diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0b52ac54..2d942256 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,7 +9,10 @@
   
 ## Changelog
   
-### **HEAD -> main** 2022/04/11 mandic00@live.com
+### **HEAD -> main** 2022/04/14 mandic00@live.com
+
+
+### **origin/main** 2022/04/11 mandic00@live.com
 
 - face attention model is available in human-models
 - beta release 2.7
diff --git a/TODO.md b/TODO.md
index 65487fbb..79d16580 100644
--- a/TODO.md
+++ b/TODO.md
@@ -2,7 +2,18 @@
 
 ## Work in Progress
 
-<br>
+Release 2.7:
+- add MediaPipe FaceMesh attention model  
+  model is available in @vladmandic/human-models repository  
+  to enable, set `config.face.attention = true`  
+  model replaces iris and eyes keypoints with high-detailed ones  
+  and adds additional keypoints for lips  
+- changed default face crop from 120% to 140%  
+  to better utilize caching between frames  
+- refactor draw methods into separate modules  
+- add **ElectronJS** demo  
+  see <https://github.com/vladmandic/human-electron>  
+- update build platform and dependencies  
 
 ### Exploring
 
diff --git a/dist/human.esm.js b/dist/human.esm.js
index 35382d2b..d38874ce 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -219,524 +219,524 @@ var config = {
 // dist/tfjs.esm.js
 var tfjs_esm_exports = {};
 __export(tfjs_esm_exports, {
-  Abs: () => io,
-  Acos: () => sl,
-  Acosh: () => rl,
-  AdadeltaOptimizer: () => fb,
-  AdagradOptimizer: () => mb,
-  AdamOptimizer: () => gb,
-  AdamaxOptimizer: () => bb,
-  Add: () => Ir,
-  AddN: () => ka,
-  All: () => al,
-  Any: () => il,
-  ArgMax: () => Ia,
-  ArgMin: () => ol,
-  Asin: () => ul,
-  Asinh: () => ll,
-  Atan: () => cl,
-  Atan2: () => pl,
-  Atanh: () => dl,
-  AvgPool: () => Sa,
-  AvgPool3D: () => qd,
-  AvgPool3DGrad: () => og,
-  AvgPoolGrad: () => ig,
-  BackendWasm: () => kde,
-  BatchMatMul: () => Ca,
-  BatchToSpaceND: () => oo,
-  Bincount: () => ug,
-  BroadcastArgs: () => lg,
-  BroadcastTo: () => A$,
-  Callback: () => CW,
-  CallbackList: () => zL,
-  Cast: () => Na,
-  Ceil: () => Ta,
-  ClipByValue: () => Sr,
-  Complex: () => jd,
-  ComplexAbs: () => Kd,
-  Concat: () => uo,
-  Conv2D: () => $a,
-  Conv2DBackpropFilter: () => cg,
-  Conv2DBackpropInput: () => _a,
-  Conv3D: () => Xd,
-  Conv3DBackpropFilterV2: () => dg,
-  Conv3DBackpropInputV2: () => pg,
-  Cos: () => Aa,
-  Cosh: () => Ea,
-  CropAndResize: () => co,
-  Cumprod: () => hl,
-  Cumsum: () => lo,
-  CustomCallback: () => BL,
-  DataStorage: () => Ud,
-  DenseBincount: () => hg,
-  DepthToSpace: () => po,
-  DepthwiseConv2dNative: () => Ra,
-  DepthwiseConv2dNativeBackpropFilter: () => fg,
-  DepthwiseConv2dNativeBackpropInput: () => mg,
-  Diag: () => gg,
-  Dilation2D: () => Yd,
-  Dilation2DBackpropFilter: () => Qf,
-  Dilation2DBackpropInput: () => Yf,
-  ENV: () => Jw,
-  EarlyStopping: () => NW,
-  Einsum: () => Qd,
-  Elu: () => Fa,
-  EluGrad: () => bg,
-  Environment: () => S$,
-  Equal: () => ho,
+  Abs: () => uo,
+  Acos: () => rl,
+  Acosh: () => al,
+  AdadeltaOptimizer: () => gb,
+  AdagradOptimizer: () => bb,
+  AdamOptimizer: () => yb,
+  AdamaxOptimizer: () => vb,
+  Add: () => Sr,
+  AddN: () => Ia,
+  All: () => il,
+  Any: () => ol,
+  ArgMax: () => Sa,
