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i=d(a.backend_util.computeOutAndReduceShapes(e,n),2),o=i[0],s=i[1],u=a.upcastType(t,"int32"),l=a.util.makeZerosTypedArray(a.util.sizeFromShape(o),u),p=a.util.sizeFromShape(s),c=0;c<l.length;++c){for(var h=c*p,f=1,m=0;m<p;++m)f*=r[h+m];l[c]=f}return{outVals:l,outShape:o,outDtype:u}}var qe={kernelName:a.Prod,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis,s=n.keepDims;p(i,"prod");var u=i.shape.length,d=a.util.parseAxisParam(o,i.shape),l=a.backend_util.getAxesPermutation(d,u),c=d,h=i,f=[];null!=l&&(h=Ve({inputs:{x:i},backend:r,attrs:{perm:l}}),f.push(h),c=a.backend_util.getInnerMostAxes(c.length,u));var m=r.data.get(h.dataId).values,v=Le(h.shape,h.dtype,m,c),k=v.outVals,g=v.outShape,b=v.outDtype,I=g;return s&&(I=a.backend_util.expandShapeToKeepDim(g,d)),f.forEach((function(e){return r.disposeIntermediateTensorInfo(e)})),r.makeTensorInfo(I,b,k)}};function Ue(e,a,t,r){var n=[],i=0,o=a.length-1+t.length,s=new Array(o).fill(null).map((function(){return[0]}));!function(e,a){for(var t=0;t<e.length;++t){var r=e[t],n=t===e.length-1?a:e[t+1].length;if(0===r.length)throw new Error("Ragged splits may not be empty");if(r[0]<0)throw new Error("Ragged splits must be non-negative");if(r[r.length-1]>n)throw new Error("Ragged splits must not point past values");for(var i=1;i<r.length;++i)if(r[i-1]>r[i])throw new Error("Ragged splits must be sorted in ascending order")}}(t,r);for(var u=1,d=0;d<a.length-1;++d){u*=a[d];for(var l=a[d+1],p=1;p<u+1;++p)s[d].push(p*l)}for(p=0;p<e.length;++p){var c=e[p],h=e[p]+1;for(d=0;d<t.length;++d){var f=t[d],m=d+a.length-1;if(m>=0)for(var v=s[m],k=v[v.length-1]-f[c],g=c;g<h;++g)s[m].push(f[g+1]+k);c=f[c],h=f[h]}h!==c&&(n.push([c,h]),i+=h-c)}return{outSplits:s,valueSlices:n,numValues:i}}function Ze(e,a){for(var t=e.slice(0,a);t.length<a;)t.push(1);for(var r=a;r<e.length;r++)t[a-1]*=e[r];return t}function je(e,t,r,n,i){var o=t.slice();o[0]=i;var s=a.util.getArrayFromDType(r,a.util.sizeFromShape(o)),d=e.length;return function(e,a,t,r,n,i){var o,s,d=Ze(a,2)[1],l=Ze(i,2)[1],p=0;try{for(var c=u(t),h=c.next();!h.done;h=c.next())for(var f=h.value,m=f[0];m<f[1];++m){for(var v=0;v<r;++v)n[p*l+v]=e[m*d+v];++p}}catch(e){o={error:e}}finally{try{h&&!h.done&&(s=c.return)&&s.call(c)}finally{if(o)throw o.error}}}(e,t,n,0===d?0:d/t[0],s,o),[s,o]}function Ke(e,t,r,n,i,o,s,u){if(0===e.length)throw new Error("paramsNestedSplits must be non empty");if(0===t[0].length)throw new Error("Split tensors must not be scalars");if(function(e,t,r){e.forEach((function(e,n){if(e<0||e>=r){var i=a.util.indexToLoc(n,t.length,a.util.computeStrides(t)).join(",");throw new Error("indices[".concat(i,"] = ").concat(e," is not in [0, ").concat(r,")"))}}))}(o,s,t[0][0]-1),0===n.length)throw new Error("params.rank must be nonzero");var d=Ue(o,s,e,n[0]),l=d.outSplits,p=d.valueSlices,c=d.numValues,h=function(e){for(var t=[],r=function(r){var n=e[r].length,i=a.util.getArrayFromDType("int32",n);t.push(i),e[r].forEach((function(e,a){return i[a]=e}))},n=0;n<e.length;++n)r(n);return t}(l),f=je(r,n,i,p,c);return[h,f[0],f[1]]}var Ye=2147483647;function Je(e,t,r,n,i,o,s){if(t.length>1)throw new Error("starts must be a scalar or vector");if(i.length>1)throw new Error("limits must be a scalar or vector");if(s.length>1)throw new Error("deltas must be a scalar or vector");var u=0===t.length,d=0===i.length,l=0===s.length,p=[];u||p.push(t[0]),d||p.push(i[0]),l||p.push(s[0]);for(var c=1;c<p.length;++c)if(p[c]!==p[c-1])throw new Error("starts, limits, and deltas must have the same shape");var h=0===p.length?1:p[0],f=a.util.getArrayFromDType("int32",h+1);f[0]=0;for(var m=0;m<h;++m){var v=u?e[0]:e[m],k=d?n[0]:n[m];if(0===(N=l?o[0]:o[m]))throw new Error("Requires delta != 0");var g=void 0;if(N>0&&k<v||N<0&&k>v)g=0;else if((g=Math.ceil(Math.abs((k-v)/N)))>Ye)throw new Error("Requires ((limit - start) / delta) <= 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t=this.getRowPartitionTensor(a-1);switch(this.getRowPartitionTypeByDimension(a-1)){case Qe.VALUE_ROWIDS:return e.getMaxWidthValueRowID(t);case Qe.ROW_SPLITS:return e.getMaxWidthRowSplit(t);default:throw new Error("Cannot handle partition type ".concat(Qe[this.getRowPartitionTypeByDimension(a-1)]))}},e.getMaxWidthRowSplit=function(e){var a=e.length;if(0===a||1===a)return 0;for(var t=0,r=0;r<a-1;++r){var n=e[r+1]-e[r];n>t&&(t=n)}return t},e.getMaxWidthValueRowID=function(e){var a=e.length;if(0===a)return 0;for(var t=0,r=e[0],n=0,i=1;i<a;++i){var o=e[i];o!==r&&(r=o,n=Math.max(i-t,n),t=i)}return Math.max(a-t,n)},e.prototype.tensorShapeFromTensor=function(e,a,t){if(void 0===t&&(t=!0),0===a.length){if(-1===e[0])return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return ea(e,t)},e.prototype.calculateOutputSize=function(e){var t=this.valuesShape,r=this.defaultValueShape;a.backend_util.validateDefaultValueShape(r,t);var n=this.tensorShapeFromTensor(this.shape,this.shapeShape),i=a.backend_util.combineRaggedTensorToTensorShapes(this.raggedRank,n,t);i[0]<0&&(i[0]=e);for(var o=1;o<=this.raggedRank;++o)i[o]<0&&(i[o]=this.getMaxWidth(o));return i},e.prototype.calculateFirstParentOutputIndex=function(e,t,r){for(var n=Math.min(e,r),i=[],o=0,s=0;s<n;++s,o+=t)i.push(o);for(s=n;s<e;++s)i.push(-1);return a.util.assert(i.length===e,(function(){return"Final length of result must be equal to firstDimension."})),i},e.prototype.calculateOutputIndexRowSplit=function(e,a,t,r){for(var n=e.length,i=[],o=0;o<n-1;++o){var s=e[o+1]-e[o],u=Math.min(r,s),d=a[o];-1===d&&(u=0);for(var l=0;l<u;++l)i.push(d),d+=t;for(l=0;l<s-u;++l)i.push(-1)}if(n>0&&i.length!==e[n-1])throw new Error("Invalid row split size.");return i},e.prototype.calculateOutputIndexValueRowID=function(e,a,t,r){var n=e.length,i=[];if(0===n)return[];var o=0,s=e[0];if(s>=a.length)throw new Error("Got currentValueRowId=".concat(s,", which is not less than ").concat(a.length));var u=a[s];i.push(u);for(var d=1;d<n;++d){var l=e[d];if(l===s)u>=0&&(++o<r?u+=t:u=-1);else{if(o=0,s=l,l>=a.length)throw new Error("Got nextValueRowId=".concat(l," which is not less than ").concat(a.length));u=a[l]}i.push(u)}if(i.length!==e.length)throw new Error("Invalid row ids.");return i},e.prototype.calculateOutputIndex=function(e,a,t,r){var n=this.getRowPartitionTensor(e),i=this.getRowPartitionTypeByDimension(e);switch(i){case Qe.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(n,a,t,r);case Qe.ROW_SPLITS:if(n.length-1>a.length)throw new Error("Row partition size is greater than output size: ".concat(n.length-1," > ").concat(a.length));return this.calculateOutputIndexRowSplit(n,a,t,r);default:throw new Error("Unsupported partition type: ".concat(Qe[i]))}},e.prototype.getFirstDimensionSize=function(){var e=this.rowPartitionValues[0];if(0===this.rowPartitionTypes.length)throw new Error("No row_partition_types given.");var a=this.rowPartitionTypes[0];switch(a){case Qe.FIRST_DIM_SIZE:return e[0];case Qe.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case Qe.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error("Cannot handle type ".concat(Qe[a]))}},e.prototype.compute=function(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");var e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),r=new Array(this.raggedRank+1);r[r.length-1]=1;for(var n=r.length-2;n>=0;--n)r[n]=r[n+1]*t[n+1];var i=ea(t,!1),o=a.util.getArrayFromDType(this.valuesDType,a.util.sizeFromShape(i));if(r[0]*t[0]>0){var s=this.calculateFirstParentOutputIndex(e,r[0],t[0]);for(n=1;n<=this.raggedRank;++n){s=this.calculateOutputIndex(n-1,s,r[n],t[n])}this.setOutput(this.raggedRank,s,o,i)}return[i,o]},e.prototype.setOutput=function(e,t,r,n){if(0!==r.length){var i=this.values,o=r,s=n.slice();s=s.slice(e+1);var u=a.util.sizeFromShape(s),d=t.length,l=this.defaultValue;if(l.length!==u&&1!==l.length){var p=this.defaultValueShape;a.tidy((function(){var e=a.reshape(l,p),t=a.broadcastTo(e,s);l=t.dataSync()}))}for(var c=0,h=0,f=0,m=0;m<=d;++m){var v=m<d?t[m]:-1;if(v!==f){if(h<f){var k=i.subarray(c*u);$e(o.subarray(h*u),k,(f-h)*u)}if(m>=d){var g=r.length;v=Math.floor(g/u)}if(v>f)if(1===this.defaultValue.length)o.subarray(f*u,v*u).fill(this.defaultValue[0]),f=v;else for(;v>f;){$e(o.slice(f*u),l,u),++f}v<0?(c=m+1,h=f):(c=m,f=(h=f)+1)}else++f}}},e}();function $e(e,a,t){for(var r=0;r<t;r++)e[r]=a[r]}function ea(e,a){var t,r,n=[];try{for(var i=u(e),o=i.next();!o.done;o=i.next()){var s=o.value;if(s<0){if(!a)throw new Error("Dimension ".concat(s," must be >= 0"));if(s<-1)throw new Error("Dimension ".concat(s," must be >= -1"));s=-1}n.push(s)}}catch(e){t={error:e}}finally{try{o&&!o.done&&(r=i.return)&&r.call(i)}finally{if(t)throw t.error}}return n}function aa(e,a,t,r,n,i,o,s,u,d){return new Xe(e,a,t,r,n,i,o,s,u,d).compute()}function ta(e,t,r,n){if(e===t||e<t&&r<0||t<e&&r>1)return a.util.makeZerosTypedArray(0,n);var i=Math.abs(Math.ceil((t-e)/r)),o=a.util.makeZerosTypedArray(i,n);t<e&&1===r&&(r=-1),o[0]=e;for(var s=1;s<o.length;s++)o[s]=o[s-1]+r;return o}var ra=H((function(e){return 1/Math.sqrt(e)})),na=B(a.Rsqrt,ra),ia={kernelName:a.Rsqrt,backendName:"cpu",kernelFunc:na};function oa(e,t,r,n,i,o,s,u,d,l){var p=[n/i,i],c=e.values,h=t.values;if(0===n)return a.buffer(r,t.dtype);var f=d instanceof a.TensorBuffer?d:a.buffer(p,t.dtype);"string"==typeof d||"number"==typeof d?f.values.fill(d):"boolean"==typeof d&&f.values.fill(+d);for(var m=0;m<o;m++){for(var v=[],k=0,g=0;g<s;g++){var b=c[m*s+g];v.push(b),k+=b*u[g]}if(k<0||k>=n/i)throw new Error("Invalid indices: ".concat(v," does not index into ").concat(r));for(var I=0;I<i;I++)l?f.values[k*i+I]+=h[m*i+I]:f.values[k*i+I]=0===t.rank?h[0]:h[m*i+I]}return f}var sa=H((function(e){return 1/(1+Math.exp(-e))})),ua=O(a.Sigmoid,(function(e){return 1/(1+Math.exp(-e))})),da={kernelName:a.Sigmoid,backendName:"cpu",kernelFunc:ua};function la(e,t,r,n,i){var o=a.slice_util.isSliceContinous(n,t,r),s=a.util.sizeFromShape(r),u=a.util.computeStrides(n);if(o){var p=a.slice_util.computeFlatOffset(t,u);return"string"===i?e.slice(p,p+s):e.subarray(p,p+s)}for(var c="string"===i?a.backend_util.fromUint8ToStringArray(e):e,h=a.buffer(n,i,c),f=a.buffer(r,i),m=0;m<f.size;++m){var v=f.indexToLoc(m),k=v.map((function(e,a){return e+t[a]}));f.set.apply(f,l([h.get.apply(h,l([],d(k),!1))],d(v),!1))}return"string"===i?a.backend_util.fromStringArrayToUint8(f.values):f.values}function pa(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.begin,s=n.size;p(i,"slice");var u=d(a.slice_util.parseSliceParams(i,o,s),2),l=u[0],c=u[1];a.slice_util.assertParamsValid(i,l,c);var h=la(r.data.get(i.dataId).values,l,c,i.shape,i.dtype);return r.makeTensorInfo(c,i.dtype,h)}var ca={kernelName:a.Slice,backendName:"cpu",kernelFunc:pa};function ha(e,t,r,n,i,o,s){var u=t[0],d=o[0],l=new Array(d),p=new Array(u),c=t[1];if(0===d){if(0!