")}read(e){return s("read")}readSync(e){return s("readSync")}numDataIds(){return s("numDataIds")}disposeData(e,t){return s("disposeData")}write(e,t,n){return s("write")}move(e,t,n,r,a){return s("move")}memory(){return s("memory")}floatPrecision(){return s("floatPrecision")}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}dispose(){return s("dispose")}}function s(e){throw new Error(`'${e}' not yet implemented or not found in the registry. 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(null==this.backendInstance){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${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?(f.Z(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(null==this.registryFactory[e])throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,null==this.registry[e]){this.backendInstance=null;const{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new c(this.backendInstance),!0}setupRegisteredKernels(){(0,i.tr)(this.backendName).forEach((e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)}))}disposeRegisteredKernels(e){(0,i.tr)(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 r.Zu||"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,f.Z(`Initialization of backend ${e} failed`),f.Z(n.stack||n.message)),!1)));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}}catch(t){return f.Z(`Initialization of backend ${e} failed`),f.Z(t.stack||t.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(0===Object.keys(this.registryFactory).length)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort(((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority))}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,r=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, 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s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-a-s;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;let o,u;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const c=g(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(g(e)){const{kernelName:t,inputs:a,attrs:s}=e;null==this.backendName&&this.backend;const c=(0,i.pI)(t,this.backendName);l.hu(null!=c,(()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`)),o=()=>{const e=this.backend.numDataIds();u=c.kernelFunc({inputs:a,attrs:s,backend:this.backend});const o=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,o);const i=o.map((e=>{if(null!=e.rank)return e;const{dataId:t,shape:n,dtype:r}=e;return this.makeTensorFromDataId(t,n,r)}));if(r){const e=this.getTensorsForGradient(t,a,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,a=e=>{r&&(n=e.map((e=>this.keep(this.clone(e)))))};o=()=>{const e=this.backend.numDataIds();u=this.tidy((()=>t(this.backend,a)));const n=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,e,n),n}}const{inputs:p,attrs:d}=e,h=g(e)?null:e.backwardsFunc;let m;return this.scopedRun((()=>this.state.kernelDepth++),(()=>this.state.kernelDepth--),(()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(m=this.profiler.profileKernel(c,p,(()=>o())),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(m),t=m.outputs):t=o()})),r&&this.addTapeNode(c,p,t,h,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:c,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(p).map((e=>null!=p[e]?p[e].shape:null)),outputShapes:t.map((e=>e.shape)),kernelTimeMs:m.timeMs,extraInfo:m.extraInfo}),Array.isArray(u)?t:t[0]}saveTensorsForBackwardMode(e){return e.map((e=>this.keep(this.clone(e))))}getTensorsForGradient(e,t,n){const r=(0,i.uk)(e);if(null!=r){const e=r.inputsToSave||[],a=r.outputsToSave||[];let s;r.saveAllInputs?