+  ArgMin: () => ul,
+  Asin: () => ll,
+  Asinh: () => cl,
+  Atan: () => dl,
+  Atan2: () => hl,
+  Atanh: () => pl,
+  AvgPool: () => Ca,
+  AvgPool3D: () => Kd,
+  AvgPool3DGrad: () => lg,
+  AvgPoolGrad: () => ug,
+  BackendWasm: () => Pde,
+  BatchMatMul: () => Na,
+  BatchToSpaceND: () => lo,
+  Bincount: () => cg,
+  BroadcastArgs: () => dg,
+  BroadcastTo: () => P$,
+  Callback: () => OW,
+  CallbackList: () => QL,
+  Cast: () => Ta,
+  Ceil: () => $a,
+  ClipByValue: () => Cr,
+  Complex: () => Xd,
+  ComplexAbs: () => Yd,
+  Concat: () => co,
+  Conv2D: () => _a,
+  Conv2DBackpropFilter: () => pg,
+  Conv2DBackpropInput: () => Aa,
+  Conv3D: () => Qd,
+  Conv3DBackpropFilterV2: () => hg,
+  Conv3DBackpropInputV2: () => fg,
+  Cos: () => Ea,
+  Cosh: () => Ra,
+  CropAndResize: () => ho,
+  Cumprod: () => po,
+  Cumsum: () => Da,
+  CustomCallback: () => eB,
+  DataStorage: () => Hd,
+  DenseBincount: () => mg,
+  DepthToSpace: () => fo,
+  DepthwiseConv2dNative: () => Fa,
+  DepthwiseConv2dNativeBackpropFilter: () => gg,
+  DepthwiseConv2dNativeBackpropInput: () => bg,
+  Diag: () => yg,
+  Dilation2D: () => Zd,
+  Dilation2DBackpropFilter: () => Zf,
+  Dilation2DBackpropInput: () => Qf,
+  ENV: () => sk,
+  EarlyStopping: () => PW,
+  Einsum: () => Jd,
+  Elu: () => Pa,
+  EluGrad: () => vg,
+  Environment: () => A$,
+  Equal: () => mo,
   Erf: () => fl,
-  Exp: () => Oa,
-  ExpandDims: () => fo,
-  Expm1: () => mo,
-  FFT: () => yg,
+  Exp: () => za,
+  ExpandDims: () => go,
+  Expm1: () => bo,
+  FFT: () => xg,
   Fill: () => ml,
-  FlipLeftRight: () => go,
-  Floor: () => Pa,
-  FloorDiv: () => za,
+  FlipLeftRight: () => yo,
+  Floor: () => Ma,
+  FloorDiv: () => La,
   FromPixels: () => fd,
-  FusedBatchNorm: () => Ma,
-  FusedConv2D: () => aa,
-  FusedDepthwiseConv2D: () => ia,
-  GPGPUContext: () => Kf,
-  GatherNd: () => yo,
-  GatherV2: () => bo,
-  GraphModel: () => E4,
-  Greater: () => vo,
-  GreaterEqual: () => La,
-  History: () => LL,
-  IFFT: () => vg,
-  Identity: () => Ba,
-  Imag: () => Zd,
+  FusedBatchNorm: () => Ba,
+  FusedConv2D: () => ia,
+  FusedDepthwiseConv2D: () => oa,
+  GPGPUContext: () => Xf,
+  GatherNd: () => xo,
+  GatherV2: () => vo,
+  GraphModel: () => V4,
+  Greater: () => wo,
+  GreaterEqual: () => Va,
+  History: () => JL,
+  IFFT: () => wg,
+  Identity: () => Wa,
+  Imag: () => ep,
   InputSpec: () => Dt,
   IsFinite: () => gl,
   IsInf: () => bl,
   IsNan: () => yl,
-  KernelBackend: () => nl,
-  LRN: () => ep,
-  LRNGrad: () => wg,
-  LayerVariable: () => AL,
-  LayersModel: () => pr,
-  LeakyRelu: () => Va,
-  Less: () => xo,
-  LessEqual: () => wo,
-  LinSpace: () => xg,
-  Log: () => Wa,
+  KernelBackend: () => sl,
+  LRN: () => np,
+  LRNGrad: () => Ig,
+  LayerVariable: () => pz,
+  LayersModel: () => hr,
+  LeakyRelu: () => Ua,
+  Less: () => ko,
+  LessEqual: () => Io,
+  LinSpace: () => kg,
+  Log: () => Ga,
   Log1p: () => vl,
-  LogSoftmax: () => E$,
-  LogicalAnd: () => ko,
+  LogSoftmax: () => z$,
+  LogicalAnd: () => So,
   LogicalNot: () => xl,
-  LogicalOr: () => Jd,
-  MathBackendCPU: () => L0,
-  MathBackendWebGL: () => M1,
-  Max: () => Ua,
-  MaxPool: () => Ha,
-  MaxPool3D: () => tp,
-  MaxPool3DGrad: () => Ig,
-  MaxPoolGrad: () => kg,