==u)throw new 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o=a.util.sizeFromShape(n),s=t[0],u=i.length,d=[],l=1,p=-1,c=0;c<u;++c){var h=i[c];if(-1===h){if(-1!==p)throw new Error(a.backend_util.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(p,c));p=c,d.push(1)}else{if(h<0)throw new Error(a.backend_util.getSparseReshapeNegativeOutputDimErrorMessage(c,h));l*=h,d.push(h)}}if(-1!==p){if(l<=0)throw new Error(a.backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());var f=Math.trunc(o/l);if(l*f!==o)throw new Error(a.backend_util.getSparseReshapeInputOutputMultipleErrorMessage(n,d));d[p]=f}if(a.util.sizeFromShape(d)!==o)throw new Error(a.backend_util.getSparseReshapeInputOutputMismatchErrorMessage(n,d));var m=n.length,v=[];if(m>0){v[m-1]=1;for(c=m-2;c>=0;--c)v[c]=v[c+1]*n[c+1]}var k=[];if(u>0){k[u-1]=1;for(c=u-2;c>=0;--c)k[c]=k[c+1]*d[c+1]}for(var g=a.util.getArrayFromDType(r,s*u),b=0;b<s;++b){for(var I=0,y=0;y<m;++y)I+=e[b*m+y]*v[y];for(y=0;y<u;++y)g[b*u+y]=Math.trunc(I/k[y]),I%=k[y]}return[g,[s,u],d]}function ma(e,t,r,n,i,o,s){void 0===o&&(o=!1),void 0===s&&(s=0);var u=n.length,d=[t[0],e.length/t[0]],l=d[1],p=u>0?i[u-1]+1:0;if(p<0)throw new Error(a.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());var c=t.slice();c[0]=p;var h=c.reduce((function(e,a){return e*a}),1),f=a.util.getArrayFromDType(r,h);if(0===u)return p>0&&f.fill(s),[f,c];if(p<=0)throw new Error(a.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());for(var m=0,v=1,k=0,g=i[m];;){var b=0;if(v<u){if(g===(b=i[v])){++v;continue}if(g>=b)throw new Error(a.backend_util.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(g<0||g>=p)throw new Error(a.backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(g,p));g>k&&f.fill(s,k*l,g*l);for(var I=m;I<v;++I){var y=n[I];if(y<0||y>=d[0])throw new Error(a.backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(I,n[I],d[0]));for(var 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Ba={__proto__:null,addImpl:A,bincountImpl:z,bincountReduceImpl:R,bitwiseAndImpl:W,castImpl:N,ceilImpl:V,concatImpl:q,equalImpl:U,expImpl:K,expm1Impl:Q,floorDivImpl:re,floorImpl:ee,gatherNdImpl:oe,gatherV2Impl:se,greaterEqualImpl:pe,greaterImpl:ue,lessEqualImpl:ke,lessImpl:fe,linSpaceImpl:Ie,logImpl:ye,maxImpl:Ne,maximumImpl:xe,minimumImpl:Me,multiplyImpl:Ee,negImpl:We,notEqualImpl:Ce,prodImpl:Le,raggedGatherImpl:Ke,raggedRangeImpl:Je,raggedTensorToTensorImpl:aa,rangeImpl:ta,rsqrtImpl:ra,scatterImpl:oa,sigmoidImpl:sa,simpleAbsImpl:f,sliceImpl:la,sparseFillEmptyRowsImpl:ha,sparseReshapeImpl:fa,sparseSegmentReductionImpl:ma,sqrtImpl:va,squaredDifferenceImpl:ba,staticRegexReplaceImpl:Sa,stridedSliceImpl:xa,stringNGramsImpl:wa,stringSplitImpl:Aa,stringToHashBucketFastImpl:Da,subImpl:Ea,tileImpl:Wa,topKImpl:Ha,transposeImpl:Be,uniqueImpl:Oa};a.registerBackend("cpu",(function(){return new h}),1);var Va=O(a.Elu,(function(e){return 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tt={kernelName:a.BatchMatMul,backendName:"cpu",kernelFunc:at};var rt={kernelName:a._FusedMatMul,backendName:"cpu",kernelFunc:function(e){var a,t,r,n,i,o=e.inputs,s=e.backend,d=e.attrs,l=o.a,p=o.b,c=o.bias,h=o.preluActivationWeights,f=d.transposeA,m=d.transposeB,v=d.activation,k=d.leakyreluAlpha,g=[];r=at({inputs:{a:l,b:p},attrs:{transposeA:f,transposeB:m},backend:s}),c&&(n=E({inputs:{a:r,b:c},backend:s}),g.push(r),r=n),v&&(i=Xa(s,r,v,h,k),g.push(r),r=i);try{for(var b=u(g),I=b.next();!I.done;I=b.next()){var y=I.value;s.disposeIntermediateTensorInfo(y)}}catch(e){a={error:e}}finally{try{I&&!I.done&&(t=b.return)&&t.call(b)}finally{if(a)throw a.error}}return r}},nt=O(a.Acos,(function(e){return Math.acos(e)})),it={kernelName:a.Acos,backendName:"cpu",kernelFunc:nt},ot=O(a.Acosh,(function(e){return Math.acosh(e)})),st={kernelName:a.Acosh,backendName:"cpu",kernelFunc:ot};var ut={kernelName:a.AddN,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=t;p(t,"addN");for(var i=n.map((function(e){return r.data.get(e.dataId).values})),o=a.buffer(n[0].shape,n[0].dtype),s=o.values,u=0;u<n.length;u++)for(var d=i[u],l=0;l<s.length;l++)s[l]+=d[l];return r.makeTensorInfo(o.shape,o.dtype,o.values)}};var dt={kernelName:a.All,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis,s=n.keepDims;p(i,"all");var u=a.util.parseAxisParam(o,i.shape),l=u,c=a.backend_util.getAxesPermutation(l,i.shape.length),h=i;null!=c&&(h=Ve({inputs:{x:i},backend:r,attrs:{perm:c}}),l=a.backend_util.getInnerMostAxes(l.length,i.shape.length)),a.backend_util.assertAxesAreInnerMostDims("all",l,h.shape.length);for(var f=d(a.backend_util.computeOutAndReduceShapes(h.shape,l),2),m=f[0],v=f[1],k=a.util.sizeFromShape(v),g=a.util.makeZerosTypedArray(a.util.sizeFromShape(m),h.dtype),b=r.data.get(h.dataId).values,I=0;I<g.length;++I){for(var y=I*k,S=b[y],T=0;T<k;++T){var N=b[y+T];S=S&&N}g[I]=S}null!=c&&r.disposeIntermediateTensorInfo(h);var x=r.makeTensorInfo(m,h.dtype,g);if(s){var F=$a({inputs:{x:x},backend:r,attrs:{shape:a.backend_util.expandShapeToKeepDim(m,u)}});return r.disposeIntermediateTensorInfo(x),F}return x}};var lt={kernelName:a.Any,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis,s=n.keepDims;p(i,"any");var u=a.util.parseAxisParam(o,i.shape),l=u,c=a.backend_util.getAxesPermutation(l,i.shape.length),h=i;null!=c&&(h=Ve({inputs:{x:i},backend:r,attrs:{perm:c}}),l=a.backend_util.getInnerMostAxes(l.length,i.shape.length)),a.backend_util.assertAxesAreInnerMostDims("any",l,h.shape.length);for(var f=d(a.backend_util.computeOutAndReduceShapes(h.shape,l),2),m=f[0],v=f[1],k=a.util.sizeFromShape(v),g=a.util.makeZerosTypedArray(a.util.sizeFromShape(m),h.dtype),b=r.data.get(h.dataId).values,I=0;I<g.length;++I){for(var y=I*k,S=b[y],T=0;T<k;++T){var N=b[y+T];S=S||N}g[I]=S}null!=c&&r.disposeIntermediateTensorInfo(h);var x=r.makeTensorInfo(m,h.dtype,g);if(s){var F=$a({inputs:{x:x},backend:r,attrs:{shape:a.backend_util.expandShapeToKeepDim(m,u)}});return r.disposeIntermediateTensorInfo(x),F}return x}};var pt={kernelName:a.ArgMax,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis;p(i,"argMax");var s=a.util.parseAxisParam(o,i.shape),u=a.backend_util.getAxesPermutation(s,i.shape.length),l=i,c=[];null!=u&&(l=Ve({inputs:{x:i},backend:r,attrs:{perm:u}}),c.push(l),s=a.backend_util.getInnerMostAxes(s.length,l.shape.length)),s=[s[0]],a.backend_util.assertAxesAreInnerMostDims("argMax",s,l.shape.length);for(var h=d(a.backend_util.computeOutAndReduceShapes(l.shape,s),2),f=h[0],m=h[1],v=a.util.sizeFromShape(f),k=a.util.makeZerosTypedArray(v,"int32"),g=a.util.sizeFromShape(m),b=r.data.get(l.dataId).values,I=0;I<k.length;++I){for(var y=I*g,S=b[y],T=0,N=0;N<g;++N){var x=b[y+N];x>S&&(S=x,T=N)}k[I]=T}return c.forEach((function(e){return r.disposeIntermediateTensorInfo(e)})),r.makeTensorInfo(f,"int32",k)}};var ct={kernelName:a.ArgMin,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis;p(i,"argMin");var s=a.util.parseAxisParam(o,i.shape),u=a.backend_util.getAxesPermutation(s,i.shape.length),l=i,c=[];null!=u&&(l=Ve({inputs:{x:i},backend:r,attrs:{perm:u}}),c.push(l),s=a.backend_util.getInnerMostAxes(s.length,l.shape.length)),s=[s[0]],a.backend_util.assertAxesAreInnerMostDims("argMin",s,l.shape.length);for(var h=d(a.backend_util.computeOutAndReduceShapes(l.shape,s),2),f=h[0],m=h[1],v=a.util.sizeFromShape(f),k=a.util.makeZerosTypedArray(v,"int32"),g=a.util.sizeFromShape(m),b=r.data.get(l.dataId).values,I=0;I<k.length;++I){for(var y=I*g,S=b[y],T=0,N=0;N<g;++N){var x=b[y+N];x<S&&(S=x,T=N)}k[I]=T}return c.forEach((function(e){return r.disposeIntermediateTensorInfo(e)})),r.makeTensorInfo(f,"int32",k)}},ht=O(a.Asin,(function(e){return Math.asin(e)})),ft={kernelName:a.Asin,backendName:"cpu",kernelFunc:ht},mt=O(a.Asinh,(function(e){return Math.asinh(e)})),vt={kernelName:a.Asinh,backendName:"cpu",kernelFunc:mt},kt=O(a.Atan,(function(e){return Math.atan(e)})),gt={kernelName:a.Atan,backendName:"cpu",kernelFunc:kt},bt=v((function(e,a){return Math.atan2(e,a)})),It=w(a.Atan2,bt),yt={kernelName:a.Atan2,backendName:"cpu",kernelFunc:It},St=O(a.Atanh,(function(e){return Math.atanh(e)})),Tt={kernelName:a.Atanh,backendName:"cpu",kernelFunc:St};function Nt(e,t,r,n,i,o){for(var s=i.strideHeight,u=i.strideWidth,d=i.dilationHeight,l=i.dilationWidth,p=i.effectiveFilterHeight,c=i.effectiveFilterWidth,h=i.padInfo.top,f=i.padInfo.left,m="max"===o?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=a.buffer(i.outShape,r),k=v.values,g=i.outShape[1]*i.outShape[2]*i.outShape[3],b=i.outShape[2]*i.outShape[3],I=i.outShape[3],y=0;y<i.batchSize;++y)for(var S=y*g,T=y*n[0],N=0;N<i.inChannels;++N)for(var x=0;x<i.outHeight;++x)for(var F=x*s-h,w=Math.max(0,F),M=Math.min(i.inHeight,p+F),A=S+x*b,D=0;D<i.outWidth;++D){for(var E=D*u-f,_=Math.max(0,E),z=Math.min(i.inWidth,c+E),R=m,W=0,P=0,C=w;C<M;C+=d){for(var H=T+C*n[1],O=_;O<z;O+=l){var B=e[H+O*n[2]+N];"max"===o&&B>R?R=B:"avg"===o&&(W+=B,P++)}if(isNaN(R))break}k[A+D*I+N]="avg"===o?W/P:R}return v}function xt(e,t,r,n,i,o){void 0===i&&(i=!1),void 0===o&&(o=!1);for(var s=a.buffer(n.outShape,"int32"),u=n.strideHeight,d=n.strideWidth,l=n.dilationHeight,p=n.dilationWidth,c=n.effectiveFilterHeight,h=n.effectiveFilterWidth,f=n.padInfo.top,m=n.padInfo.left,v=a.buffer(t,r,e),k=0;k<n.batchSize;++k)for(var g=0;g<n.inChannels;++g)for(var b=0;b<n.outHeight;++b){for(var I=b*u-f,y=I;y<0;)y+=l;for(var S=Math.min(n.inHeight,c+I),T=0;T<n.outWidth;++T){for(var N=T*d-m,x=N;x<0;)x+=p;for(var F=Math.min(n.inWidth,h+N),w=Number.NEGATIVE_INFINITY,M=-1,A=y;A<S;A+=l)for(var D=A-I,E=x;E<F;E+=p){var _=E-N,z=v.get(k,A,E,g);z>w&&(w=z,M=i?o?