(l.hu(Array.isArray(t),(()=>"saveAllInputs is true, expected inputs to be an 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this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*l.bT(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 h._w||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 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Please use float32 or int32 tensors.`);const o=await n.data(),i="float32"===n.dtype?255:1,u=new Uint8ClampedArray(a*r*4);for(let e=0;e<r*a;++e){const t=[0,0,0,255];for(let r=0;r<s;r++){const a=o[e*s+r];if("float32"===n.dtype){if(a<0||a>1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${a}.`)}else if("int32"===n.dtype&&(a<0||a>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${a}.`);1===s?(t[0]=a*i,t[1]=a*i,t[2]=a*i):t[r]=a*i}const r=4*e;u[r+0]=Math.round(t[0]),u[r+1]=Math.round(t[1]),u[r+2]=Math.round(t[2]),u[r+3]=Math.round(t[3])}if(null!=t){t.width=a,t.height=r;const e=t.getContext("2d"),n=new ImageData(u,a,r);e.putImageData(n,0,0)}return n!==e&&n.dispose(),u}const tt=(0,qe.op)({fromPixels_:Je});function nt(e,t){const n=e.shape.length,r=t.shape.length;if(n<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);if(r<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${r}.`);if("int32"!==t.dtype)throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${t.dtype}.`);if(t.shape[r-1]>n)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${t.shape[r-1]} vs. ${n}`);if(0===(0,v.NA)(e.shape))throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${e.shape}.`);const a=t.shape,s=a[a.length-1];let o=1;for(let e=0;e<a.length-1;++e)o*=a[e];const i=e.shape,u=a.slice();u.pop();let l=1;for(let e=s;e<n;++e)l*=i[e],u.push(i[e]);const c=[...(0,v.e3)(e.shape).map((e=>e/l)),1].slice(0,s);return[u,o,l,c]}var rt=n(3028);function at(e,t,n){const r=e.shape.length;v.hu(r===t.length,(()=>`Error in slice${r}D: Length of begin ${t} must match the rank of the array (${r}).`)),v.hu(r===n.length,(()=>`Error in slice${r}D: Length of size ${n} must match the rank of the array (${r}).`));for(let a=0;a<r;++a)v.hu(t[a]+n[a]<=e.shape[a],(()=>`Error in slice${r}D: begin[${a}] + size[${a}] (${t[a]+n[a]}) would overflow input.shape[${a}] (${e.shape[a]})`))}function st(e){const t=[];let n=0;for(;e>0;)1&e&&t.push(n),e/=2,n++;return t}function ot(e,t,n){const r=[];for(let a=0;a<e.length;a++)r[a]=Math.ceil((t[a]-e[a])/n[a]);return r}function it(e,t,n,r){const a=[...e];for(let e=a.length;e<r.length;e++)a.push(1);for(let e=0;e<n;e++)0===e?a[t]=1:(a.splice(t,0,1),a.pop());return a}function ut(e,t,n){return n<=e?n:n-(t-1)}function lt(e,t){const n=[];for(let r=0;r<e;r++)n.push(t+r);return n}function ct(e,t,n,r,a,s,o,i,u){const l=e.length;let c=new Array(l),p=new Array(l),d=new Array(l);if(t.length&&n>0){const u=t[0],l=n+1;c=pt(o,u,l,r,e),p=dt(i,u,l,a,e),d=it(s,u,l,e)}else for(let t=0;t<l;t++)c[t]=mt(o,r,s,e,t,u),p[t]=ft(i,a,s,e,t,u),d[t]=ht(s,t,u);return{begin:c,end:p,strides:d}}function pt(e,t,n,r,a){const s=[...a],o=lt(n,t);for(let a=0;a<s.length;a++)if(o.indexOf(a)>-1)s[a]=0;else{const o=ut(t,n,a);let i=r[o];e&1<<o&&(i=0),s[a]=i}return s}function dt(e,t,n,r,a){const s=[...a],o=lt(n,t);for(let a=0;a<s.length;a++)if(o.indexOf(a)>-1)s[a]=Number.MAX_SAFE_INTEGER;else{const o=ut(t,n,a);let i=r[o];e&1<<o&&(i=Number.MAX_SAFE_INTEGER),s[a]=i}for(let e=0;e<s.length;e++){const t=a[e];s[e]<0&&(s[e]+=t),s[e]=v.uZ(0,s[e],a[e])}return s}function ht(e,t,n){let r=e[t];return(n&1<<t||null==r)&&(r=1),r}function mt(e,t,n,r,a,s){let o=t[a];const i=n[a]||1;(e&1<<a||s&1<<a||null==o)&&(o=i>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);const u=r[a];return o<0&&(o+=u),o=v.uZ(0,o,u-1),o}function ft(e,t,n,r,a,s){let o=t[a];const i=n[a]||1;(e&1<<a||s&1<<a||null==o)&&(o=i>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);const u=r[a];return o<0&&(o+=u),o=i>0?v.uZ(0,o,u):v.uZ(-1,o,u-1),o}function gt(e,t,n){let r=n.length;for(let e=0;e<n.length;e++)if(n[e]>1){r=e;break}for(let a=r+1;a<n.length;a++)if(t[a]>0||n[a]!