-  MaxPoolWithArgmax: () => Sg,
-  Maximum: () => Ga,
-  Mean: () => qa,
-  Min: () => ja,
-  Minimum: () => Ka,
-  MirrorPad: () => Xa,
+  LogicalOr: () => tp,
+  MathBackendCPU: () => G0,
+  MathBackendWebGL: () => U1,
+  Max: () => Ha,
+  MaxPool: () => ja,
+  MaxPool3D: () => sp,
+  MaxPool3DGrad: () => Cg,
+  MaxPoolGrad: () => Sg,
+  MaxPoolWithArgmax: () => Ng,
+  Maximum: () => qa,
+  Mean: () => Ka,
+  Min: () => Xa,
+  Minimum: () => Ya,
+  MirrorPad: () => Qa,
   Mod: () => wl,
-  MomentumOptimizer: () => yb,
-  Multinomial: () => Cg,
-  Multiply: () => Ya,
-  Neg: () => Io,
-  NonMaxSuppressionV3: () => Co,
+  MomentumOptimizer: () => xb,
+  Multinomial: () => Tg,
+  Multiply: () => Za,
+  Neg: () => Co,
+  NonMaxSuppressionV3: () => To,
   NonMaxSuppressionV4: () => kl,
-  NonMaxSuppressionV5: () => No,
-  NotEqual: () => So,
-  OP_SCOPE_SUFFIX: () => l_,
-  OneHot: () => $o,
-  OnesLike: () => To,
-  Optimizer: () => _r,
-  OptimizerConstructors: () => Wr,
-  Pack: () => _o,
-  PadV2: () => Qa,
-  Pool: () => $de,
-  Pow: () => Za,
-  Prelu: () => Ja,
-  Prod: () => Ao,
-  RMSPropOptimizer: () => vb,
-  RNN: () => Ar,
+  NonMaxSuppressionV5: () => $o,
+  NotEqual: () => No,
+  OP_SCOPE_SUFFIX: () => m_,
+  OneHot: () => Ao,
+  OnesLike: () => _o,
+  Optimizer: () => Ar,
+  OptimizerConstructors: () => Ur,
+  Pack: () => Eo,
+  PadV2: () => Ja,
+  Pool: () => Wde,
+  Pow: () => ei,
+  Prelu: () => ti,
+  Prod: () => ni,
+  RMSPropOptimizer: () => wb,
+  RNN: () => Er,
   Range: () => Il,
-  Rank: () => e_,
-  Real: () => np,
-  RealDiv: () => Da,
+  Rank: () => i_,
+  Real: () => rp,
+  RealDiv: () => Oa,
   Reciprocal: () => Sl,
-  Reduction: () => oO,
-  Relu: () => ei,
-  Relu6: () => ni,
-  Reshape: () => Eo,
-  ResizeBilinear: () => ti,
-  ResizeBilinearGrad: () => Tg,
+  Reduction: () => pO,
+  Relu: () => si,
+  Relu6: () => ai,
+  Reshape: () => Ro,
+  ResizeBilinear: () => ri,
+  ResizeBilinearGrad: () => _g,
   ResizeNearestNeighbor: () => Cl,
-  ResizeNearestNeighborGrad: () => Ng,
-  Reverse: () => Ro,
-  RotateWithOffset: () => qo,
-  Round: () => Do,
-  Rsqrt: () => si,
-  SGDOptimizer: () => Ip,
-  ScatterNd: () => Fo,
-  Select: () => Oo,
+  ResizeNearestNeighborGrad: () => $g,
+  Reverse: () => Do,
+  RotateWithOffset: () => jo,
+  Round: () => Fo,
+  Rsqrt: () => ii,
+  SGDOptimizer: () => Cp,
+  ScatterNd: () => Oo,
+  Select: () => Po,
   Selu: () => Nl,
-  Sequential: () => Ub,
-  Sigmoid: () => ai,
+  Sequential: () => Hb,
+  Sigmoid: () => ui,
   Sign: () => Tl,
-  Sin: () => ri,
-  Sinh: () => zo,
-  Slice: () => Po,
-  Softmax: () => ui,
+  Sin: () => oi,
+  Sinh: () => Mo,
+  Slice: () => zo,
+  Softmax: () => di,
   Softplus: () => $l,
-  SpaceToBatchND: () => Mo,
-  SparseFillEmptyRows: () => sp,
+  SpaceToBatchND: () => Lo,
+  SparseFillEmptyRows: () => ap,
   SparseReshape: () => _l,
-  SparseSegmentMean: () => rp,
-  SparseSegmentSum: () => ap,
-  SparseToDense: () => ip,
-  SplitV: () => Lo,
-  Sqrt: () => ii,
+  SparseSegmentMean: () => ip,
+  SparseSegmentSum: () => op,
+  SparseToDense: () => up,
+  SplitV: () => Bo,
+  Sqrt: () => li,
   Square: () => Al,
-  SquaredDifference: () => li,
-  Step: () => hi,
-  StridedSlice: () => Bo,
-  StringNGrams: () => op,
-  StringSplit: () => $g,