((k*n.inHeight+A)*n.inWidth+E)*n.inChannels+g:(A*n.inWidth+E)*n.inChannels+g:D*h+_)}s.set(M,k,b,T,g)}}return s}function Ft(e,t,r,n,i,o){for(var s=i.strideDepth,u=i.strideHeight,d=i.strideWidth,l=i.dilationDepth,p=i.dilationHeight,c=i.dilationWidth,h=i.effectiveFilterDepth,f=i.effectiveFilterHeight,m=i.effectiveFilterWidth,v=i.padInfo.front,k=i.padInfo.top,g=i.padInfo.left,b="max"===o?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,I=a.buffer(i.outShape,r),y=I.values,S=i.outShape[1]*i.outShape[2]*i.outShape[3]*i.outShape[4],T=i.outShape[2]*i.outShape[3]*i.outShape[4],N=i.outShape[3]*i.outShape[4],x=i.outShape[4],F=0;F<i.batchSize;++F)for(var w=F*S,M=F*n[0],A=0;A<i.inChannels;++A)for(var D=0;D<i.outDepth;++D){for(var E=D*s-v,_=E;_<0;)_+=l;for(var z=Math.min(i.inDepth,h+E),R=w+D*T,W=0;W<i.outHeight;++W){for(var P=W*u-k,C=P;C<0;)C+=p;for(var H=Math.min(i.inHeight,f+P),O=R+W*N,B=0;B<i.outWidth;++B){for(var V=B*d-g,G=V;G<0;)G+=c;for(var L=Math.min(i.inWidth,m+V),q=O+B*x,U=b,Z=0,j=0,K=_;K<z;K+=l){for(var Y=M+K*n[1],J=C;J<H;J+=p){for(var Q=Y+J*n[2],X=G;X<L;X+=c){var $=e[Q+X*n[3]+A];if("max"===o&&$>U?U=$:"avg"===o&&(Z+=$,j++),isNaN(U))break}if(isNaN(U))break}if(isNaN(U))break}y[q+A]="avg"===o?Z/Math.max(j,1):U}}}return I}var wt={kernelName:a.AvgPool,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x;p(i,"avgPool");var o=n.filterSize,s=n.strides,u=n.pad,d=n.dimRoundingMode;a.util.assert(a.backend_util.eitherStridesOrDilationsAreOne(s,1),(function(){return"Error in avgPool: Either strides or dilations must be 1. "+"Got strides ".concat(s," and dilations '").concat(1,"'")}));var l,c=a.backend_util.computePool2DInfo(i.shape,o,s,1,u,d);if(1===c.filterWidth&&1===c.filterHeight&&a.util.arraysEqual(c.inShape,c.outShape))l=I({inputs:{x:i},backend:r});else{var h=r.data.get(i.dataId).values,f=a.util.computeStrides(i.shape),m=Nt(h,i.shape,i.dtype,f,c,"avg");l=r.makeTensorInfo(c.outShape,i.dtype,m.values)}return l}};var Mt={kernelName:a.AvgPool3D,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.filterSize,s=n.strides,u=n.pad,d=n.dimRoundingMode,l=n.dataFormat;p(i,"avgPool3d");var c=a.backend_util.computePool3DInfo(i.shape,o,s,1,u,d,l),h=Ft(r.data.get(i.dataId).values,i.shape,i.dtype,a.util.computeStrides(i.shape),c,"avg");return r.makeTensorInfo(h.shape,"float32",h.values)}};var At={kernelName:a.AvgPool3DGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.dy,o=t.input,s=n.filterSize,u=n.strides,d=n.pad,l=n.dimRoundingMode;p([i,o],"avgPool3DGrad");for(var c=a.backend_util.computePool3DInfo(o.shape,s,u,1,d,l),h=c.strideDepth,f=c.strideHeight,m=c.strideWidth,v=c.filterDepth,k=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,I=c.dilationHeight,y=c.dilationWidth,S=c.effectiveFilterDepth,T=c.effectiveFilterHeight,N=c.effectiveFilterWidth,x=S-1-c.padInfo.front,F=N-1-c.padInfo.left,w=T-1-c.padInfo.top,M=a.buffer(o.shape,"float32"),A=1/(v*k*g),D=r.bufferSync(i),E=0;E<c.batchSize;++E)for(var _=0;_<c.inChannels;++_)for(var z=0;z<c.inDepth;++z)for(var R=0;R<c.inHeight;++R)for(var W=0;W<c.inWidth;++W){for(var P=z-x,C=R-w,H=W-F,O=0,B=0;B<S;B+=b){var V=(P+B)/h;if(!(V<0||V>=c.outDepth||Math.floor(V)!==V))for(var G=0;G<T;G+=I){var L=(C+G)/f;if(!(L<0||L>=c.outHeight||Math.floor(L)!==L))for(var q=0;q<N;q+=y){var U=(H+q)/m;if(!(U<0||U>=c.outWidth||Math.floor(U)!==U))O+=D.get(E,V,L,U,_)}}}M.set(O*A,E,z,R,W,_)}return r.makeTensorInfo(M.shape,M.dtype,M.values)}};var Dt={kernelName:a.AvgPoolGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.dy,o=t.input,s=o;p([i,o],"avgPoolGrad");for(var u=n.filterSize,d=n.strides,l=n.pad,c=a.backend_util.computePool2DInfo(s.shape,u,d,1,l),h=c.strideHeight,f=c.strideWidth,m=c.filterHeight,v=c.filterWidth,k=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,I=c.effectiveFilterWidth,y=I-1-c.padInfo.left,S=b-1-c.padInfo.top,T=a.buffer(s.shape,"float32"),N=1/(m*v),x=r.data.get(i.dataId).values,F=a.buffer(i.shape,"float32",x),w=0;w<c.batchSize;++w)for(var M=0;M<c.inChannels;++M)for(var A=0;A<c.inHeight;++A)for(var D=0;D<c.inWidth;++D){for(var E=A-S,_=D-y,z=0,R=0;R<b;R+=k){var W=(E+R)/h;if(!(W<0||W>=c.outHeight||Math.floor(W)!==W))for(var P=0;P<I;P+=g){var C=(_+P)/f;if(!(C<0||C>=c.outWidth||Math.floor(C)!==C))z+=F.get(w,W,C,M)}}T.set(z*N,w,A,D,M)}return r.makeTensorInfo(T.shape,T.dtype,T.values)}};var Et={kernelName:a.FusedBatchNorm,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.scale,s=t.offset,u=t.mean,d=t.variance;a.util.assert(u.shape.length===d.shape.length,(function(){return"Batch normalization gradient requires mean and variance to have equal ranks."})),a.util.assert(null==s||u.shape.length===s.shape.length,(function(){return"Batch normalization gradient requires mean and offset to have equal ranks."})),a.util.assert(null==o||u.shape.length===o.shape.length,(function(){return"Batch normalization gradient requires mean and scale to have equal ranks."})),p([i,u,d,o,s],"batchNorm");var l=n.varianceEpsilon;null==l&&(l=.001);for(var c=r.data.get(i.dataId).values,h=r.data.get(u.dataId).values,f=r.data.get(d.dataId).values,m=o?r.data.get(o.dataId).values:new Float32Array([1]),v=s?r.data.get(s.dataId).values:new Float32Array([0]),k=new Float32Array(c.length),g=v.length,b=m.length,I=f.length,y=h.length,S=0,T=0,N=0,x=0,F=0;F<c.length;++F)k[F]=v[S++]+(c[F]-h[T++])*m[N++]/Math.sqrt(f[x++]+l),S>=g&&(S=0),T>=y&&(T=0),N>=b&&(N=0),x>=I&&(x=0);return r.makeTensorInfo(i.shape,i.dtype,k)}};var _t={kernelName:a.BatchToSpaceND,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.blockShape,s=n.crops;p([i],"batchToSpaceND");var u=o.reduce((function(e,a){return e*a})),d=a.backend_util.getReshaped(i.shape,o,u),l=a.backend_util.getPermuted(d.length,o.length),c=a.backend_util.getReshapedPermuted(i.shape,o,u),h=a.backend_util.getSliceBeginCoords(s,o.length),f=a.backend_util.getSliceSize(c,s,o.length),m=$a({inputs:{x:i},backend:r,attrs:{shape:d}}),v=Ve({inputs:{x:m},backend:r,attrs:{perm:l}}),k=$a({inputs:{x:v},backend:r,attrs:{shape:c}}),g=pa({inputs:{x:k},backend:r,attrs:{begin:h,size:f}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo(k),g}};var zt={kernelName:a.Bincount,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.x,i=a.weights,o=r.size,s=z(t.data.get(n.dataId).values,t.data.get(i.dataId).values,i.dtype,i.shape,o);return t.makeTensorInfo([o],i.dtype,s)}};var Rt={kernelName:a.BroadcastArgs,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=t.s0,i=t.s1,o=r.data.get(n.dataId).values,s=r.data.get(i.dataId).values,u=a.backend_util.assertAndGetBroadcastShape(Array.from(o),Array.from(s));return r.makeTensorInfo([u.length],"int32",Int32Array.from(u))}},Wt=O(a.ClipByValue,(function(e,a){var t=a;return e>t.clipValueMax?t.clipValueMax:e<t.clipValueMin?t.clipValueMin:e})),Pt={kernelName:a.ClipByValue,backendName:"cpu",kernelFunc:Wt},Ct={kernelName:a.ComplexAbs,backendName:"cpu",kernelFunc:function(e){for(var t=e.inputs.x,r=e.backend,n=new Float32Array(a.util.sizeFromShape(t.shape)),i=r.data.get(t.dataId),o=i.complexTensorInfos.real,s=i.complexTensorInfos.imag,u=r.data.get(o.dataId).values,d=r.data.get(s.dataId).values,l=0;l<u.length;l++){var p=u[l],c=d[l];n[l]=Math.hypot(p,c)}return r.makeOutput(n,t.shape,"float32")}};function Ht(e){var a=e.inputs,t=e.backend,r=a.input,n=t.data.get(r.dataId).complexTensorInfos.imag,i=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,i)}var Ot={kernelName:a.Imag,backendName:"cpu",kernelFunc:Ht};function Bt(e){var t=e.inputs,r=e.backend,n=e.attrs.axis,i=a.util.parseAxisParam(n,t[0].shape)[0],o=t.map((function(e){return e.shape}));a.backend_util.assertParamsConsistent(o,i);var s=a.backend_util.computeOutShape(t.map((function(e){return e.shape})),i);if(0===a.util.sizeFromShape(s))return r.makeTensorInfo(s,t[0].dtype,[]);var u=t.filter((function(e){return a.util.sizeFromShape(e.shape)>0}));if(1===u.length)return I({inputs:{x:u[0]},backend:r});if("complex64"===u[0].dtype){var d=u.map((function(e){return S({inputs:{input:e},backend:r})})),l=u.map((function(e){return Ht({inputs:{input:e},backend:r})})),p=Bt({inputs:d,backend:r,attrs:{axis:i}}),c=Bt({inputs:l,backend:r,attrs:{axis:i}}),h=k({inputs:{real:p,imag:c},backend:r});return d.forEach((function(e){return r.disposeIntermediateTensorInfo(e)})),l.forEach((function(e){return r.disposeIntermediateTensorInfo(e)})),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),h}var f=u.map((function(e){var t=a.util.sizeFromShape(e.shape.slice(i));return $a({inputs:{x:e},backend:r,attrs:{shape:[-1,t]}})})),m=f.map((function(e){return{vals:r.data.get(e.dataId).values,shape:e.shape}}));s=a.backend_util.computeOutShape(f.map((function(e){return e.shape})),1);var v=1===f[0].shape[0],g=q(m,s,t[0].dtype,v),b=a.backend_util.computeOutShape(u.map((function(e){return e.shape})),i),y=r.makeTensorInfo(b,t[0].dtype,g);return f.forEach((function(e){return r.disposeIntermediateTensorInfo(e)})),y}var Vt={kernelName:a.Concat,backendName:"cpu",kernelFunc:Bt};function Gt(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.filter,s=n.strides,u=n.pad,d=n.dataFormat,l=n.dilations,c=n.dimRoundingMode;p([i,o],"conv2d");for(var h=a.backend_util.convertConv2DDataFormat(d),f=a.backend_util.computeConv2DInfo(i.shape,o.shape,s,l,u,c,!1,h),m=f.filterHeight,v=f.filterWidth,k=f.dilationHeight,g=f.dilationWidth,b=f.padInfo.left,I=f.padInfo.top,y="channelsLast"===f.dataFormat,S=new a.TensorBuffer(f.outShape,i.dtype),T=a.util.computeStrides(i.shape),N=a.util.computeStrides(o.shape),x=T[0],F=y?T[1]:T[2],w=y?T[2]:1,M=y?1:T[1],A=S.strides[0],D=y?S.strides[1]:S.strides[2],E=y?S.strides[2]:1,_=y?1:S.strides[1],z=r.data.get(i.dataId).values,R=r.data.get(o.dataId).values,W=S.values,P=0;P<f.batchSize;++P)for(var C=P*x,H=P*A,O=0;O<f.outHeight;++O)for(var B=H+O*D,V=O*f.strideHeight-I,G=0;G<m;++G){var L=V+G*k;if(!(L<0||L>=f.inHeight))for(var q=G*N[0],U=C+L*F,Z=0;Z<f.outWidth;++Z)for(var j=B+Z*E,K=Z*f.strideWidth-b,Y=0;Y<v;++Y){var J=K+Y*g;if(!