==e[a])return!1;return!0}function yt(e,t){let n=e.length>0?e[e.length-1]:1;for(let r=0;r<e.length-1;r++)n+=e[r]*t[r];return n}function bt(e,t,n){let r;const a=e.shape.length;let s;return r="number"==typeof t?[t,...new Array(a-1).fill(0)]:t.length<a?t.concat(new Array(a-t.length).fill(0)):t.slice(),r.forEach((e=>{v.hu(-1!==e,(()=>"slice() does not support negative begin indexing."))})),s=null==n?new Array(a).fill(-1):"number"==typeof n?[n,...new Array(a-1).fill(-1)]:n.length<a?n.concat(new Array(a-n.length).fill(-1)):n,s=s.map(((t,n)=>t>=0?t:(v.hu(-1===t,(()=>`Negative size values should be exactly -1 but got ${t} for the slice() size at index ${n}.`)),e.shape[n]-r[n]))),[r,s]}function kt(e,t,n,r,a,s,o,i,u){let l=t.slice(),c=n.slice(),p=r;null==r&&(p=new Array(l.length));const d=st(o);if(d.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(0!==o&&0!==i)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(0!==o&&0!==u)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");const h=e.length-l.length,m=st(i),f=e.slice();m.forEach((e=>{l[e]=0,c[e]=1,f.splice(e,0,1)}));const{begin:g,end:y,strides:b}=ct(f,d,h,l,c,p,a,s,o);l=g,c=y,p=b;const k=st(u);k.forEach((e=>{c[e]=l[e]+1,p[e]=1}));const w=ot(l,c,p),v=w.filter(((e,t)=>-1===k.indexOf(t)));return{nonStrided:p.every((e=>1===e)),$begin:l,$end:c,$strides:p,size:w,newShape:f,outShape:v}}class wt{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class vt{constructor(){this.classNameMap={}}static getMap(){return null==vt.instance&&(vt.instance=new vt),vt.instance}static register(e){vt.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function xt(e){(0,v.hu)(null!=e.className,(()=>"Class being registered does not have the static className property defined.")),(0,v.hu)("string"==typeof e.className,(()=>"className is required to be a string, but got type "+typeof e.className)),(0,v.hu)(e.className.length>0,(()=>"Class being registered has an empty-string as its className, which is disallowed.")),vt.register(e)}var Nt=n(747),St=n(9122);const It=.1;function Tt(e,t,n){return null==n&&(n=_t()),Et(e,t,((e,t)=>Dt(e,t,n)))}function _t(){return 32===m.BV.backend.floatPrecision()?.001:It}function Et(e,t,n){let r=!0;if(((0,v.fU)(e)||(0,v.fU)(t))&&(r=!1),(0,v.fU)(e)&&(0,v.fU)(t)&&(r=!0),r){const n=e.constructor.name,r=t.constructor.name;if(n!==r)throw new Error(`Arrays are of different type. Actual: ${n}. Expected: ${r}`)}if(Array.isArray(e)&&Array.isArray(t)){const n=(0,Le.C)(e),r=(0,Le.C)(t);if(!(0,v.cO)(n,r))throw new Error(`Arrays have different shapes. Actual: [${n}]. Expected: [${r}]`)}const a=(0,v.fU)(e)?e:(0,v.xH)(e),s=(0,v.fU)(t)?t:(0,v.xH)(t);if(a.length!==s.length)throw new Error(`Arrays have different lengths actual: ${a.length} vs expected: ${s.length}.\nActual:   ${a}.\nExpected: ${s}.`);for(let e=0;e<s.length;++e){const t=a[e],r=s[e];if(!n(t,r))throw new Error(`Arrays differ: actual[${e}] = ${t}, expected[${e}] = ${r}.\nActual:   ${a}.\nExpected: ${s}.`)}}function At(e,t){e().then((()=>t.fail()),(()=>t()))}function Mt(e,t){const n="string"==typeof t||"number"==typeof t||"boolean"==typeof t?[t]:t;return(0,v.HD)(e)||(0,v.HD)(e[0])||(0,v.HD)(t)||(0,v.HD)(t[0])?Et(e,n,((e,t)=>e==t)):Et(e,t,((e,t)=>Dt(e,t,0)))}function $t(e,t,n){if(null==n&&(n=_t()),!Dt(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function Dt(e,t,n){return!isFinite(e)&&!isFinite(t)||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function Ft(e,t,n){for(let r=0;r<e.length;r++)if(e[r]<t||e[r]>n)throw new Error(`Value out of range:${e[r]} low: ${t}, high: ${n}`)}function Ot(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function Bt(e){for(let t=0;t<e.length;t++){const n=e[t];Array.isArray(n)?Bt(n):e[t]=(0,St.encodeString)(n)}return e}const Ct="3.9.0";var Vt=n(4368),Rt=n(6407),zt=n(1274),Pt=n(4841),Lt=n(3261),Wt=n(248),Ht=n(6577),qt=n(633),Gt=n(9494);class Ut extends wt{minimize(e,t=!1,n){const{value:r,grads:a}=this.computeGradients(e,n);if(null!