-  StringToHashBucketFast: () => _g,
-  Sub: () => ci,
-  Sum: () => oi,
+  SquaredDifference: () => pi,
+  Step: () => gi,
+  StridedSlice: () => Vo,
+  StringNGrams: () => lp,
+  StringSplit: () => Ag,
+  StringToHashBucketFast: () => Eg,
+  Sub: () => hi,
+  Sum: () => ci,
   SymbolicTensor: () => $s,
-  Tan: () => Vo,
-  Tanh: () => di,
+  Tan: () => Wo,
+  Tanh: () => fi,
   Tensor: () => et,
   TensorBuffer: () => Vt,
-  Tile: () => Cr,
-  TopK: () => Wo,
-  Transform: () => Uo,
-  Transpose: () => pi,
-  Unique: () => Ag,
-  Unpack: () => Go,
-  UnsortedSegmentSum: () => up,
+  Tile: () => Nr,
+  TopK: () => Uo,
+  Transform: () => Go,
+  Transpose: () => mi,
+  Unique: () => Rg,
+  Unpack: () => Ho,
+  UnsortedSegmentSum: () => cp,
   Variable: () => gd,
-  ZerosLike: () => Ho,
-  _FusedMatMul: () => ra,
+  ZerosLike: () => qo,
+  _FusedMatMul: () => aa,
   abs: () => Mt,
-  acos: () => GA,
-  acosh: () => qA,
+  acos: () => YA,
+  acosh: () => ZA,
   add: () => ie,
-  addN: () => KA,
-  all: () => qk,
-  any: () => pm,
-  argMax: () => Hu,
-  argMin: () => JA,
-  asin: () => tE,
-  asinh: () => sE,
-  atan: () => aE,
-  atan2: () => oE,
-  atanh: () => lE,
-  avgPool: () => Gg,
-  avgPool3d: () => Yk,
-  backend: () => zA,
+  addN: () => eE,
+  all: () => Yk,
+  any: () => hm,
+  argMax: () => qu,
+  argMin: () => aE,
+  asin: () => oE,
+  asinh: () => lE,
+  atan: () => dE,
+  atan2: () => hE,
+  atanh: () => mE,
+  avgPool: () => qg,
+  avgPool3d: () => eI,
+  backend: () => UA,
   backend_util: () => S,
-  basicLSTMCell: () => qde,
-  batchNorm: () => ju,
-  batchNorm2d: () => $E,
-  batchNorm3d: () => AE,
-  batchNorm4d: () => RE,
-  batchToSpaceND: () => Hg,
-  bincount: () => Qk,
-  booleanMaskAsync: () => xpe,
-  broadcastArgs: () => OE,
+  basicLSTMCell: () => ipe,
+  batchNorm: () => Ku,
+  batchNorm2d: () => FE,
+  batchNorm3d: () => PE,
+  batchNorm4d: () => ME,
+  batchToSpaceND: () => jg,
+  bincount: () => tI,
+  booleanMaskAsync: () => Fpe,
+  broadcastArgs: () => VE,
   broadcastTo: () => nd,
-  broadcast_util: () => jo,
-  browser: () => Nk,
+  broadcast_util: () => Ko,
+  browser: () => Ak,
   buffer: () => De,
-  callbacks: () => Ape,
+  callbacks: () => Gpe,
   cast: () => ce,
-  ceil: () => ME,
+  ceil: () => GE,
   clipByValue: () => Vn,
-  clone: () => lr,
-  complex: () => oa,
+  clone: () => cr,
+  complex: () => ua,
   concat: () => Ft,
-  concat1d: () => VE,
-  concat2d: () => UE,
-  concat3d: () => HE,
-  concat4d: () => jE,
-  constraints: () => MM,
-  conv1d: () => Zk,
-  conv2d: () => ca,
-  conv2dTranspose: () => Jk,
-  conv3d: () => eI,
-  conv3dTranspose: () => tR,
-  copyRegisteredKernels: () => Ede,
-  cos: () => jg,
-  cosh: () => nI,
-  cosineWindow: () => TI,
-  cumprod: () => aR,
-  cumsum: () => sI,
+  concat1d: () => jE,
+  concat2d: () => XE,
+  concat3d: () => QE,
+  concat4d: () => JE,
+  constraints: () => $L,
+  conv1d: () => nI,
+  conv2d: () => da,
+  conv2dTranspose: () => sI,
+  conv3d: () => rI,
+  conv3dTranspose: () => oR,
+  copyRegisteredKernels: () => Hde,
+  cos: () => Xg,
+  cosh: () => iI,
+  cosineWindow: () => EI,
+  cumprod: () => mm,
+  cumsum: () => oI,
   customGrad: () => js,
-  data: () => R4,
-  denseBincount: () => uR,