(J<0||J>=f.inWidth))for(var Q=U+J*w,X=q+Y*N[1],$=0;$<f.inChannels;++$){for(var ee=z[Q+$*M],ae=0;ae<f.outChannels;++ae)W[j+ae*_]+=ee*R[X+ae];X+=f.outChannels}}}return r.makeTensorInfo(S.shape,S.dtype,W)}var Lt={kernelName:a.Conv2D,backendName:"cpu",kernelFunc:Gt};var qt={kernelName:a.Conv2DBackpropFilter,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.dy,s=n.strides,u=n.pad,d=n.dataFormat,l=n.dimRoundingMode,c=n.filterShape;p([i,o],"conv2dBackpropFilter");for(var h=a.backend_util.convertConv2DDataFormat(d),f=a.backend_util.computeConv2DInfo(i.shape,c,s,1,u,l,!1,h),m=f.strideHeight,v=f.strideWidth,k=f.filterHeight,g=f.filterWidth,b="channelsLast"===f.dataFormat,I=new a.TensorBuffer(f.filterShape,"float32"),y=f.padInfo.left,S=f.padInfo.top,T=r.data.get(i.dataId).values,N=r.data.get(o.dataId).values,x=new a.TensorBuffer(i.shape,i.dtype,T),F=new a.TensorBuffer(o.shape,o.dtype,N),w=0;w<k;++w)for(var M=Math.max(0,Math.ceil((S-w)/m)),A=Math.min(f.outHeight,(f.inHeight+S-w)/m),D=0;D<g;++D)for(var E=Math.max(0,Math.ceil((y-D)/v)),_=Math.min(f.outWidth,(f.inWidth+y-D)/v),z=0;z<f.inChannels;++z)for(var R=0;R<f.outChannels;++R){for(var W=0,P=0;P<f.batchSize;++P)for(var C=M;C<A;++C)for(var H=w+C*m-S,O=E;O<_;++O){var B=D+O*v-y;W+=b?x.get(P,H,B,z)*F.get(P,C,O,R):x.get(P,z,H,B)*F.get(P,R,C,O)}I.set(W,w,D,z,R)}return r.makeTensorInfo(I.shape,I.dtype,I.values)}};var Ut={kernelName:a.Conv2DBackpropInput,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.dy,o=t.filter,s=n.inputShape,u=n.strides,l=n.pad,c=n.dataFormat,h=n.dimRoundingMode;p([i,o],"conv2dBackpropInput");var f=a.util.computeStrides(o.shape),m=a.util.computeStrides(i.shape),v=a.backend_util.convertConv2DDataFormat(c),k=a.backend_util.computeConv2DInfo(s,o.shape,u,1,l,h,!1,v),g=new a.TensorBuffer(k.inShape,"float32"),b=g.values,I=r.data.get(i.dataId).values,y=r.data.get(o.dataId).values,S=d(f,3),T=S[0],N=S[1],x=S[2],F=k.batchSize,w=k.filterHeight,M=k.filterWidth,A=k.inChannels,D=k.inHeight,E=k.inWidth,_=k.outChannels,z=k.outHeight,R=k.outWidth,W=k.strideHeight,P=k.strideWidth;v=k.dataFormat;for(var C=w-1-k.padInfo.top,H=M-1-k.padInfo.left,O="channelsLast"===v,B=g.strides[0],V=O?g.strides[1]:g.strides[2],G=O?g.strides[2]:1,L=O?1:g.strides[1],q=m[0],U=O?m[1]:m[2],Z=O?m[2]:1,j=O?1:m[1],K=0;K<F;++K)for(var Y=0;Y<A;++Y)for(var J=0;J<D;++J)for(var Q=J-C,X=Math.max(0,Math.ceil(Q/W)),$=Math.min(z,(w+Q)/W),ee=0;ee<E;++ee){for(var ae=ee-H,te=Math.max(0,Math.ceil(ae/P)),re=Math.min(R,(M+ae)/P),ne=0,ie=X;ie<$;++ie)for(var oe=ie*W-Q,se=te;se<re;++se)for(var ue=q*K+U*ie+Z*se,de=T*(w-1-oe)+N*(M-1-(se*P-ae))+x*Y,le=0;le<_;++le){ne+=I[ue+j*le]*y[de+le]}b[B*K+V*J+G*ee+L*Y]=ne}return r.makeTensorInfo(g.shape,g.dtype,g.values)}};var Zt={kernelName:a.Conv3D,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.filter,s=n.strides,u=n.pad,d=n.dilations;p([i,o],"conv3d");for(var l=a.backend_util.computeConv3DInfo(i.shape,o.shape,s,d,u),c=l.filterDepth,h=l.filterHeight,f=l.filterWidth,m=l.dilationDepth,v=l.dilationHeight,k=l.dilationWidth,g=l.padInfo,b=g.front,I=g.left,y=g.top,S=new a.TensorBuffer(l.outShape,i.dtype),T=r.data.get(i.dataId).values,N=r.data.get(o.dataId).values,x=S.values,F=a.util.computeStrides(i.shape),w=a.util.computeStrides(o.shape),M=0;M<l.batchSize;++M)for(var A=M*F[0],D=M*S.strides[0],E=0;E<l.outDepth;++E)for(var _=D+E*S.strides[1],z=E*l.strideDepth-b,R=0;R<c;++R){var W=z+R*m;if(!(W<0||W>=l.inDepth))for(var P=R*w[0],C=A+W*F[1],H=0;H<l.outHeight;++H)for(var O=_+H*S.strides[2],B=H*l.strideHeight-y,V=0;V<h;++V){var G=B+V*v;if(!(G<0||G>=l.inHeight))for(var L=P+V*w[1],q=C+G*F[2],U=0;U<l.outWidth;++U)for(var Z=O+U*l.outChannels,j=U*l.strideWidth-I,K=0;K<f;++K){var Y=j+K*k;if(!(Y<0||Y>=l.inWidth))for(var J=L+K*w[2],Q=q+Y*l.inChannels,X=J,$=0;$<l.inChannels;++$){for(var ee=T[Q+$],ae=0;ae<l.outChannels;++ae)x[Z+ae]+=ee*N[X+ae];X+=l.outChannels}}}}return r.makeTensorInfo(S.shape,S.dtype,S.values)}};var jt={kernelName:a.Conv3DBackpropFilterV2,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.dy,s=n.strides,u=n.pad,l=n.filterShape;p([i,o],"conv3dBackpropFilterV2");for(var c=a.util.computeStrides(i.shape),h=a.util.computeStrides(o.shape),f=a.backend_util.computeConv3DInfo(i.shape,l,s,1,u),m=f.strideDepth,v=f.strideHeight,k=f.strideWidth,g=f.filterDepth,b=f.filterHeight,I=f.filterWidth,y=new a.TensorBuffer(f.filterShape,"float32"),S=y.values,T=d(y.strides,4),N=T[0],x=T[1],F=T[2],w=T[3],M=r.data.get(o.dataId).values,A=d(h,4),D=A[0],E=A[1],_=A[2],z=A[3],R=r.data.get(i.dataId).values,W=d(c,4),P=W[0],C=W[1],H=W[2],O=W[3],B=f.padInfo.front,V=f.padInfo.left,G=f.padInfo.top,L=0;L<g;++L)for(var q=Math.max(0,Math.ceil((B-L)/m)),U=Math.min(f.outDepth,(f.inDepth+B-L)/m),Z=L*N,j=0;j<b;++j)for(var K=Math.max(0,Math.ceil((G-j)/v)),Y=Math.min(f.outHeight,(f.inHeight+G-j)/v),J=j*x+Z,Q=0;Q<I;++Q)for(var X=Math.max(0,Math.ceil((V-Q)/k)),$=Math.min(f.outWidth,(f.inWidth+V-Q)/k),ee=Q*F+J,ae=0;ae<f.inChannels;++ae)for(var te=ae*w+ee,re=0;re<f.outChannels;++re){for(var ne=0,ie=0;ie<f.batchSize;++ie)for(var oe=ie*P,se=ie*D,ue=q;ue<U;++ue)for(var de=(L+ue*m-B)*C+oe,le=ue*E+se,pe=K;pe<Y;++pe)for(var ce=(j+pe*v-G)*H+de,he=pe*_+le,fe=X;fe<$;++fe){var me=fe*z+he;ne+=R[(Q+fe*k-V)*O+ce+ae]*M[me+re]}S[te+re]=ne}return r.makeTensorInfo(y.shape,y.dtype,y.values)}};var Kt={kernelName:a.Conv3DBackpropInputV2,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.dy,o=t.filter,s=n.pad,u=n.strides,l=n.inputShape;p([i],"conv3dBackpropInputV2");for(var c=a.util.computeStrides(i.shape),h=a.util.computeStrides(o.shape),f=a.backend_util.computeConv3DInfo(l,o.shape,u,1,s),m=new a.TensorBuffer(f.inShape,"float32"),v=m.values,k=d(m.strides,4),g=k[0],b=k[1],I=k[2],y=k[3],S=r.data.get(i.dataId).values,T=d(c,4),N=T[0],x=T[1],F=T[2],w=T[3],M=r.data.get(o.dataId).values,A=d(h,4),D=A[0],E=A[1],_=A[2],z=A[3],R=f.batchSize,W=f.filterDepth,P=f.filterHeight,C=f.filterWidth,H=f.inChannels,O=f.inDepth,B=f.inHeight,V=f.inWidth,G=f.outChannels,L=f.outDepth,q=f.outHeight,U=f.outWidth,Z=f.strideDepth,j=f.strideHeight,K=f.strideWidth,Y=W-1-f.padInfo.front,J=P-1-f.padInfo.top,Q=C-1-f.padInfo.left,X=0;X<R;++X)for(var $=0;$<H;++$)for(var ee=0;ee<O;++ee)for(var ae=ee-Y,te=Math.max(0,Math.ceil(ae/Z)),re=Math.min(L,(W+ae)/Z),ne=0;ne<B;++ne)for(var ie=ne-J,oe=Math.max(0,Math.ceil(ie/j)),se=Math.min(q,(P+ie)/j),ue=0;ue<V;++ue){for(var de=ue-Q,le=Math.max(0,Math.ceil(de/K)),pe=Math.min(U,(C+de)/K),ce=0,he=te;he<re;++he)for(var fe=he*Z-ae,me=oe;me<se;++me)for(var ve=me*j-ie,ke=le;ke<pe;++ke)for(var ge=N*X+x*he+F*me+w*ke,be=D*(W-1-fe)+E*(P-1-ve)+_*(C-1-(ke*K-de))+z*$,Ie=0;Ie<G;++Ie){ce+=S[ge+Ie]*M[be+Ie]}v[g*X+b*ee+I*ne+y*ue+$]=ce}return r.makeTensorInfo(m.shape,m.dtype,m.values)}},Yt=O(a.Cos,(function(e){return Math.cos(e)})),Jt={kernelName:a.Cos,backendName:"cpu",kernelFunc:Yt},Qt=O(a.Cosh,(function(e){return Math.cosh(e)})),Xt={kernelName:a.Cosh,backendName:"cpu",kernelFunc:Qt};var $t={kernelName:a.CropAndResize,backendName:"cpu",kernelFunc:function(e){for(var t=e.inputs,r=e.backend,n=e.attrs,i=t.image,o=t.boxes,s=t.boxInd,u=n.cropSize,l=n.method,p=n.extrapolationValue,c=d(i.shape,4),h=c[0],f=c[1],m=c[2],v=c[3],k=o.shape[0],g=d(u,2),b=g[0],I=g[1],y=a.buffer([k,b,I,v],"float32"),S=r.data.get(o.dataId).values,T=r.data.get(s.dataId).values,N=r.data.get(i.dataId).values,x=a.util.computeStrides(i.shape),F=a.util.computeStrides(y.shape),w=0;w<k;w++){var M=4*w,A=S[M],D=S[M+1],E=S[M+2],_=S[M+3],z=T[w];if(!(z>=h))for(var R=b>1?(E-A)*(f-1)/(b-1):0,W=I>1?(_-D)*(m-1)/(I-1):0,P=0;P<b;P++){var C=b>1?A*(f-1)+P*R:.5*(A+E)*(f-1);if(C<0||C>f-1)for(var H=0;H<I;H++)for(var O=0;O<v;O++){var B=O+H*F[2]+P*F[1]+w*F[0];y.values[B]=p}else if("bilinear"===l){var V=Math.floor(C),G=Math.ceil(C),L=C-V;for(H=0;H<I;H++){if((X=I>1?D*(m-1)+H*W:.5*(D+_)*(m-1))<0||X>m-1)for(O=0;O<v;O++){B=O+H*F[2]+P*F[1]+w*F[0];y.values[B]=p}else{var q=Math.floor(X),U=Math.ceil(X),Z=X-q;for(O=0;O<v;O++){var j=N[B=O+q*x[2]+V*x[1]+z*x[0]],K=N[B=O+U*x[2]+V*x[1]+z*x[0]],Y=N[B=O+q*x[2]+G*x[1]+z*x[0]],J=j+(K-j)*Z,Q=Y+(N[B=O+U*x[2]+G*x[1]+z*x[0]]-Y)*Z;B=O+H*F[2]+P*F[1]+w*F[0],y.values[B]=J+(Q-J)*L}}}}else for(H=0;H<I;++H){var X;if((X=I>1?D*(m-1)+H*W:.5*(D+_)*(m-1))<0||X>m-1)for(O=0;O<v;O++){B=O+H*F[2]+P*F[1]+w*F[0];y.values[B]=p}else{var $=Math.round(X),ee=Math.round(C);for(O=0;O<v;O++){var ae=O+$*x[2]+ee*x[1]+z*x[0],te=O+H*F[2]+P*F[1]+w*F[0];y.values[te]=N[ae]}}}}}return r.makeTensorInfo(y.shape,y.dtype,y.values)}};var er={kernelName:a.Cumprod,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis,s=n.exclusive,u=n.reverse;p(i,"cumprod");var d=a.backend_util.getAxesPermutation([o],i.shape.length),l=i;null!=d&&(l=Ve({inputs:{x:i},backend:r,attrs:{perm:d}}));var c=a.backend_util.getInnerMostAxes(1,i.shape.length)[0];if(c!==l.shape.length-1)throw new Error("backend.cumprod in CPU expects an inner-most "+"axis=".concat(l.shape.length-1," but got axis=").concat(c));for(var h=a.upcastType(l.dtype,"int32"),f=a.util.makeOnesTypedArray(a.util.sizeFromShape(l.shape),h),m=r.data.get(l.dataId).values,v=l.shape[l.shape.length-1],k=u?function(e,a){return e+v-a-1}:function(e,a){return e+a},g=0;g<m.length;g+=v)for(var b=0;b<v;b++){var I=k(g,b);if(0===b)f[I]=s?1:m[I];else{var y=k(g,b-1);f[I]=s?m[y]*f[y]:m[I]*f[y]}}var S=r.makeTensorInfo(l.shape,h,f);if(null!=d){var T=Ve({inputs:{x:S},backend:r,attrs:{perm:a.backend_util.getUndoAxesPermutation(d)}});return r.disposeIntermediateTensorInfo(S),r.disposeIntermediateTensorInfo(l),T}return S}};var ar={kernelName:a.Cumsum,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis,s=n.exclusive,u=n.reverse;p(i,"cumsum");var d=a.backend_util.getAxesPermutation([o],i.shape.length),l=i;null!=d&&(l=Ve({inputs:{x:i},backend:r,attrs:{perm:d}}));var c=a.backend_util.getInnerMostAxes(1,i.shape.length)[0];if(c!==l.shape.length-1)throw new Error("backend.cumsum in CPU expects an inner-most "+"axis=".concat(l.shape.length-1," but got axis=").concat(c));for(var h=a.upcastType(l.dtype,"int32"),f=a.util.makeZerosTypedArray(a.util.sizeFromShape(l.shape),h),m=r.data.get(l.dataId).values,v=l.shape[l.shape.length-1],k=u?function(e,a){return e+v-a-1}:function(e,a){return e+a},g=0;g<m.length;g+=v)for(var b=0;b<v;b++){var I=k(g,b);if(0===b)f[I]=s?0:m[I];else{var y=k(g,b-1);f[I]=s?m[y]+f[y]:m[I]+f[y]}}var S=r.makeTensorInfo(l.shape,h,f);if(null!=d){var T=Ve({inputs:{x:S},backend:r,attrs:{perm:a.backend_util.getUndoAxesPermutation(d)}});return r.disposeIntermediateTensorInfo(S),r.disposeIntermediateTensorInfo(l),T}return S}};var tr={kernelName:a.DenseBincount,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.x,i=a.weights,o=r.size,s=r.binaryOutput;if(1===n.shape.length){var u=z(t.data.get(n.dataId).values,t.data.get(i.dataId).values,i.dtype,i.shape,o);return t.makeTensorInfo([o],i.dtype,u)}if(2===n.shape.length){var d=R(t.bufferSync(n),t.bufferSync(i),o,s);return t.makeTensorInfo(d.shape,i.dtype,d.values)}throw new Error("Error in denseBincount: input must be at most rank 2, but got rank"+"".concat(n.shape.length,"."))