=n){const e=n.map((e=>({name:e.name,tensor:a[e.name]})));this.applyGradients(e)}else this.applyGradients(a);return(0,Vt.B9)(a),t?r:(r.dispose(),null)}get iterations(){return null==this.iterations_&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return(0,qt.pn)(e,t)}dispose(){null!=this.iterations_&&(0,Vt.B9)(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:(0,Gt.i)(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(Ut,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class jt extends Ut{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=m.BV.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=m.BV.registeredVariables[t];null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:(0,Vt.lu)((()=>(0,Ht.P)(r).variable(false)))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:(0,Vt.lu)((()=>(0,Ht.P)(r).variable(false)))});const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const s=this.accumulatedGrads[n].variable,o=this.accumulatedUpdates[n].variable;(0,Vt.lu)((()=>{const e=(0,Rt.I)((0,Pt.d)(s,this.rho),(0,Pt.d)((0,Wt.h)(a),1-this.rho)),t=(0,Pt.d)((0,zt.h)((0,Lt._)((0,Rt.I)(o,this.epsilon)),(0,Lt._)((0,Rt.I)(s,this.epsilon))),a),n=(0,Rt.I)((0,Pt.d)(o,this.rho),(0,Pt.d)((0,Wt.h)(t),1-this.rho));s.assign(e),o.assign(n);const i=(0,Rt.I)((0,Pt.d)(t,-this.learningRate),r);r.assign(i)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&((0,Vt.B9)(this.accumulatedGrads.map((e=>e.variable))),(0,Vt.B9)(this.accumulatedUpdates.map((e=>e.variable))))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2;this.accumulatedGrads=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedUpdates=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}jt.className="Adadelta",xt(jt);var Kt=n(4006);class Xt extends Ut{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=m.BV.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:(0,Vt.lu)((()=>(0,Kt.h)(r.shape,this.initialAccumulatorValue).variable(e)))}}const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const s=this.accumulatedGrads[n].variable;(0,Vt.lu)((()=>{const e=(0,Rt.I)(s,(0,Wt.h)(a));s.assign(e);const t=(0,Rt.I)((0,Pt.d)((0,zt.h)(a,(0,Lt._)((0,Rt.I)(e,m.BV.backend.epsilon()))),-this.learningRate),r);r.assign(t)}))})),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&(0,Vt.B9)(this.accumulatedGrads.map((e=>e.variable)))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);this.accumulatedGrads=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}Xt.className="Adagrad",xt(Xt);var Zt=n(3453),Jt=n(827);class Qt extends Ut{constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],(0,Vt.lu)((()=>{this.accBeta1=(0,Gt.i)(t).variable(),this.accBeta2=(0,Gt.i)(n).variable()})),null==r&&(this.epsilon=m.BV.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);(0,Vt.lu)((()=>{const n=(0,Jt.l)(1,this.accBeta1),r=(0,Jt.l)(1,this.accBeta2);t.forEach(((t,a)=>{const s=m.BV.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:(0,Vt.lu)((()=>(0,Ht.P)(s).variable(false)))}),null==this.accumulatedSecondMoment[a]&&(this.accumulatedSecondMoment[a]={originalName:`${t}/v`,variable:(0,Vt.lu)((()=>(0,Ht.P)(s).variable(false)))});const o=Array.isArray(e)?e[a].tensor:e[t];if(null==o)return;const i=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,l=(0,Rt.I)((0,Pt.d)(i,this.beta1),(0,Pt.d)(o,1-this.beta1)),c=(0,Rt.I)((0,Pt.d)(u,this.beta2),(0,Pt.d)((0,Wt.h)(o),1-this.beta2)),p=(0,zt.h)(l,n),d=(0,zt.h)(c,r);i.assign(l),u.assign(c);const h=(0,Rt.I)((0,Pt.d)((0,zt.h)(p,(0,Rt.I)((0,Lt._)(d),this.epsilon)),-this.learningRate),s);s.assign(h)})),this.accBeta1.assign((0,Pt.d)(this.accBeta1,this.beta1)),this.accBeta2.assign((0,Pt.d)(this.accBeta2,this.beta2))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&(0,Vt.B9)(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedSecondMoment&&(0,Vt.B9)(this.accumulatedSecondMoment.