-  deprecationWarn: () => Gk,
-  depthToSpace: () => cR,
-  depthwiseConv2d: () => hp,
-  deregisterOp: () => Rpe,
-  device_util: () => dp,
-  diag: () => jde,
-  dilation2d: () => fR,
-  disableDeprecationWarnings: () => Ode,
+  data: () => W4,
+  denseBincount: () => hR,
+  deprecationWarn: () => Kk,
+  depthToSpace: () => mR,
+  depthwiseConv2d: () => mp,
+  deregisterOp: () => qpe,
+  device_util: () => hp,
+  diag: () => ope,
+  dilation2d: () => vR,
+  disableDeprecationWarnings: () => Xde,
   dispose: () => Re,
-  disposeVariables: () => Pde,
+  disposeVariables: () => Yde,
   div: () => xe,
-  divNoNan: () => vR,
-  dot: () => Kde,
-  dropout: () => nF,
-  einsum: () => kR,
-  elu: () => fp,
-  enableDebugMode: () => Fde,
-  enableProdMode: () => Dde,
-  enclosingPowerOfTwo: () => sF,
+  divNoNan: () => SR,
+  dot: () => upe,
+  dropout: () => oF,
+  einsum: () => TR,
+  elu: () => gp,
+  enableDebugMode: () => Kde,
+  enableProdMode: () => jde,
+  enclosingPowerOfTwo: () => uF,
   engine: () => Ss,
   env: () => X,
-  equal: () => Kn,
-  erf: () => CR,
-  exp: () => Xn,
+  equal: () => Xn,
+  erf: () => AR,
+  exp: () => Yn,
   expandDims: () => Pn,
-  expm1: () => _R,
-  eye: () => rI,
-  fft: () => lb,
+  expm1: () => FR,
+  eye: () => uI,
+  fft: () => db,
   fill: () => Pl,
-  findBackend: () => Ude,
-  findBackendFactory: () => Gde,
-  floor: () => mp,
-  floorDiv: () => Hk,
-  forceHalfFloat: () => GX,
-  fused: () => ha,
-  gather: () => Ku,
-  gatherND: () => J3,
-  gather_util: () => $k,
-  getBackend: () => Vde,
-  getGradient: () => nx,
-  getKernel: () => Zf,
-  getKernelsForBackend: () => Jf,
-  getThreadsCount: () => Wpe,
-  gpgpu_util: () => RK,
-  grad: () => Qde,
-  grads: () => Zde,
-  greater: () => Wn,
-  greaterEqual: () => Ko,
+  findBackend: () => spe,
+  findBackendFactory: () => rpe,
+  floor: () => bp,
+  floorDiv: () => Xk,
+  forceHalfFloat: () => e8,
+  fused: () => fa,
+  gather: () => Xu,
+  gatherND: () => rF,
+  gather_util: () => Rk,
+  getBackend: () => tpe,
+  getGradient: () => rx,
+  getKernel: () => Jf,
+  getKernelsForBackend: () => em,
+  getThreadsCount: () => nhe,
+  gpgpu_util: () => WK,
+  grad: () => dpe,
+  grads: () => ppe,
+  greater: () => Un,
+  greaterEqual: () => Xo,
   ifft: () => Id,
-  imag: () => Kg,
-  image: () => qn,
-  inTopKAsync: () => kpe,
-  initializers: () => qM,
-  input: () => OB,
+  imag: () => Yg,
+  image: () => jn,
+  inTopKAsync: () => Ppe,
+  initializers: () => DL,
+  input: () => HB,
   io: () => En,
-  irfft: () => xI,
-  isFinite: () => Xde,
-  isInf: () => Yde,
-  isNaN: () => BR,
+  irfft: () => SI,
+  isFinite: () => lpe,
+  isInf: () => cpe,
+  isNaN: () => HR,
   keep: () => Ht,
   kernel_impls: () => xs,
-  layers: () => $L,
-  leakyRelu: () => Xg,
-  less: () => aI,
-  lessEqual: () => Xo,
-  linalg: () => VO,
-  linspace: () => GR,
-  loadGraphModel: () => Dpe,
-  loadLayersModel: () => $pe,
-  localResponseNormalization: () => qR,
-  log: () => Yn,
-  log1p: () => Yg,
-  logSigmoid: () => tpe,
-  logSoftmax: () => iI,
-  logSumExp: () => iD,
+  layers: () => XL,
+  leakyRelu: () => Qg,
+  less: () => lI,
+  lessEqual: () => Yo,
+  linalg: () => qO,
+  linspace: () => XR,
+  loadGraphModel: () => jpe,
+  loadLayersModel: () => Wpe,