}};var rr={kernelName:a.DepthToSpace,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.blockSize,s=n.dataFormat;a.util.assert("NHWC"===s,(function(){return"Only NHWC dataFormat supported on CPU for depthToSpace. Got ".concat(s)}));for(var u=i.shape[0],d=i.shape[1],l=i.shape[2],p=i.shape[3],c=d*o,h=l*o,f=p/(o*o),m=r.data.get(i.dataId).values,v=new Float32Array(u*c*h*f),k=0,g=0;g<u;++g)for(var b=0;b<c;++b)for(var I=Math.floor(b/o),y=b%o,S=0;S<h;++S)for(var T=Math.floor(S/o),N=(y*o+S%o)*f,x=0;x<f;++x){var F=x+N+p*(T+l*(I+d*g));v[k++]=m[F]}return r.makeTensorInfo([u,c,h,f],i.dtype,v)}};function nr(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.filter,s=n.strides,u=n.pad,d=n.dilations,l=n.dimRoundingMode;p([i,o],"depthwiseConv2DNative");var c=a.util.computeStrides(i.shape),h=a.util.computeStrides(o.shape),f=d;null==f&&(f=[1,1]),a.util.assert(a.backend_util.eitherStridesOrDilationsAreOne(s,f),(function(){return"Error in depthwiseConv2d: Either strides or dilations must be "+"1. Got strides ".concat(s," and dilations '").concat(f,"'")}));for(var m=a.backend_util.computeConv2DInfo(i.shape,o.shape,s,f,u,l,!0),v=m.filterHeight,k=m.filterWidth,g=m.dilationHeight,b=m.dilationWidth,I=m.padInfo,y=I.left,S=I.top,T=m.outChannels/m.inChannels,N=new a.TensorBuffer(m.outShape,i.dtype),x=r.data.get(i.dataId).values,F=r.data.get(o.dataId).values,w=N.values,M=0;M<m.batchSize;++M)for(var A=M*c[0],D=M*N.strides[0],E=0;E<m.outHeight;++E)for(var _=D+E*N.strides[1],z=E*m.strideHeight-S,R=0;R<v;++R){var W=z+R*g;if(!(W<0||W>=m.inHeight))for(var P=R*h[0],C=A+W*c[1],H=0;H<m.outWidth;++H)for(var O=_+H*N.strides[2],B=H*m.strideWidth-y,V=0;V<k;++V){var G=B+V*b;if(!(G<0||G>=m.inWidth))for(var L=P+V*h[1],q=C+G*m.inChannels,U=O,Z=L,j=0;j<m.inChannels;++j){for(var K=x[q+j],Y=0;Y<T;++Y)w[U+Y]+=K*F[Z+Y];U+=T,Z+=T}}}return r.makeTensorInfo(N.shape,N.dtype,N.values)}var ir={kernelName:a.DepthwiseConv2dNative,backendName:"cpu",kernelFunc:nr};var or={kernelName:a.DepthwiseConv2dNativeBackpropFilter,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.dy,s=n.strides,u=n.dilations,d=n.pad,l=n.dimRoundingMode,c=n.filterShape;p([i,o],"depthwiseConv2dNativeBackpropFilter");for(var h=a.backend_util.computeConv2DInfo(i.shape,c,s,u,d,l,!0),f=h.strideHeight,m=h.strideWidth,v=h.filterHeight,k=h.filterWidth,g=new a.TensorBuffer(h.filterShape,"float32"),b=h.padInfo.left,I=h.padInfo.top,y=h.outChannels/h.inChannels,S=r.data.get(i.dataId).values,T=new a.TensorBuffer(i.shape,i.dtype,S),N=r.data.get(o.dataId).values,x=new a.TensorBuffer(o.shape,o.dtype,N),F=0;F<v;++F)for(var w=Math.max(0,Math.ceil((I-F)/f)),M=Math.min(h.outHeight,(h.inHeight+I-F)/f),A=0;A<k;++A)for(var D=Math.max(0,Math.ceil((b-A)/m)),E=Math.min(h.outWidth,(h.inWidth+b-A)/m),_=0;_<h.outChannels;++_){for(var z=Math.trunc(_/y),R=_%y,W=0,P=0;P<h.batchSize;++P)for(var C=w;C<M;++C)for(var H=F+C*f-I,O=D;O<E;++O){var B=A+O*m-b;W+=T.get(P,H,B,z)*x.get(P,C,O,_)}g.set(W,F,A,z,R)}return r.makeTensorInfo(g.shape,g.dtype,g.values)}};var sr={kernelName:a.DepthwiseConv2dNativeBackpropInput,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.dy,o=t.filter,s=n.strides,u=n.dilations,l=n.pad,c=n.dimRoundingMode,h=n.inputShape;p([i,o],"depthwiseConv2DNativeBackpropInput");for(var f=a.util.computeStrides(i.shape),m=a.util.computeStrides(o.shape),v=a.backend_util.computeConv2DInfo(h,o.shape,s,u,l,c,!0),k=new a.TensorBuffer(v.inShape,"float32"),g=k.values,b=d(k.strides,3),I=b[0],y=b[1],S=b[2],T=r.data.get(i.dataId).values,N=d(f,3),x=N[0],F=N[1],w=N[2],M=r.data.get(o.dataId).values,A=d(m,3),D=A[0],E=A[1],_=A[2],z=v.batchSize,R=v.filterHeight,W=v.filterWidth,P=v.inChannels,C=v.inHeight,H=v.inWidth,O=v.outChannels,B=v.outHeight,V=v.outWidth,G=v.strideHeight,L=v.strideWidth,q=R-1-v.padInfo.top,U=W-1-v.padInfo.left,Z=O/P,j=0;j<z;++j)for(var K=0;K<P;++K)for(var Y=0;Y<C;++Y)for(var J=Y-q,Q=Math.max(0,Math.ceil(J/G)),X=Math.min(B,(R+J)/G),$=0;$<H;++$){for(var ee=$-U,ae=Math.max(0,Math.ceil(ee/L)),te=Math.min(V,(W+ee)/L),re=0,ne=Q;ne<X;++ne)for(var ie=ne*G-J,oe=ae;oe<te;++oe)for(var se=x*j+F*ne+w*oe,ue=D*(R-1-ie)+E*(W-1-(oe*L-ee))+_*K,de=0;de<Z;++de){re+=T[se+(K*Z+de)]*M[ue+de]}g[I*j+y*Y+S*$+K]=re}return r.makeTensorInfo(k.shape,k.dtype,k.values)}};var ur={kernelName:a.Diag,backendName:"cpu",kernelFunc:function(e){for(var t=e.inputs,r=e.backend,n=t.x,i=a.util.sizeFromShape(n.shape),o=r.data.get(n.dataId).values,s=a.buffer([i,i],n.dtype),u=s.values,p=0;p<o.length;p++)u[p*i+p]=o[p];var c=l(l([],d(n.shape),!1),d(n.shape),!1);return r.makeTensorInfo(c,s.dtype,s.values)}},dr={kernelName:a.Dilation2D,backendName:"cpu",kernelFunc:function(e){for(var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.filter,s=n.strides,u=n.pad,d=n.dilations,l=r,p=l.data.get(i.dataId).values,c=i.shape.length,h=l.data.get(o.dataId).values,f=o.shape.length,m=a.backend_util.computeDilation2DInfo(i.shape,o.shape,s,u,"NHWC",d),v=m.batchSize,k=m.inHeight,g=m.inWidth,b=m.inChannels,I=m.outHeight,y=m.outWidth,S=m.padInfo,T=m.strideHeight,N=m.strideWidth,x=m.filterHeight,F=m.filterWidth,w=m.dilationHeight,M=m.dilationWidth,A=m.outShape,D=a.util.sizeFromShape(A),E=A.length,_=a.util.getArrayFromDType(i.dtype,D),z=0;z<v;++z)for(var R=0;R<I;++R)for(var W=R*T-S.top,P=0;P<y;++P)for(var C=P*N-S.left,H=0;H<b;++H){for(var O=Number.MIN_SAFE_INTEGER,B=0;B<x;++B){var V=W+B*w;if(V>=0&&V<k)for(var G=0;G<F;++G){var L=C+G*M;if(L>=0&&L<g){var q=a.util.locToIndex([z,V,L,H],c,a.util.computeStrides(i.shape)),U=a.util.locToIndex([B,G,H],f,a.util.computeStrides(o.shape)),Z=p[q]+h[U];Z>O&&(O=Z)}}}_[a.util.locToIndex([z,R,P,H],E,a.util.computeStrides(A))]=O}return{dataId:l.write(a.util.toTypedArray(_,i.dtype),A,i.dtype),shape:A,dtype:i.dtype}}},lr={kernelName:a.Dilation2DBackpropFilter,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.filter,s=t.dy,u=n.strides,d=n.pad,l=n.dilations,p=r,c=a.util.toNestedArray(i.shape,p.data.get(i.dataId).values),h=a.util.toNestedArray(o.shape,p.data.get(o.dataId).values),f=a.backend_util.computeDilation2DInfo(i.shape,o.shape,u,d,"NHWC",l),m=f.batchSize,v=f.inHeight,k=f.inWidth,g=f.inChannels,b=f.outHeight,I=f.outWidth,y=f.padInfo,S=f.strideHeight,T=f.strideWidth,N=f.filterHeight,x=f.filterWidth,F=f.dilationHeight,w=f.dilationWidth,M=f.outShape;a.util.assert(s.rank===M.length,(function(){return"Error in ".concat(a.Dilation2DBackpropFilter,", dy ")+"must have the same rank as output ".concat(M.length,", but got ")+"".concat(s.rank)}));for(var A=a.util.toNestedArray(M,p.data.get(s.dataId).values),D=a.util.makeZerosNestedTypedArray(o.shape,o.dtype),E=0;E<m;++E)for(var _=0;_<b;++_)for(var z=_*S-y.top,R=0;R<I;++R)for(var W=R*T-y.left,P=0;P<g;++P){for(var C=Number.MIN_SAFE_INTEGER,H=0,O=0,B=0;B<N;++B){var V=z+B*F;if(V>=0&&V<v)for(var G=0;G<x;++G){var L=W+G*w;if(L>=0&&L<k){var q=c[E][V][L][P]+h[B][G][P];q>C&&(C=q,H=B,O=G)}}}D[H][O][P]+=A[E][_][R][P]}return{dataId:p.write(a.util.toTypedArray(D,i.dtype),o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}},pr={kernelName:a.Dilation2DBackpropInput,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.filter,s=t.dy,u=n.strides,d=n.pad,l=n.dilations,p=r,c=a.util.toNestedArray(i.shape,p.data.get(i.dataId).values),h=a.util.toNestedArray(o.shape,p.data.get(o.dataId).values),f=a.backend_util.computeDilation2DInfo(i.shape,o.shape,u,d,"NHWC",l),m=f.batchSize,v=f.inHeight,k=f.inWidth,g=f.inChannels,b=f.outHeight,I=f.outWidth,y=f.padInfo,S=f.strideHeight,T=f.strideWidth,N=f.filterHeight,x=f.filterWidth,F=f.dilationHeight,w=f.dilationWidth,M=f.outShape;a.util.assert(s.rank===M.length,(function(){return"Error in ".concat(a.Dilation2DBackpropInput,", dy ")+"must have the same rank as output ".concat(M.length,", but got ")+"".concat(s.rank)}));for(var A=a.util.toNestedArray(M,p.data.get(s.dataId).values),D=a.util.makeZerosNestedTypedArray(i.shape,i.dtype),E=0;E<m;++E)for(var _=0;_<b;++_)for(var z=_*S-y.top,R=0;R<I;++R)for(var W=R*T-y.left,P=0;P<g;++P){for(var C=Number.MIN_SAFE_INTEGER,H=z<0?0:z,O=W<0?0:W,B=0;B<N;++B){var V=z+B*F;if(V>=0&&V<v)for(var G=0;G<x;++G){var L=W+G*w;if(L>=0&&L<k){var q=c[E][V][L][P]+h[B][G][P];q>C&&(C=q,H=V,O=L)}}}D[E][H][O][P]+=A[E][_][R][P]}return{dataId:p.write(a.util.toTypedArray(D,i.dtype),i.shape,i.dtype),shape:i.shape,dtype:i.dtype}}};var cr={kernelName:a.Draw,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.image,i=r.canvas,o=r.options||{},s=o.contextOptions,u=o.imageOptions,l=(null==u?void 0:u.alpha)||1,p=(null==s?void 0:s.contextType)||"2d";if("2d"!==p)throw new Error("Context type ".concat(s.contextType," is not supported by the CPU backend."));var c=i.getContext(p,(null==s?void 0:s.contextAttributes)||{});if(null==c)throw new Error("Could not get the context with ".concat(p," type."));for(var h=d(n.shape.slice(0,2),2),f=h[0],m=h[1],v=2===n.shape.length?1:n.shape[2],k=t.data.get(n.dataId).values,g="float32"===n.dtype?255:1,b=new Uint8ClampedArray(m*f*4),I=0;I<f*m;++I){for(var y=[0,0,0,255*l],S=0;S<v;S++){var T=k[I*v+S];if("float32"===n.dtype){if(T<0||T>1)throw new Error("Tensor values for a float32 Tensor must be in the "+"range [0 - 1] but encountered ".concat(T,"."))}else if("int32"===n.dtype&&(T<0||T>255))throw new Error("Tensor values for a int32 Tensor must be in the "+"range [0 - 255] but encountered ".concat(T,"."));1===v?(y[0]=T*g,y[1]=T*g,y[2]=T*g):y[S]=T*g}var N=4*I;b[N+0]=Math.round(y[0]),b[N+1]=Math.round(y[1]),b[N+2]=Math.round(y[2]),b[N+3]=Math.round(y[3])}i.width=m,i.height=f;var x=new ImageData(b,m,f);return c.putImageData(x,0,0),n}};function hr(e){var t,r=e.inputs,n=e.backend,i=e.attrs,o=r.x,s=i.axis,u=i.keepDims;p(o,"sum");var l=(t="bool"===o.dtype?x({inputs:{x:o},backend:n,attrs:{dtype:"int32"}}):I({inputs:{x:o},backend:n})).shape.length,c=a.util.parseAxisParam(s,t.shape),h=a.backend_util.getAxesPermutation(c,l),f=c,m=t;null!