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e),(0,Vt.lu)((()=>{this.accBeta1.assign((0,Zt.s)(this.beta1,this.iterations_+1)),this.accBeta2.assign((0,Zt.s)(this.beta2,this.iterations_+1))}));const t=e.length/2;this.accumulatedFirstMoment=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)}))),this.accumulatedSecondMoment=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}Qt.className="Adam",xt(Qt);var Yt=n(6235),en=n(632);class tn extends Ut{constructor(e,t,n,r=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],(0,Vt.lu)((()=>{this.iteration=(0,Gt.i)(0).variable(),this.accBeta1=(0,Gt.i)(t).variable()})),null==r&&(this.epsilon=m.BV.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map((e=>e.name)):Object.keys(e);(0,Vt.lu)((()=>{const n=(0,Jt.l)(1,this.accBeta1),r=(0,zt.h)(-this.learningRate,(0,Rt.I)((0,Pt.d)(this.iteration,this.decay),1));t.forEach(((t,a)=>{const s=m.BV.registeredVariables[t];null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:(0,Ht.P)(s).variable(false)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:(0,Ht.P)(s).variable(false)});const o=Array.isArray(e)?e[a].tensor:e[t];if(null==o)return;const i=this.accumulatedFirstMoment[a].variable,u=this.accumulatedWeightedInfNorm[a].variable,l=(0,Rt.I)((0,Pt.d)(i,this.beta1),(0,Pt.d)(o,1-this.beta1)),c=(0,Pt.d)(u,this.beta2),p=(0,Yt.W)(o),d=(0,en.g)(c,p);i.assign(l),u.assign(d);const h=(0,Rt.I)((0,Pt.d)((0,zt.h)(r,n),(0,zt.h)(l,(0,Rt.I)(d,this.epsilon))),s);s.assign(h)})),this.iteration.assign((0,Rt.I)(this.iteration,1)),this.accBeta1.assign((0,Pt.d)(this.accBeta1,this.beta1))})),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&(0,Vt.B9)(this.accumulatedFirstMoment.map((e=>e.variable))),null!=this.accumulatedWeightedInfNorm&&(0,Vt.B9)(this.accumulatedWeightedInfNorm.map((e=>e.variable)))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}tn.className="Adamax",xt(tn);class nn extends Ut{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const a=m.BV.registeredVariables[t];(0,Vt.lu)((()=>{const e=(0,Rt.I)((0,Pt.d)(this.c,r),a);a.assign(e)}))})),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=(0,Vt.Cn)((0,Gt.i)(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).length)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}nn.className="SGD",xt(nn);class rn extends nn{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=(0,Gt.i)(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=m.BV.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:(0,Vt.lu)((()=>(0,Ht.P)(r).variable(e)))}}const a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];null!=s&&(0,Vt.lu)((()=>{let e;const t=(0,Rt.I)((0,Pt.d)(this.m,a),s);e=this.useNesterov?(0,Rt.I)((0,Pt.d)(this.c,(0,Rt.I)(s,(0,Pt.d)(t,this.m))),r):(0,Rt.I)((0,Pt.d)(this.c,t),r),a.assign(t),r.assign(e)}))})),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&(0,Vt.B9)(this.accumulations.map((e=>e.variable)))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);this.accumulations=e.map((e=>({originalName:e.name,variable:e.tensor.variable(false)})))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}rn.className="Momentum",xt(rn);class an extends Ut{constructor(e,t=.9,n=0,r=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==r&&(this.epsilon=m.BV.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map((e=>e.name)):Object.keys(e)).forEach(((t,n)=>{const r=m.BV.