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============================
Hithere\u{1F44B}.LookslikeyouarerunningTensorFlow.jsinNode.js.Tospeedthingsupdramatically,installournodebackend,whichbindstoTensorFlowC++,byrunningnpmi@tensorflow/tfjs-node,ornpmi@tensorflow/tfjs-node-gpuifyouhaveCUDA.Thencallrequire('@tensorflow/tfjs-node');(-gpusuffixforCUDA)atthestartofyourprogram.Visithttps://github.com/tensorflow/tfjs-node for more details.
============================`));let o={id:this.nextDataId()};return this.data.set(o,{values:e,dtype:n,refCount:1}),o}makeTensorInfo(e,t,n){let o;if(t==="string"&&n!=null&&n.length>0&&y.isString(n[0])){let s=n.map(a=>y.encodeString(a));o=this.write(s,e,t)}else o=this.write(n,e,t);return{dataId:o,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,o,s){this.data.set(e,{values:t,dtype:o,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let o=this.readSync(n.real.dataId),s=this.readSync(n.imag.dataId);return I.mergeRealAndImagArrays(o,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(o=>y.decodeString(o))}catch(o){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ie(e.shape,e.dtype,n)}makeOutput(e,t,n){let o=this.write(e,t,n);return Ns().makeTensorFromDataId(o,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=y.now();return e(),{kernelMs:y.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){te([e],"where");let t=this.readSync(e.dataId);return y5(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Hu.nextDataId=0;var Nx={};je(Nx,{addImpl:()=>gk,bincountImpl:()=>Up,bincountReduceImpl:()=>fx,ceilImpl:()=>xk,concatImpl:()=>Ku,equalImpl:()=>yk,expImpl:()=>wk,expm1Impl:()=>kk,floorImpl:()=>vk,gatherNdImpl:()=>dx,gatherV2Impl:()=>hx,greaterEqualImpl:()=>Ik,greaterImpl:()=>Ck,lessEqualImpl:()=>Nk,lessImpl:()=>Sk,linSpaceImpl:()=>gx,logImpl:()=>Tk,maxImpl:()=>xx,maximumImpl:()=>Ek,minimumImpl:()=>Ak,multiplyImpl:()=>Zf,negImpl:()=>Dk,notEqualImpl:()=>$k,prodImpl:()=>Rk,rangeImpl:()=>Yu,rsqrtImpl:()=>Fk,simpleAbsImpl:()=>hk,sliceImpl:()=>Zu,sparseFillEmptyRowsImpl:()=>yx,sparseReshapeImpl:()=>bx,sparseSegmentReductionImpl:()=>Hp,squaredDifferenceImpl:()=>Ok,stridedSliceImpl:()=>wx,stringNGramsImpl:()=>_x,stringSplitImpl:()=>kx,stringToHashBucketFastImpl:()=>vx,subImpl:()=>Pk,tileImpl:()=>Cx,topKImpl:()=>Ix,transposeImpl:()=>qp,uniqueImpl:()=>Sx});function hk(r){let e=new Float32Array(r.length);for(let t=0;t<r.length;++t)e[t]=Math.abs(r[t]);return e}var b5=r=>{let{x:e}=r.inputs,t=r.backend;te(e,"abs");let n=new Float32Array(y.sizeFromShape(e.shape)),o=t.data.get(e.dataId).values;return n=hk(o),t.makeOutput(n,e.shape,"float32")},eA={kernelName:Js,backendName:"cpu",kernelFunc:b5};function Ze(r){return(e,t,n,o,s)=>{let a=I.assertAndGetBroadcastShape(e,t),i=a.length,l=y.computeStrides(a),u=y.sizeFromShape(a),c=y.getTypedArrayFromDType(s,u),p=e.length,m=t.length,f=y.computeStrides(e),d=y.computeStrides(t),h=I.getBroadcastDims(e,a),g=I.getBroadcastDims(t,a);if(h.length+g.length===0)for(let x=0;x<c.length;++x)c[x]=r(n[x%n.length],o[x%o.length]);else for(let x=0;x<c.length;++x){let b=y.indexToLoc(x,i,l),w=b.slice(-p);h.forEach(D=>w[D]=0);let _=y.locToIndex(w,p,f),C=b.slice(-m);g.forEach(D=>C[D]=0);let A=y.locToIndex(C,m,d);c[x]=r(n[_],o[A])}return[c,a]}}function xr(r){let{inputs:e,backend:t}=r,{real:n,imag:o}=e,s=t.data.get(n.dataId).values,a=t.data.get(o.dataId).values,i=t.makeTensorInfo(n.shape,"complex64"),l=t.data.get(i.dataId);return l.complexTensorInfos={real:t.makeTensorInfo(n.shape,"float32",s),imag:t.makeTensorInfo(o.shape,"float32",a)},i}var tA={kernelName:Bc,backendName:"cpu",kernelFunc:xr};function Wp(r,e,t="float32"){if(t==="complex64"){let o=Wp(r,e,"float32"),s=Wp(r,e,"float32");return xr({inputs:{real:o,imag:s
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${s.shape}`);if(n.shape.length!==2)throw new Error(`Indicesmustbeamatrix,saw:
${n.shape}`);if(o.shape.length!==1)throw new Error(`Valuesmustbeavector,saw:
${o.shape}`);if(a.shape.length!==0)throw new Error(`Defaultvaluemustbeascalar,saw:
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${n.shape}`);if(o.shape.length!==1)throw new Error(`Inputshapeshouldbeavectorbutreceivedshape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Targetshapeshouldbeavectorbutreceivedshape${s.shape}`);let a=Array.from(t.data.get(o.dataId).values),i=t.data.get(n.dataId).values,l=Array.from(t.data.get(s.dataId).values),[u,c,p]=bx(i,n.shape,n.dtype,a,l);return[t.makeTensorInfo(c,n.dtype,u),t.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var LD={kernelName:up,backendName:"cpu",kernelFunc:WX};function jX(r){let{inputs:e,backend:t}=r,{data:n,indices:o,segmentIds:s}=e;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indicesshouldbeavectorbutreceivedshape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segmentidsshouldbeavectorbutreceivedshape
${s.shape}`);let a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,[u,c]=Hp(a,n.shape,n.dtype,i,l,!0);return t.makeTensorInfo(c,n.dtype,u)}var zD={kernelName:cp,backendName:"cpu",kernelFunc:jX};function UX(r){let{inputs:e,backend:t}=r,{data:n,indices:o,segmentIds:s}=e;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(o.shape.length!==1)throw new Error(`Indicesshouldbeavectorbutreceivedshape
${o.shape}`);if(s.shape.length!==1)throw new Error(`Segmentidsshouldbeavectorbutreceivedshape
${s.shape}`);let a=t.data.get(n.dataId).values,i=t.data.get(o.dataId).values,l=t.data.get(s.dataId).values,[u,c]=Hp(a,n.shape,n.dtype,i,l);return t.makeTensorInfo(c,n.dtype,u)}var BD={kernelName:pp,backendName:"cpu",kernelFunc:UX};function qX(r){let{inputs:e,backend:t,attrs:n}=r,{sparseIndices:o,sparseValues:s,defaultValue:a}=e,{outputShape:i}=n,{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:m}=I.calculateShapes(s,o,i),f=!1,d=t.bufferSync(o),h=t.bufferSync(s),g=t.data.get(a.dataId).values[0],x=$x(d,h,i,m,c,u,l,p,g,f);return t.makeTensorInfo(i,x.dtype,x.values)}var VD={kernelName:mp,backendName:"cpu",kernelFunc:qX};function HX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{numOrSizeSplits:s,axis:a}=n,i=y.parseAxisParam(a,o.shape)[0],l=I.prepareSplitSize(o,s,i),u=new Array(o.shape.length).fill(0),c=o.shape.slice();return l.map(p=>{let m=[...c];m[i]=p;let f=_o({inputs:{x:o},backend:t,attrs:{begin:u,size:m}});return u[i]+=p,f})}var