=h&&(m=Ve({inputs:{x:t},backend:n,attrs:{perm:h}}),f=a.backend_util.getInnerMostAxes(f.length,l)),a.backend_util.assertAxesAreInnerMostDims("sum",f,m.shape.length);for(var v=d(a.backend_util.computeOutAndReduceShapes(m.shape,f),2),k=v[0],g=v[1],y=b(n,k,a.backend_util.upcastType(m.dtype,"int32")),S=a.util.sizeFromShape(g),T=n.data.get(y.dataId).values,N=n.data.get(m.dataId).values,F=0;F<T.length;++F){for(var w=F*S,M=0,A=0;A<S;++A)M+=N[w+A];T[F]=M}if(u){var D=y;y=$a({inputs:{x:y},backend:n,attrs:{shape:a.backend_util.expandShapeToKeepDim(y.shape,c)}}),n.disposeIntermediateTensorInfo(D)}return n.disposeIntermediateTensorInfo(t),null!=h&&n.disposeIntermediateTensorInfo(m),y}var fr={kernelName:a.Sum,backendName:"cpu",kernelFunc:hr};var mr={kernelName:a.Einsum,backendName:"cpu",kernelFunc:function(e){var t,r,n,i,o=e.inputs,s=e.backend,d=e.attrs.equation,l=o,p=a.backend_util.decodeEinsumEquation(d,l.length),c=p.allDims,h=p.summedDims,f=p.idDims;a.backend_util.checkEinsumDimSizes(c.length,f,l);for(var m=a.backend_util.getEinsumComputePath(h,f),v=m.path,k=m.steps,g=k.length,b=null,I=c.length,y=[],S=0;S<g;++S){try{for(var T=(t=void 0,u(k[S])),N=T.next();!N.done;N=T.next()){var x=N.value,F=a.backend_util.getEinsumPermutation(I,f[x]),w=F.permutationIndices,M=F.expandDims,A=void 0;a.backend_util.isIdentityPermutation(w)?A=l[x]:(A=Ve({inputs:{x:l[x]},backend:s,attrs:{perm:w}}),y.push(A));for(var D=A.shape.slice(),E=0;E<M.length;++E)D.splice(M[E],0,1);a.util.arraysEqual(A.shape,D)||(A=$a({inputs:{x:A},backend:s,attrs:{shape:D}}),y.push(A)),null===b?b=A:(b=ze({inputs:{a:A,b:b},backend:s}),y.push(b))}}catch(e){t={error:e}}finally{try{N&&!N.done&&(r=T.return)&&r.call(T)}finally{if(t)throw t.error}}S<g-1&&(v[S]>=0&&(b=hr({inputs:{x:b},backend:s,attrs:{axis:v[S]-(c.length-I),keepDims:!1}}),y.push(b)),I--)}try{for(var _=u(y),z=_.next();!z.done;z=_.next()){var R=z.value;R!==b&&s.disposeIntermediateTensorInfo(R)}}catch(e){n={error:e}}finally{try{z&&!z.done&&(i=_.return)&&i.call(_)}finally{if(n)throw n.error}}return b}};var vr={kernelName:a.EluGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=t.dy,i=t.y;p([n,i],"eluGrad");for(var o=new Float32Array(a.util.sizeFromShape(i.shape)),s=r.data.get(i.dataId).values,u=r.data.get(n.dataId).values,d=0;d<s.length;++d){var l=s[d];o[d]=l>=0?u[d]:u[d]*(l+1)}return r.makeTensorInfo(i.shape,"float32",o)}},kr=a.backend_util.ERF_P,gr=a.backend_util.ERF_A1,br=a.backend_util.ERF_A2,Ir=a.backend_util.ERF_A3,yr=a.backend_util.ERF_A4,Sr=a.backend_util.ERF_A5,Tr=O(a.Erf,(function(e){var a=Math.sign(e),t=Math.abs(e),r=1/(1+kr*t);return a*(1-((((Sr*r+yr)*r+Ir)*r+br)*r+gr)*r*Math.exp(-t*t))})),Nr={kernelName:a.Erf,backendName:"cpu",kernelFunc:Tr};function xr(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.input,o=n.dim,s=i.shape.length,u=i.shape.slice(),d=o;return o<0&&(a.util.assert(-(s+1)<=o,(function(){return"Axis must be in the interval [".concat(-(s+1),", ").concat(s,"]")})),d=s+o+1),u.splice(d,0,1),$a({inputs:{x:i},backend:r,attrs:{shape:u}})}var Fr={kernelName:a.ExpandDims,backendName:"cpu",kernelFunc:xr},wr=v((function(e,a){return e/a})),Mr=w(a.RealDiv,wr),Ar={kernelName:a.RealDiv,backendName:"cpu",kernelFunc:Mr};function Dr(e,t,r){for(var n=e.shape,i=n[0],o=n[1],s=r.data.get(e.dataId),u=s.complexTensorInfos.real,d=s.complexTensorInfos.imag,l=[i,o],p=a.util.sizeFromShape(l),c=a.util.getTypedArrayFromDType("float32",p),h=a.util.getTypedArrayFromDType("float32",p),f=0;f<i;f++){for(var m=pa({inputs:{x:u},backend:r,attrs:{begin:[f,0],size:[1,o]}}),v=pa({inputs:{x:d},backend:r,attrs:{begin:[f,0],size:[1,o]}}),g=k({inputs:{real:m,imag:v},backend:r}),b=Er(g,t,r),I=b.real,y=b.imag,S=a.backend_util.mergeRealAndImagArrays(I,y),T=0;T<o;T++){var N=a.backend_util.getComplexWithIndex(S,T);c[f*o+T]=N.real,h[f*o+T]=N.imag}r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(v),r.disposeIntermediateTensorInfo(g)}var x=r.makeTensorInfo(l,"float32",c),F=r.makeTensorInfo(l,"float32",h),w=k({inputs:{real:x,imag:F},backend:r});return r.disposeIntermediateTensorInfo(x),r.disposeIntermediateTensorInfo(F),w}function Er(e,t,r){var n=a.util.sizeFromShape(e.shape),i=r.data.get(e.dataId),o=r.data.get(i.complexTensorInfos.real.dataId).values,s=r.data.get(i.complexTensorInfos.imag.dataId).values;if(0==((g=n)&g-1)){var u=_r(o,s,n,t,r),d=[e.shape[0],e.shape[1]];if(t){var l=r.makeTensorInfo(d,"float32",u.real),p=r.makeTensorInfo(d,"float32",u.imag),c=r.makeTensorInfo([],"float32",a.util.createScalarValue(n,"float32")),h=I({inputs:{x:c},backend:r}),f=Ar.kernelFunc({inputs:{a:l,b:c},backend:r}),m=Ar.kernelFunc({inputs:{a:p,b:h},backend:r}),v=r.data.get(f.dataId).values,k=r.data.get(m.dataId).values;return r.disposeIntermediateTensorInfo(l),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),{real:v,imag:k}}return u}var g,b=function(e,t,r){for(var n=new Float32Array(2*t),i=0;i<t;i++){for(var o=0,s=0,u=0;u<t;u++){var d=a.backend_util.exponent(i*u,t,r),l=a.backend_util.getComplexWithIndex(e,u);o+=l.real*d.real-l.imag*d.imag,s+=l.real*d.imag+l.imag*d.real}r&&(o/=t,s/=t),a.backend_util.assignToTypedArray(n,o,s,i)}return n}(a.backend_util.mergeRealAndImagArrays(o,s),n,t);return a.backend_util.splitRealAndImagArrays(b)}function _r(e,t,r,n,i){if(1===r)return{real:e,imag:t};var o=a.backend_util.mergeRealAndImagArrays(e,t),s=r/2,u=a.backend_util.complexWithEvenIndex(o),d=u.real,l=u.imag,p=[d.length],c=i.makeTensorInfo(p,"float32",d),h=i.makeTensorInfo(p,"float32",l),f=k({inputs:{real:c,imag:h},backend:i}),m=a.backend_util.complexWithOddIndex(o),v=m.real,g=m.imag,b=[v.length],I=i.makeTensorInfo(b,"float32",v),y=i.makeTensorInfo(b,"float32",g),T=k({inputs:{real:I,imag:y},backend:i}),N=_r(d,l,s,n,i),x=N.real,F=N.imag,w=[x.length],M=i.makeTensorInfo(w,"float32",x),A=i.makeTensorInfo(w,"float32",F),D=k({inputs:{real:M,imag:A},backend:i}),_=_r(v,g,s,n,i),z=_.real,R=_.imag,W=[z.length],P=i.makeTensorInfo(W,"float32",z),C=i.makeTensorInfo(W,"float32",R),H=k({inputs:{real:P,imag:C},backend:i}),O=a.backend_util.exponents(r,n),B=[O.real.length],V=i.makeTensorInfo(B,"float32",O.real),G=i.makeTensorInfo(B,"float32",O.imag),L=k({inputs:{real:V,imag:G},backend:i}),q=ze({inputs:{a:L,b:H},backend:i}),U=E({inputs:{a:D,b:q},backend:i}),Z=za({inputs:{a:D,b:q},backend:i}),j=S({inputs:{input:U},backend:i}),K=S({inputs:{input:Z},backend:i}),Y=Ht({inputs:{input:U},backend:i}),J=Ht({inputs:{input:Z},backend:i}),Q=Bt({inputs:[j,K],backend:i,attrs:{axis:0}}),X=Bt({inputs:[Y,J],backend:i,attrs:{axis:0}}),$=i.data.get(Q.dataId).values,ee=i.data.get(X.dataId).values;return i.disposeIntermediateTensorInfo(c),i.disposeIntermediateTensorInfo(h),i.disposeIntermediateTensorInfo(f),i.disposeIntermediateTensorInfo(I),i.disposeIntermediateTensorInfo(y),i.disposeIntermediateTensorInfo(T),i.disposeIntermediateTensorInfo(M),i.disposeIntermediateTensorInfo(A),i.disposeIntermediateTensorInfo(D),i.disposeIntermediateTensorInfo(P),i.disposeIntermediateTensorInfo(C),i.disposeIntermediateTensorInfo(H),i.disposeIntermediateTensorInfo(V),i.disposeIntermediateTensorInfo(G),i.disposeIntermediateTensorInfo(L),i.disposeIntermediateTensorInfo(q),i.disposeIntermediateTensorInfo(U),i.disposeIntermediateTensorInfo(Z),i.disposeIntermediateTensorInfo(j),i.disposeIntermediateTensorInfo(Y),i.disposeIntermediateTensorInfo(K),i.disposeIntermediateTensorInfo(J),i.disposeIntermediateTensorInfo(Q),i.disposeIntermediateTensorInfo(X),{real:$,imag:ee}}var zr={kernelName:a.FFT,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=t.input,i=a.util.sizeFromShape(n.shape),o=n.shape[n.shape.length-1],s=$a({inputs:{x:n},backend:r,attrs:{shape:[i/o,o]}}),u=Dr(s,!1,r),d=$a({inputs:{x:u},backend:r,attrs:{shape:n.shape}});return r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(u),d}};function Rr(e){var t=e.backend,r=e.attrs,n=r.shape,i=r.value,o=r.dtype||a.util.inferDtype(i),s=a.util.getArrayFromDType(o,a.util.sizeFromShape(n));return function(e,a,t){e.fill(a)}(s,i),t.makeTensorInfo(n,o,s)}var Wr={kernelName:a.Fill,backendName:"cpu",kernelFunc:Rr};var Pr={kernelName:a.FlipLeftRight,backendName:"cpu",kernelFunc:function(e){var t=e.inputs;e.attrs;for(var r=e.backend,n=t.image,i=r,o=a.util.getTypedArrayFromDType(n.dtype,a.util.sizeFromShape(n.shape)),s=d(n.shape,4),u=s[0],l=s[1],p=s[2],c=s[3],h=i.data.get(n.dataId).values,f=0;f<u;f++)for(var m=f*p*l*c,v=0;v<l;v++)for(var k=v*(p*c),g=0;g<p;g++)for(var b=g*c,I=0;I<c;I++){var y=Math.round(p-g-1),S=m+k+b+I,T=h[S];if(y>=0&&y<p)T=h[m+k+y*c+I];o[S]=T}return{dataId:i.write(o,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}};var Cr={kernelName:a.FusedConv2D,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.x,i=a.filter,o=a.bias,s=a.preluActivationWeights,u=r.strides,d=r.pad,l=r.dataFormat,p=r.dilations,c=r.dimRoundingMode,h=r.activation,f=r.leakyreluAlpha,m=Gt({inputs:{x:n,filter:i},backend:t,attrs:{strides:u,pad:d,dataFormat:l,dilations:p,dimRoundingMode:c}});if(o){var v=m;if("NCHW"===l&&1===o.shape.length&&1!==o.shape[0]){var k=$a({inputs:{x:o},backend:t,attrs:{shape:[o.shape[0],1,1]}});m=E({inputs:{a:m,b:k},backend:t}),t.disposeIntermediateTensorInfo(k)}else m=E({inputs:{a:m,b:o},backend:t});t.disposeIntermediateTensorInfo(v)}if(h){v=m;if("NCHW"===l&&"prelu"===h&&1===s.shape.length&&1!