+  localResponseNormalization: () => QR,
+  log: () => Qn,
+  log1p: () => Zg,
+  logSigmoid: () => mpe,
+  logSoftmax: () => cI,
+  logSumExp: () => dD,
   logicalAnd: () => Ds,
-  logicalNot: () => Jg,
-  logicalOr: () => cI,
-  logicalXor: () => npe,
-  losses: () => Cpe,
+  logicalNot: () => tb,
+  logicalOr: () => fI,
+  logicalXor: () => gpe,
+  losses: () => Lpe,
   matMul: () => We,
-  math: () => iA,
+  math: () => pA,
   max: () => As,
-  maxPool: () => eb,
-  maxPool3d: () => dI,
-  maxPoolWithArgmax: () => fD,
-  maximum: () => $r,
+  maxPool: () => nb,
+  maxPool3d: () => mI,
+  maxPoolWithArgmax: () => vD,
+  maximum: () => _r,
   mean: () => St,
-  memory: () => dm,
-  meshgrid: () => spe,
-  metrics: () => rW,
-  min: () => fm,
-  minimum: () => bp,
-  mirrorPad: () => xD,
-  mod: () => kD,
-  model: () => Npe,
-  models: () => xW,
-  moments: () => tb,
-  movingAverage: () => wpe,
+  memory: () => pm,
+  meshgrid: () => bpe,
+  metrics: () => fW,
+  min: () => gm,
+  minimum: () => vp,
+  mirrorPad: () => CD,
+  mod: () => TD,
+  model: () => Bpe,
+  models: () => AW,
+  moments: () => sb,
+  movingAverage: () => Ope,
   mul: () => V,
-  multiRNNCell: () => rpe,
-  multinomial: () => TD,
+  multiRNNCell: () => ype,
+  multinomial: () => RD,
   neg: () => kt,
-  nextFrame: () => OI,
-  norm: () => CI,
-  notEqual: () => Xu,
+  nextFrame: () => LI,
+  norm: () => _I,
+  notEqual: () => Yu,
   oneHot: () => vd,
   ones: () => Mn,
-  onesLike: () => Qn,
+  onesLike: () => Zn,
   op: () => L,
-  outerProduct: () => ape,
-  pad: () => fi,
-  pad1d: () => ipe,
-  pad2d: () => ope,
-  pad3d: () => upe,
-  pad4d: () => lpe,
-  pool: () => cpe,
-  pow: () => pa,
-  prelu: () => sb,
-  print: () => U_,
-  prod: () => pI,
-  profile: () => zde,
-  rand: () => dpe,
-  randomGamma: () => ppe,
-  randomNormal: () => KD,
+  outerProduct: () => vpe,
+  pad: () => bi,
+  pad1d: () => xpe,
+  pad2d: () => wpe,
+  pad3d: () => kpe,
+  pad4d: () => Ipe,
+  pool: () => Spe,
+  pow: () => ha,
+  prelu: () => ab,
+  print: () => X_,
+  prod: () => gI,
+  profile: () => Qde,
+  rand: () => Cpe,
+  randomGamma: () => Npe,
+  randomNormal: () => JD,
   randomUniform: () => Ml,
-  range: () => Yu,
-  ready: () => Bde,
+  range: () => Qu,
+  ready: () => epe,
   real: () => wd,
-  reciprocal: () => ZD,
-  registerBackend: () => pp,
-  registerCallbackConstructor: () => _pe,
-  registerGradient: () => D$,
+  reciprocal: () => s3,
+  registerBackend: () => fp,
+  registerCallbackConstructor: () => Upe,
+  registerGradient: () => L$,
   registerKernel: () => El,
-  registerOp: () => Epe,
-  regularizers: () => wW,
-  relu: () => Xs,
-  relu6: () => hI,
-  removeBackend: () => Wde,
+  registerOp: () => Hpe,
+  regularizers: () => EW,
+  relu: () => Ys,
+  relu6: () => bI,
+  removeBackend: () => npe,
   reshape: () => G,
-  reverse: () => Zn,
-  reverse1d: () => hpe,
-  reverse2d: () => fpe,
-  reverse3d: () => mpe,
-  reverse4d: () => gpe,
-  rfft: () => cb,
-  round: () => fI,
-  rsqrt: () => mI,
+  reverse: () => Jn,
+  reverse1d: () => Tpe,
+  reverse2d: () => $pe,
+  reverse3d: () => _pe,
+  reverse4d: () => Ape,
+  rfft: () => pb,
+  round: () => yI,
+  rsqrt: () => vI,
   scalar: () => we,
-  scatterND: () => X3,