GD={kernelName:li,backendName:"cpu",kernelFunc:HX};var KX=Re(gs,r=>Math.sqrt(r)),WD={kernelName:gs,backendName:"cpu",kernelFunc:KX};var jD={kernelName:bl,backendName:"cpu",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,n=e;te(t,"square");let o=n.data.get(t.dataId).values,s=new Float32Array(o.length);for(let i=0;i<o.length;++i){let l=o[i];s[i]=l*l}return{dataId:n.write(s,t.shape,t.dtype),shape:t.shape,dtype:t.dtype}}};var XX=Re(oo,(r,e)=>{let t=e;return isNaN(r)?NaN:r>0?1:t.alpha}),UD={kernelName:oo,backendName:"cpu",kernelFunc:XX};function YX(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=n;te(o,"stridedSlice");let{nonStrided:f,$begin:d,$strides:h,size:g,newShape:x,outShape:b}=lr.sliceInfo(o.shape,s,a,i,l,u,c,p,m),w=Je({inputs:{x:o},backend:t,attrs:{shape:x}}),_;if(f){let A=_o({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=Je({inputs:{x:A},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo(A)}else if(b.some(A=>A===0))_=t.makeTensorInfo(b,o.dtype,[]);else{let A=t.bufferSync(w),D=wx(b,A,h,d);_=t.makeTensorInfo(D.shape,D.dtype,D.values)}let C=Je({inputs:{x:_},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),C}var qD={kernelName:xa,backendName:"cpu",kernelFunc:YX};function ZX(r){let{inputs:e,backend:t,attrs:n}=r,{separator:o,nGramWidths:s,leftPad:a,rightPad:i,padWidth:l,preserveShortSequences:u}=n,{data:c,dataSplits:p}=e,m=t.data.get(c.dataId).values,f=t.data.get(p.dataId).values,[d,h]=_x(m,f,o,s,a,i,l,u);return[t.makeTensorInfo([d.length],"string",d),t.makeTensorInfo(p.shape,"int32",h)]}var HD={kernelName:fp,backendName:"cpu",kernelFunc:ZX};function JX(r){let{inputs:e,backend:t,attrs:n}=r,{skipEmpty:o}=n,{input:s,delimiter:a}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Inputmustbeavector,gotshape:${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimitermustbeascalar,gotshape:${a.shape}`);let i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values[0],[u,c,p]=kx(i,l,o),m=c.length;return[t.makeTensorInfo([m,2],"int32",u),t.makeTensorInfo([m],"string",c),t.makeTensorInfo([2],"int32",new Int32Array(p))]}var KD={kernelName:dp,backendName:"cpu",kernelFunc:JX};function QX(r){let{inputs:e,backend:t,attrs:n}=r,{numBuckets:o}=n,{input:s}=e;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(o<=0)throw new Error("Number of buckets must be at least 1");let a=t.data.get(s.dataId).values,i=vx(a,o);return t.makeTensorInfo(s.shape,"int32",i)}var XD={kernelName:hp,backendName:"cpu",kernelFunc:QX};var e7=Re(_s,r=>Math.tan(r)),YD={kernelName:_s,backendName:"cpu",kernelFunc:e7};var t7=Re(ks,r=>Math.tanh(r)),ZD={kernelName:ks,backendName:"cpu",kernelFunc:t7};function r7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{reps:s}=n;te(o,"tile");let a=Cx(t.bufferSync(o),s);return t.makeTensorInfo(a.shape,a.dtype,a.values)}var JD={kernelName:Un,backendName:"cpu",kernelFunc:r7};function n7(r){let{inputs:e,backend:t,attrs:n}=r,{x:o}=e,{k:
// Explicitly use integer operations as dot() only works on floats.
intindex=row*${o}+col*${s}+depth+${f};
vec2uv=uvFromFlat(${c},${p},index);
returnsampleTexture(${t},uv);
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