==s.shape[0]){var g=$a({inputs:{x:s},backend:t,attrs:{shape:[s.shape[0],1,1]}});m=Xa(t,m,h,g,f),t.disposeIntermediateTensorInfo(g)}else m=Xa(t,m,h,s,f);t.disposeIntermediateTensorInfo(v)}return m}};var Hr={kernelName:a.FusedDepthwiseConv2D,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.x,i=a.filter,o=a.bias,s=a.preluActivationWeights,u=r.strides,d=r.pad,l=r.dataFormat,p=r.dilations,c=r.dimRoundingMode,h=r.activation,f=r.leakyreluAlpha,m=nr({inputs:{x:n,filter:i},backend:t,attrs:{strides:u,pad:d,dataFormat:l,dilations:p,dimRoundingMode:c}});if(o){var v=m;m=E({inputs:{a:m,b:o},backend:t}),t.disposeIntermediateTensorInfo(v)}if(h){v=m;m=Xa(t,m,h,s,f),t.disposeIntermediateTensorInfo(v)}return m}};var Or={kernelName:a.GatherNd,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=t.params,i=t.indices,o=a.util.sizeFromShape(n.shape),s=i.shape,u=s[s.length-1],l=d(a.backend_util.prepareAndValidate(n,i),4),p=l[0],c=l[1],h=l[2],f=l[3];if(0===c)return r.makeTensorInfo(p,n.dtype,[]);var m=oe(r.data.get(i.dataId).values,r.bufferSync(n),n.dtype,c,u,h,f,n.shape,o);return r.makeTensorInfo(p,n.dtype,m.values)}};var Br={kernelName:a.GatherV2,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.indices,s=n.axis,u=n.batchDims;p([i,o],"gatherV2");for(var d=a.util.parseAxisParam(s,i.shape)[0],l=r.data.get(o.dataId).values,c=i.shape[d],h=function(e){var t=l[e];a.util.assert(t<=c-1&&t>=0,(function(){return"GatherV2: the index value ".concat(t," is not in [0, ").concat(c-1,"]")}))},f=0;f<l.length;++f)h(f);var m=u;null==u&&(m=0);var v=a.util.sizeFromShape(o.shape),k=a.backend_util.segment_util.collectGatherOpShapeInfo(i,o,d,m),g=$a({inputs:{x:i},backend:r,attrs:{shape:[k.batchSize,k.outerSize,k.dimSize,k.sliceSize]}}),b=$a({inputs:{x:o},backend:r,attrs:{shape:[k.batchSize,v/k.batchSize]}}),I=[k.batchSize,k.outerSize,v/k.batchSize,k.sliceSize],y=r.bufferSync(b),S=se(r.bufferSync(g),y,I);return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(b),r.makeTensorInfo(k.outputShape,S.dtype,S.values)}};var Vr={kernelName:a.IFFT,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=t.input,i=a.util.sizeFromShape(n.shape),o=n.shape[n.shape.length-1],s=$a({inputs:{x:n},backend:r,attrs:{shape:[i/o,o]}}),u=Dr(s,!0,r),d=$a({inputs:{x:u},backend:r,attrs:{shape:n.shape}});return r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(u),d}},Gr=O(a.IsFinite,(function(e){return Number.isFinite(e)?1:0}),"bool"),Lr={kernelName:a.IsFinite,backendName:"cpu",kernelFunc:Gr},qr=O(a.IsInf,(function(e){return Math.abs(e)===1/0?1:0}),"bool"),Ur={kernelName:a.IsInf,backendName:"cpu",kernelFunc:qr},Zr=O(a.IsNan,(function(e){return Number.isNaN(e)?1:0}),"bool"),jr={kernelName:a.IsNan,backendName:"cpu",kernelFunc:Zr};var Kr={kernelName:a.LinSpace,backendName:"cpu",kernelFunc:function(e){var a=e.backend,t=e.attrs,r=Ie(t.start,t.stop,t.num);return a.makeTensorInfo([r.length],"float32",r)}},Yr=O(a.Log1p,(function(e){return Math.log1p(e)})),Jr={kernelName:a.Log1p,backendName:"cpu",kernelFunc:Yr},Qr=v((function(e,a){return e&&a})),Xr=w(a.LogicalAnd,Qr,null,"bool"),$r={kernelName:a.LogicalAnd,backendName:"cpu",kernelFunc:Xr},en=O(a.LogicalNot,(function(e){return e?0:1}),"bool"),an={kernelName:a.LogicalNot,backendName:"cpu",kernelFunc:en},tn=v((function(e,a){return e||a})),rn=w(a.LogicalOr,tn,null,"bool"),nn={kernelName:a.LogicalOr,backendName:"cpu",kernelFunc:rn};var on={kernelName:a.LRN,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.depthRadius,s=n.bias,u=n.alpha,d=n.beta;p(i,"LRN");var l=i.shape[3],c=l-1,h=r.data.get(i.dataId).values,f=a.util.sizeFromShape(i.shape),m=new Float32Array(f);function v(e){for(var a=e%l,t=e-a+Math.max(0,a-o),r=e-a+Math.min(a+o,c),n=0;t<=r;t++){var i=h[t];n+=i*i}return n}for(var k=0;k<f;k++){var g=v(k),b=h[k]*Math.pow(s+u*g,-d);m[k]=b}return r.makeTensorInfo(i.shape,i.dtype,m)}};var sn={kernelName:a.LRNGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=t.y,s=t.dy,u=n.depthRadius,d=n.bias,l=n.alpha,c=n.beta;p(s,"LRNGrad");for(var h=a.util.sizeFromShape(s.shape),f=s.shape[3],m=r.data.get(s.dataId).values,v=r.data.get(i.dataId).values,k=r.data.get(o.dataId).values,g=new Float32Array(h),b=h,I=0;I<b;I++){for(var y=I%f,S=I-y+Math.max(0,y-u),T=I-y+Math.min(f,y+u+1),N=0,x=S;x<T;x++)N+=Math.pow(v[x],2);N=l*N+d;for(x=S;x<T;x++){var F=-2*l*c*v[x]*k[I]/N;I===x&&(F+=Math.pow(N,-c)),F*=m[I],g[x]+=F}}return r.makeTensorInfo(s.shape,i.dtype,g)}};function un(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.reductionIndices,s=n.keepDims,u=r,l=i.shape,c=l.length,h=a.util.parseAxisParam(o,l),f=h,m=a.backend_util.getAxesPermutation(f,c),v=u.data.get(i.dataId).values;if(null!=m){for(var k=new Array(c),g=0;g<k.length;g++)k[g]=l[m[g]];v=Be(v,l,i.dtype,m,k),f=a.backend_util.getInnerMostAxes(f.length,c),l=k}p(i,"max"),a.backend_util.assertAxesAreInnerMostDims("max",f,c);var b=d(a.backend_util.computeOutAndReduceShapes(l,f),2),I=b[0],y=b[1],S=Ne(v,a.util.sizeFromShape(y),I,i.dtype),T=u.write(S,I,i.dtype),N=I;s&&(N=k=a.backend_util.expandShapeToKeepDim(I,h));return{dataId:T,shape:N,dtype:i.dtype}}var dn={kernelName:a.Max,backendName:"cpu",kernelFunc:un};var ln={kernelName:a.MaxPool,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x;p(i,"maxPool");var o=n.filterSize,s=n.strides,u=n.pad,d=n.dimRoundingMode;a.util.assert(a.backend_util.eitherStridesOrDilationsAreOne(s,1),(function(){return"Error in maxPool: Either strides or dilations must be 1. "+"Got strides ".concat(s," and dilations '").concat(1,"'")}));var l,c=a.backend_util.computePool2DInfo(i.shape,o,s,1,u,d);if(1===c.filterWidth&&1===c.filterHeight&&a.util.arraysEqual(c.inShape,c.outShape))l=I({inputs:{x:i},backend:r});else{var h=r.data.get(i.dataId).values,f=a.util.computeStrides(i.shape),m=Nt(h,i.shape,i.dtype,f,c,"max");l=r.makeTensorInfo(c.outShape,i.dtype,m.values)}return l}};var pn={kernelName:a.MaxPool3D,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.filterSize,s=n.strides,u=n.pad,d=n.dimRoundingMode,l=n.dataFormat;p(i,"maxPool3d");var c=a.backend_util.computePool3DInfo(i.shape,o,s,1,u,d,l),h=Ft(r.data.get(i.dataId).values,i.shape,i.dtype,a.util.computeStrides(i.shape),c,"max");return r.makeTensorInfo(h.shape,"float32",h.values)}};var cn={kernelName:a.MaxPool3DGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.dy,o=t.input,s=n.filterSize,u=n.strides,d=n.pad,l=n.dimRoundingMode;p([i,o],"maxPool3DGrad");for(var c=a.backend_util.computePool3DInfo(o.shape,s,u,1,d,l),h=function(e,t){for(var r=a.buffer(t.outShape,"int32"),n=t.strideDepth,i=t.strideHeight,o=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,d=t.dilationWidth,l=t.effectiveFilterDepth,p=t.effectiveFilterHeight,c=t.effectiveFilterWidth,h=t.padInfo.front,f=t.padInfo.top,m=t.padInfo.left,v=0;v<t.batchSize;++v)for(var k=0;k<t.inChannels;++k)for(var g=0;g<t.outDepth;++g){for(var b=g*n-h,I=b;I<0;)I+=s;for(var y=Math.min(t.inDepth,l+b),S=0;S<t.outHeight;++S){for(var T=S*i-f,N=T;N<0;)N+=u;for(var x=Math.min(t.inHeight,p+T),F=0;F<t.outWidth;++F){for(var w=F*o-m,M=w;M<0;)M+=d;for(var A=Math.min(t.inWidth,c+w),D=Number.NEGATIVE_INFINITY,E=-1,_=I;_<y;_+=s)for(var z=_-b,R=N;R<x;R+=u)for(var W=R-T,P=M;P<A;P+=d){var C=P-w,H=e.get(v,_,R,P,k);H>=D&&(D=H,E=z*p*c+W*p+C)}r.set(E,v,g,S,F,k)}}}return r}(r.bufferSync(o),c),f=c.strideDepth,m=c.strideHeight,v=c.strideWidth,k=c.dilationDepth,g=c.dilationHeight,b=c.dilationWidth,I=c.effectiveFilterDepth,y=c.effectiveFilterHeight,S=c.effectiveFilterWidth,T=I-1-c.padInfo.front,N=S-1-c.padInfo.left,x=y-1-c.padInfo.top,F=a.buffer(o.shape,"float32"),w=r.bufferSync(i),M=0;M<c.batchSize;++M)for(var A=0;A<c.inChannels;++A)for(var D=0;D<c.inDepth;++D)for(var E=0;E<c.inHeight;++E)for(var _=0;_<c.inWidth;++_){for(var z=D-T,R=E-x,W=_-N,P=0,C=0;C<I;C+=k){var H=(z+C)/f;if(!(H<0||H>=c.outDepth||Math.floor(H)!==H))for(var O=0;O<y;O+=g){var B=(R+O)/m;if(!(B<0||B>=c.outHeight||Math.floor(B)!==B))for(var V=0;V<S;V+=b){var G=(W+V)/v;if(!(G<0||G>=c.outWidth||Math.floor(G)!==G)){var L=I*y*S-1-h.get(M,H,B,G,A)===C*y*S+O*S+V?1:0;if(0!==L)P+=w.get(M,H,B,G,A)*L}}}}F.set(P,M,D,E,_,A)}return r.makeTensorInfo(F.shape,F.dtype,F.values)}};var hn={kernelName:a.MaxPoolGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.dy,o=t.input,s=o;p([o,t.output],"maxPoolGrad");for(var u=n.filterSize,d=n.strides,l=n.pad,c=n.dimRoundingMode,h=a.backend_util.computePool2DInfo(s.shape,u,d,1,l,c),f=r.data.get(s.dataId).values,m=a.buffer(h.outShape,s.dtype,xt(f,s.shape,s.dtype,h).values),v=h.strideHeight,k=h.strideWidth,g=h.dilationHeight,b=h.dilationWidth,I=h.effectiveFilterHeight,y=h.effectiveFilterWidth,S=y-1-h.padInfo.left,T=I-1-h.padInfo.top,N=a.buffer(s.shape,"float32"),x=r.data.get(i.dataId).values,F=a.buffer(i.shape,"float32",x),w=0;w<h.batchSize;++w)for(var M=0;M<h.inChannels;++M)for(var A=0;A<h.inHeight;++A)for(var D=0;D<h.inWidth;++D){for(var E=A-T,_=D-S,z=0,R=0;R<I;R+=g){var W=(E+R)/v;if(!(W<0||W>=h.outHeight||Math.floor(W)!==W))for(var P=0;P<y;P+=b){var C=(_+P)/k;if(!(C<0||C>=h.outWidth||Math.floor(C)!==C)){var H=I*y-1-m.get(w,W,C,M)===R*y+P?1:0;if(0!==H)z+=F.get(w,W,C,M)*H}}}N.set(z,w,A,D,M)}return r.makeTensorInfo(N.shape,N.dtype,N.values)}};var fn={kernelName:a.MaxPoolWithArgmax,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.attrs,n=e.backend,i=t.x,o=r.filterSize,s=r.strides,u=r.pad,l=r.includeBatchInIndex,c=n;p(i,"MaxPoolWithArgmax");var h=c.data.get(i.dataId).values,f=a.backend_util.computePool2DInfo(i.shape,o,s,[1,1],u),m=d(function(e,t,r,n,i){var o=Nt(e,0,r,a.util.computeStrides(t),i,"max"),s=xt(e,t,r,i,!0,n);return[o.values,s.values]}(h,i.shape,i.dtype,l,f),2),v=m[0],k=m[1],g=c.write(v,f.outShape,i.dtype),b=c.write(k,f.outShape,i.dtype);return[{dataId:g,shape:f.outShape,dtype:i.dtype},{dataId:b,shape:f.outShape,dtype:"int32"}]}};var mn={kernelName:a.Mean,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis,s=n.keepDims,u=a.util.parseAxisParam(o,i.shape),d=a.backend_util.computeOutAndReduceShapes(i.shape,u)[1],l=a.util.sizeFromShape(d),p=[],c=r.makeTensorInfo([],"float32",new Float32Array([l]));p.push(c);var h=x({inputs:{x:i},backend:r,attrs:{dtype:"float32"}});p.push(h);var f=Mr({inputs:{a:h,b:c},backend:r});p.push(f);var m=hr({inputs:{x:f},backend:r,attrs:{axis:o,keepDims:s}});return p.forEach((function(e){return r.disposeIntermediateTensorInfo(e)})),m}};var vn={kernelName:a.Min,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.axis,s=n.keepDims;p(i,"min");var u=a.util.parseAxisParam(o,i.shape),l=u,c=a.backend_util.getAxesPermutation(l,i.shape.length),h=i;null!=c&&(h=Ve({inputs:{x:i},backend:r,attrs:{perm:c}}),l=a.backend_util.getInnerMostAxes(l.length,i.shape.length)),a.backend_util.assertAxesAreInnerMostDims("min",l,h.shape.length);for(var f=d(a.backend_util.computeOutAndReduceShapes(h.shape,l),2),m=f[0],v=f[1],k=a.util.sizeFromShape(v),g=a.util.makeZerosTypedArray(a.util.sizeFromShape(m),h.dtype),b=r.data.get(h.dataId).values,I=0;I<g.length;++I){for(var y=I*k,S=b[y],T=0;T<k;++T){var N=b[y+T];(Number.isNaN(N)||N<S)&&(S=N)}g[I]=S}null!