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:(0,Vt.lu)((()=>(0,Ht.P)(r).variable(a)))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:(0,Vt.lu)((()=>(0,Ht.P)(r).variable(a)))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:(0,Vt.lu)((()=>(0,Ht.P)(r).variable(a)))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedMeanSquares[n].variable,i=this.accumulatedMoments[n].variable;(0,Vt.lu)((()=>{const e=(0,Rt.I)((0,Pt.d)(o,this.decay),(0,Pt.d)((0,Wt.h)(s),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,a=(0,Rt.I)((0,Pt.d)(t,this.decay),(0,Pt.d)(s,1-this.decay)),u=(0,zt.h)((0,Pt.d)(s,this.learningRate),(0,Lt._)((0,Jt.l)(e,(0,Rt.I)((0,Wt.h)(a),this.epsilon)))),l=(0,Rt.I)((0,Pt.d)(i,this.momentum),u);o.assign(e),t.assign(a),i.assign(l);const c=(0,Jt.l)(r,l);r.assign(c)}else{const e=(0,Rt.I)((0,Pt.d)(o,this.decay),(0,Pt.d)((0,Wt.h)(s),1-this.decay)),t=(0,Rt.I)((0,Pt.d)(i,this.momentum),(0,zt.h)((0,Pt.d)(s,this.learningRate),(0,Lt._)((0,Rt.I)(e,this.epsilon))));o.assign(e),i.assign(t);const n=(0,Jt.l)(r,t);r.assign(n)}}))})),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&(0,Vt.B9)(this.accumulatedMeanSquares.map((e=>e.variable))),null!=this.accumulatedMeanGrads&&this.centered&&(0,Vt.B9)(this.accumulatedMeanGrads.map((e=>e.variable))),null!=this.accumulatedMoments&&(0,Vt.B9)(this.accumulatedMoments.map((e=>e.variable)))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map((e=>({name:e.originalName,tensor:e.variable}))))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.accumulatedMoments=e.slice(t,2*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).map((e=>({originalName:e.name,variable:e.tensor.variable(n)}))))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}an.className="RMSProp",xt(an);var sn=n(1221),on=n(4885),un=n(9876);class ln{static sgd(e){return new nn(e)}static momentum(e,t,n=!1){return new rn(e,t,n)}static rmsprop(e,t=.9,n=0,r=null,a=!1){return new an(e,t,n,r,a)}static adam(e=.001,t=.9,n=.999,r=null){return new Qt(e,t,n,r)}static adadelta(e=.001,t=.95,n=null){return new jt(e,t,n)}static adamax(e=.002,t=.9,n=.999,r=null,a=0){return new tn(e,t,n,r,a)}static adagrad(e,t=.1){return new Xt(e,t)}}const cn={sgd:ln.sgd,momentum:ln.momentum,adadelta:ln.adadelta,adagrad:ln.adagrad,rmsprop:ln.rmsprop,adamax:ln.adamax,adam:ln.adam},pn="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:"undefined"!=typeof setImmediate?setImmediate:e=>e();function dn(){return new Promise((e=>pn((()=>e()))))}var hn=n(3591),mn=n(2200);function fn(e,t){const n=e[0].length;e.forEach(((e,t)=>{v.hu(e.length===n,(()=>`Error in concat${n}D: rank of tensors[${t}] must be the same as the rank of the rest (${n})`))})),v.hu(t>=0&&t<n,(()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`));const r=e[0];e.forEach(((e,a)=>{for(let s=0;s<n;s++)v.hu(s===t||e[s]===r[s],(()=>`Error in concat${n}D: Shape of tensors[${a}] (${e}) does not match the shape of the rest (${r}) along the non-concatenated axis ${a}.`))}))}function gn(e,t){const n=e[0].slice();for(let r=1;r<e.length;r++)n[t]+=e[r][t];return n}var yn=n(2582),bn=n(9323);const kn=30;function wn(e){return e<=kn?e:(0,v.jP)(e,Math.floor(Math.sqrt(e)))}function vn(e,t,n){return[n*("number"==typeof e?e:e[0]),t*("number"==typeof e?e:e[1])]}function xn(e,t,n,r=!0){let a=[];if(r)a=a.concat(t.slice(0)),a.push(e[0]/n),a=a.concat(e.slice(1));else{a=a.concat(e[0]);const n=t.length;for(let r=0;r<n;++r)a=a.concat([e[r+1]/t[r],t[r]]);a=a.concat(e.slice(n+1))}return a}function Nn(e,t,n=!0){const r=[];if(n){r.push(t);for(let n=t+1;n<e;++n)n<=2*t?(r.push(n),r.push(n-(t+1))):r.push(n)}else{const n=[],a=[];for(let r=1;r<e;++r)r>=2*t+1||r%2==1?a.push(r):n.push(r);r.push(...n),r.push(0),r.push(...a)}return r}function Sn(e,t,n,r=!0){const a=[];r?a.push(e[0]/n):a.push(e[0]*n);for(let 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