-  scatter_util: () => Ak,
-  selu: () => gI,
-  separableConv2d: () => c3,
-  sequential: () => Tpe,
+  scatterND: () => eF,
+  scatter_util: () => Fk,
+  selu: () => xI,
+  separableConv2d: () => m3,
+  sequential: () => Vpe,
   serialization: () => ae,
-  setBackend: () => Lde,
-  setPlatform: () => Hde,
-  setThreadsCount: () => Vpe,
-  setWasmPath: () => Lpe,
-  setWasmPaths: () => Bpe,
-  setWebGLContext: () => S5,
-  setdiff1dAsync: () => p3,
-  shared: () => Zy,
+  setBackend: () => Jde,
+  setPlatform: () => ape,
+  setThreadsCount: () => the,
+  setWasmPath: () => Jpe,
+  setWasmPaths: () => ehe,
+  setWebGLContext: () => F5,
+  setdiff1dAsync: () => b3,
+  shared: () => ev,
   sigmoid: () => qs,
-  sign: () => f3,
-  signal: () => Spe,
-  sin: () => bI,
-  sinh: () => yI,
-  slice: () => He,
-  slice1d: () => ib,
-  slice2d: () => vI,
-  slice3d: () => ob,
+  sign: () => v3,
+  signal: () => Mpe,
+  sin: () => wI,
+  sinh: () => kI,
+  slice: () => qe,
+  slice1d: () => ub,
+  slice2d: () => II,
+  slice3d: () => lb,
   slice4d: () => kd,
   slice_util: () => wt,
-  softmax: () => ub,
+  softmax: () => cb,
   softplus: () => zl,
-  spaceToBatchND: () => nb,
+  spaceToBatchND: () => rb,
   sparse: () => Wc,
-  sparseToDense: () => NI,
-  spectral: () => Ipe,
+  sparseToDense: () => AI,
+  spectral: () => zpe,
   split: () => Bn,
   sqrt: () => dn,
   square: () => ct,
-  squaredDifference: () => wI,
-  squeeze: () => mr,
-  stack: () => Jn,
-  step: () => yp,
-  stridedSlice: () => D3,
-  string: () => Mf,
+  squaredDifference: () => CI,
+  squeeze: () => gr,
+  stack: () => es,
+  step: () => xp,
+  stridedSlice: () => M3,
+  string: () => Bf,
   sub: () => ge,
   sum: () => ve,
-  sumOutType: () => cp,
-  tan: () => O3,
-  tanh: () => qu,
+  sumOutType: () => pp,
+  tan: () => B3,
+  tanh: () => ju,
   tensor: () => fs,
   tensor1d: () => Zt,
-  tensor2d: () => Ki,
-  tensor3d: () => pA,
-  tensor4d: () => bpe,
-  tensor5d: () => ype,
-  tensor6d: () => vpe,
+  tensor2d: () => Yi,
+  tensor3d: () => yA,
+  tensor4d: () => Epe,
+  tensor5d: () => Rpe,
+  tensor6d: () => Dpe,
   tensor_util: () => _s,
-  test_util: () => _A,
+  test_util: () => OA,
   tidy: () => j,
   tile: () => ps,
-  time: () => Mde,
-  topk: () => z3,
-  train: () => Pi,
-  transpose: () => qe,
-  truncatedNormal: () => db,
-  unique: () => hx,
-  unregisterGradient: () => Ade,
-  unregisterKernel: () => _de,
-  unsortedSegmentSum: () => V3,
+  time: () => Zde,
+  topk: () => W3,
+  train: () => Mi,
+  transpose: () => Ge,
+  truncatedNormal: () => hb,
+  unique: () => mx,
+  unregisterGradient: () => Gde,
+  unregisterKernel: () => Ude,
+  unsortedSegmentSum: () => q3,
   unstack: () => Fs,
   upcastType: () => cn,
   util: () => w,
-  valueAndGrad: () => Jde,
-  valueAndGrads: () => epe,
-  variable: () => U3,
-  variableGrads: () => XR,
-  version: () => Gpe,
-  version_converter: () => Fpe,
-  version_core: () => Rde,
-  version_cpu: () => Ope,
-  version_layers: () => uS,
-  version_wasm: () => Upe,
-  version_webgl: () => Ppe,
-  webgl: () => zpe,
-  webgl_util: () => I5,
-  webgpu: () => roe,
+  valueAndGrad: () => hpe,
+  valueAndGrads: () => fpe,
+  variable: () => K3,
+  variableGrads: () => eD,
+  version: () => rhe,