=c&&r.disposeIntermediateTensorInfo(h);var x=r.makeTensorInfo(m,h.dtype,g);if(s){var F=$a({inputs:{x:x},backend:r,attrs:{shape:a.backend_util.expandShapeToKeepDim(m,u)}});return r.disposeIntermediateTensorInfo(x),F}return x}};var kn={kernelName:a.MirrorPad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.paddings,s=n.mode;p(i,"mirrorPad");for(var u=o.map((function(e,a){return e[0]+i.shape[a]+e[1]})),d=o.map((function(e){return e[0]})),l=o.map((function(e,a){return e[0]+i.shape[a]})),c="reflect"===s?0:1,h=r.data.get(i.dataId).values,f=i.shape.length,m=a.util.computeStrides(i.shape),v=a.util.sizeFromShape(u),k=u.length,g=a.util.computeStrides(u),b=a.util.getTypedArrayFromDType(i.dtype,v),I=0;I<v;I++){for(var y=a.util.indexToLoc(I,k,g),S=0;S<k;S++)y[S]<d[S]?y[S]=2*d[S]-y[S]-c:y[S]>=l[S]&&(y[S]=2*(l[S]-1)-y[S]+c);y=y.map((function(e,a){return e-d[a]}));var T=a.util.locToIndex(y,f,m);b[I]=h[T]}return{dataId:r.write(b,u,i.dtype),shape:u,dtype:i.dtype}}},gn=v((function(e,a){var t=e%a;return e<0&&a<0||e>=0&&a>=0?t:(t+a)%a})),bn=w(a.Mod,gn),In={kernelName:a.Mod,backendName:"cpu",kernelFunc:bn};function yn(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.logits,o=n.dim,s=i.shape.length,u=o;if(-1===u&&(u=s-1),u!==s-1)throw Error("Softmax along a non-last dimension is not yet supported. 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l=d?o:yn({inputs:{logits:o},backend:r,attrs:{dim:-1}}),c=l.shape[0],h=l.shape[1],f=r.data.get(l.dataId).values,m=[c,s],v=a.util.makeZerosTypedArray(a.util.sizeFromShape(m),"int32"),k=0;k<c;++k){var g=k*h,b=new Float32Array(h-1);b[0]=f[g];for(var I=1;I<b.length;++I)b[I]=b[I-1]+f[g+I];for(var y=n.alea(u.toString()),S=k*s,T=0;T<s;++T){var N=y();v[S+T]=b.length;for(I=0;I<b.length;I++)if(N<b[I]){v[S+T]=I;break}}}return d||r.disposeIntermediateTensorInfo(l),r.makeTensorInfo(m,"int32",v)}},Nn=a.kernel_impls.nonMaxSuppressionV3Impl;var xn={kernelName:a.NonMaxSuppressionV3,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.boxes,i=a.scores,o=r.maxOutputSize,s=r.iouThreshold,u=r.scoreThreshold;p(n,"NonMaxSuppression");var d=t.data.get(n.dataId).values,l=t.data.get(i.dataId).values,c=Nn(d,l,o,s,u).selectedIndices;return t.makeTensorInfo([c.length],"int32",new Int32Array(c))}},Fn=a.kernel_impls.nonMaxSuppressionV4Impl;var wn={kernelName:a.NonMaxSuppressionV4,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.boxes,i=a.scores,o=r.maxOutputSize,s=r.iouThreshold,u=r.scoreThreshold,d=r.padToMaxOutputSize;p(n,"NonMaxSuppressionPadded");var l=t.data.get(n.dataId).values,c=t.data.get(i.dataId).values,h=Fn(l,c,o,s,u,d),f=h.selectedIndices,m=h.validOutputs;return[t.makeTensorInfo([f.length],"int32",new Int32Array(f)),t.makeTensorInfo([],"int32",new Int32Array([m]))]}},Mn=a.kernel_impls.nonMaxSuppressionV5Impl;var An={kernelName:a.NonMaxSuppressionV5,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.boxes,i=a.scores,o=r.maxOutputSize,s=r.iouThreshold,u=r.scoreThreshold,d=r.softNmsSigma;p(n,"NonMaxSuppressionWithScore");var l=t.data.get(n.dataId).values,c=t.data.get(i.dataId).values,h=Mn(l,c,o,s,u,d),f=h.selectedIndices,m=h.selectedScores;return[t.makeTensorInfo([f.length],"int32",new Int32Array(f)),t.makeTensorInfo([m.length],"float32",new Float32Array(m))]}};var Dn={kernelName:a.OneHot,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.indices,o=n.dtype,s=n.depth,u=n.onValue,c=n.offValue;p(i,"oneHot");var h=a.util.sizeFromShape(i.shape),f=new Float32Array(h*s);f.fill(c);for(var m=r.data.get(i.dataId).values,v=0;v<h;++v)m[v]>=0&&m[v]<s&&(f[v*s+m[v]]=u);return r.makeTensorInfo(l(l([],d(i.shape),!1),[s],!1),o,f)}};function En(e){var a=e.inputs,t=e.backend,r=a.x;if("string"===r.dtype)throw new Error("zerosLike is not supported for string tensors");if("complex64"===r.dtype){var n=S({inputs:{input:r},backend:t}),i=En({inputs:{x:n},backend:t}),o=Ht({inputs:{input:r},backend:t}),s=En({inputs:{x:o},backend:t}),u=k({inputs:{real:i,imag:s},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(o),t.disposeIntermediateTensorInfo(s),u}return Rr({backend:t,attrs:{shape:r.shape,value:0,dtype:r.dtype}})}var _n={kernelName:a.ZerosLike,backendName:"cpu",kernelFunc:En};var zn={kernelName:a.OnesLike,backendName:"cpu",kernelFunc:function e(a){var t=a.inputs,r=a.backend,n=t.x;if("string"===n.dtype)throw new Error("onesLike is not supported for string tensors");if("complex64"===n.dtype){var i=S({inputs:{input:n},backend:r}),o=e({inputs:{x:i},backend:r}),s=Ht({inputs:{input:n},backend:r}),u=En({inputs:{x:s},backend:r}),d=k({inputs:{real:o,imag:u},backend:r});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(u),d}return Rr({backend:r,attrs:{shape:n.shape,value:1,dtype:n.dtype}})}};function Rn(e){var t=e.inputs,r=e.backend,n=e.attrs.axis;if(1===t.length)return xr({inputs:{input:t[0]},backend:r,attrs:{dim:n}});var i=t[0].shape,o=t[0].dtype;t.forEach((function(e){a.util.assertShapesMatch(i,e.shape,"All tensors passed to stack must have matching shapes"),a.util.assert(o===e.dtype,(function(){return"All 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a=e.inputs,t=e.backend,r=a.starts,n=a.limits,i=a.deltas,o=t.data.get(r.dataId).values,s=t.data.get(n.dataId).values,u=t.data.get(i.dataId).values,l=d(Je(o,r.shape,r.dtype,s,n.shape,u,i.shape),2),p=l[0],c=l[1];return[t.makeTensorInfo([p.length],"int32",p),t.makeTensorInfo([c.length],r.dtype,c)]}};var Gn={kernelName:a.RaggedTensorToTensor,backendName:"cpu",kernelFunc:function(e){var a=e.inputs,t=e.backend,r=e.attrs,n=a.shape,i=a.values,o=a.defaultValue,s=a.rowPartitionTensors,u=r.rowPartitionTypes,l=t.data.get(n.dataId).values,p=t.data.get(i.dataId).values,c=t.data.get(o.dataId).values,h=s.map((function(e){return t.data.get(e.dataId).values})),f=s.map((function(e){return e.shape})),m=d(aa(l,n.shape,p,i.shape,i.dtype,c,o.shape,h,f,u),2),v=m[0],k=m[1];return t.makeTensorInfo(v,i.dtype,k)}};var Ln={kernelName:a.Range,backendName:"cpu",kernelFunc:function(e){var a=e.backend,t=e.attrs,r=t.start,n=t.stop,i=t.dtype,o=ta(r,n,t.step,i);return a.makeTensorInfo([o.length],i,o)}},qn=O(a.Reciprocal,(function(e){return 1/e})),Un={kernelName:a.Reciprocal,backendName:"cpu",kernelFunc:qn};var Zn={kernelName:a.ResizeBilinear,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.images,o=n.alignCorners,s=n.halfPixelCenters,u=n.size;p(i,"resizeBilinear");for(var l=a.util.computeStrides(i.shape),c=d(u,2),h=c[0],f=c[1],m=d(i.shape,4),v=m[0],k=m[1],g=m[2],b=m[3],I=r.data.get(i.dataId).values,y=new Float32Array(a.util.sizeFromShape([v,h,f,b])),S=[o&&h>1?k-1:k,o&&f>1?g-1:g],T=[o&&h>1?h-1:h,o&&f>1?f-1:f],N=0,x=S[0]/T[0],F=S[1]/T[1],w=0;w<v;w++)for(var M=0;M<h;M++){var A=void 0;A=s?x*(M+.5)-.5:x*M;for(var D=Math.max(0,Math.floor(A)),E=A-D,_=Math.min(k-1,Math.ceil(A)),z=w*l[0]+D*l[1],R=w*l[0]+_*l[1],W=0;W<f;W++){var P=void 0;P=s?F*(W+.5)-.5:F*W;for(var C=Math.max(0,Math.floor(P)),H=P-C,O=Math.min(g-1,Math.ceil(P)),B=z+C*l[2],V=R+C*l[2],G=z+O*l[2],L=R+O*l[2],q=0;q<b;q++){var U=I[B+q],Z=I[V+q],j=U+(I[G+q]-U)*H,K=j+(Z+(I[L+q]-Z)*H-j)*E;y[N++]=K}}}return r.makeTensorInfo([v,h,f,b],"float32",y)}};var jn={kernelName:a.ResizeBilinearGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.images,o=t.dy,s=n.alignCorners;p([o,i],"resizeBilinearGrad");for(var u=a.util.computeStrides(i.shape),l=d(i.shape,4),c=l[0],h=l[1],f=l[2],m=l[3],v=d(o.shape,3),k=v[1],g=v[2],b=new Float32Array(c*h*f*m),I=[s&&k>1?h-1:h,s&&g>1?f-1:f],y=[s&&k>1?k-1:k,s&&g>1?g-1:g],S=I[0]/y[0],T=I[1]/y[1],N=r.data.get(o.dataId).values,x=0,F=0;F<c;F++)for(var w=F*u[0],M=0;M<k;M++)for(var A=M*S,D=Math.floor(A),E=Math.min(Math.ceil(A),h-1),_=w+D*u[1],z=w+E*u[1],R=A-D,W=1-R,P=0;P<g;P++)for(var C=P*T,H=Math.floor(C),O=Math.min(Math.ceil(C),f-1),B=C-H,V=1-B,G=_+H*u[2],L=_+O*u[2],q=z+H*u[2],U=z+O*u[2],Z=W*V,j=W*B,K=R*V,Y=R*B,J=0;J<m;J++){var Q=N[x++];b[G+J]+=Q*Z,b[L+J]+=Q*j,b[q+J]+=Q*K,b[U+J]+=Q*Y}return r.makeTensorInfo([c,f,h,m],"float32",b)}};var Kn={kernelName:a.ResizeNearestNeighbor,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.images,o=n.alignCorners,s=n.halfPixelCenters,u=n.size;p(i,"resizeNearestNeighbor");for(var l=a.util.computeStrides(i.shape),c=d(u,2),h=c[0],f=c[1],m=d(i.shape,4),v=m[0],k=m[1],g=m[2],b=m[3],I=r.data.get(i.dataId).values,y=new Float32Array(v*h*f*b),S=[o&&h>1?k-1:k,o&&f>1?g-1:g],T=[o&&h>1?h-1:h,o&&f>1?f-1:f],N=S[0]/T[0],x=S[1]/T[1],F=0,w=0;w<v;w++)for(var M=w*l[0],A=0;A<h;A++){var D=s?N*(A+.5):N*A,E=Math.min(k-1,o?Math.round(D):Math.floor(D));s&&(E=Math.max(0,E));for(var _=M+E*l[1],z=0;z<f;z++){var R=s?x*(z+.5):x*z,W=Math.min(g-1,o?Math.round(R):Math.floor(R));s&&(W=Math.max(0,W));for(var P=_+W*l[2],C=0;C<b;C++){var H=I[P+C];y[F++]=H}}}return r.makeTensorInfo([v,h,f,b],i.dtype,y)}};var Yn={kernelName:a.ResizeNearestNeighborGrad,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.images,o=t.dy,s=n.alignCorners;p([o,i],"resizeNearestNeighborGrad");for(var u=a.util.computeStrides(i.shape),l=a.util.computeStrides(o.shape),c=d(i.shape,4),h=c[0],f=c[1],m=c[2],v=c[3],k=d(o.shape,3),g=k[1],b=k[2],I=new Float32Array(h*f*m*v),y=r.data.get(o.dataId).values,S=[s&&g>1?f-1:f,s&&b>1?m-1:m],T=[s&&g>1?g-1:g,s&&b>1?b-1:b],N=S[0]/T[0],x=S[1]/T[1],F=1/N,w=1/x,M=2*Math.ceil(F)+2,A=2*Math.ceil(w)+2,D=0;D<h;D++)for(var E=D*u[0],_=0;_<f;_++)for(var z=E+_*u[1],R=Math.floor(_*F),W=Math.floor(R-M/2),P=0;P<m;P++)for(var C=z+P*u[2],H=Math.floor(P*w),O=Math.floor(H-A/2),B=0;B<v;B++){for(var V=0,G=0;G<M;G++){var L=G+W;if(!(L<0||L>=g)){var q=E+L*l[1],U=L*N;if(_===Math.min(f-1,s?Math.round(U):Math.floor(U)))for(var Z=0;Z<A;Z++){var j=Z+O;if(!(j<0||j>=b)){var K=q+j*l[2],Y=j*x;P===Math.min(m-1,s?Math.round(Y):Math.floor(Y))&&(V+=y[K+B])}}}}I[C+B]=V}return r.makeTensorInfo(i.shape,i.dtype,I)}};var Jn={kernelName:a.Reverse,backendName:"cpu",kernelFunc:function(e){var t=e.inputs,r=e.backend,n=e.attrs,i=t.x,o=n.dims;p(i,"reverse");var s=i.shape.length,u=a.util.parseAxisParam(o,i.shape);if(0===s)return I({inputs:{x:i},backend:r});for(var c=new a.TensorBuffer(i.shape,i.dtype),h=r.bufferSync(i),f=function(e){var a=c.indexToLoc(e),t=a.slice();u.forEach((function(e){return t[e]=i.shape[e]-1-t[e]})),c.set.apply(c,l([h.get.apply(h,l([],d(t),!1))],d(a),!1))},m=0;m<c.size;m++)f(m);return r.makeTensorInfo(c.shape,c.dtype,c.values)}},Qn={kernelName:a.RotateWithOffset,backendName:"cpu",kernelFunc:function(e){for(var t=e.inputs,r=e.attrs,n=e.backend,i=t.image,o=r.radians,s=r.fillValue,u=r.center,l=n,p=a.util.getTypedArrayFromDType(i.dtype,a.util.sizeFromShape(i.shape)),c=d(i.shape,4),h=c[0],f=c[1],m=c[2],v=c[3],k=d(a.backend_util.getImageCenter(u,f,m),2),g=k[0],b=k[1],I=Math.sin(o),y=Math.cos(o),S=l.data.get(i.dataId).values,T=0;T<h;T++)for(var N=T*m*f*v,x=0;x<f;x++)for(var 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