+  version_converter: () => Kpe,
+  version_core: () => qde,
+  version_cpu: () => Xpe,
+  version_layers: () => hS,
+  version_wasm: () => she,
+  version_webgl: () => Ype,
+  webgl: () => Qpe,
+  webgl_util: () => D5,
+  webgpu: () => yoe,
   where: () => vn,
-  whereAsync: () => II,
+  whereAsync: () => TI,
   zeros: () => $t,
   zerosLike: () => je
 });
-var LT = Object.create;
-var Vd = Object.defineProperty;
-var BT = Object.getOwnPropertyDescriptor;
-var Vw = Object.getOwnPropertyNames;
-var VT = Object.getPrototypeOf;
-var WT = Object.prototype.hasOwnProperty;
-var UT = (e) => Vd(e, "__esModule", { value: true });
+var HT = Object.create;
+var Ud = Object.defineProperty;
+var qT = Object.getOwnPropertyDescriptor;
+var Hw = Object.getOwnPropertyNames;
+var jT = Object.getPrototypeOf;
+var KT = Object.prototype.hasOwnProperty;
+var XT = (e) => Ud(e, "__esModule", { value: true });
 var zt = (e, t) => function() {
-  return t || (0, e[Vw(e)[0]])((t = { exports: {} }).exports, t), t.exports;
+  return t || (0, e[Hw(e)[0]])((t = { exports: {} }).exports, t), t.exports;
 };
 var Ae = (e, t) => {
   for (var n in t)
-    Vd(e, n, { get: t[n], enumerable: true });
+    Ud(e, n, { get: t[n], enumerable: true });
 };
-var GT = (e, t, n, s) => {
+var YT = (e, t, n, s) => {
   if (t && typeof t == "object" || typeof t == "function")
-    for (let r of Vw(t))
-      !WT.call(e, r) && (n || r !== "default") && Vd(e, r, { get: () => t[r], enumerable: !(s = BT(t, r)) || s.enumerable });
+    for (let r of Hw(t))
+      !KT.call(e, r) && (n || r !== "default") && Ud(e, r, { get: () => t[r], enumerable: !(s = qT(t, r)) || s.enumerable });
   return e;
 };
-var xa = (e, t) => GT(UT(Vd(e != null ? LT(VT(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
-var HT = zt({ "src/node_modules/long/src/long.js"(e, t) {
+var wa = (e, t) => YT(XT(Ud(e != null ? HT(jT(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
+var QT = zt({ "src/node_modules/long/src/long.js"(e, t) {
   t.exports = s;
   var n = null;
   try {
@@ -1004,11 +1004,11 @@ var HT = zt({ "src/node_modules/long/src/long.js"(e, t) {
     return new s($[4] << 24 | $[5] << 16 | $[6] << 8 | $[7], $[0] << 24 | $[1] << 16 | $[2] << 8 | $[3], z);
   };
 } });
-var qT = zt({ "(disabled):src/node_modules/node-fetch/browser.js"() {
+var ZT = zt({ "(disabled):src/node_modules/node-fetch/browser.js"() {
 } });
-var jT = zt({ "(disabled):util"() {
+var JT = zt({ "(disabled):util"() {
 } });
-var KT = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
+var e$ = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
   (function(n, s, r) {
     function a(l) {
       var c = this, p = u();
@@ -1047,7 +1047,7 @@ var KT = zt({ "src/node_modules/seedrandom/lib/alea.js"(e, t) {
     }) : this.alea = o;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var XT = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
+var t$ = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this, c = "";
@@ -1079,7 +1079,7 @@ var XT = zt({ "src/node_modules/seedrandom/lib/xor128.js"(e, t) {
     }) : this.xor128 = o;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var YT = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
+var n$ = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this, c = "";
@@ -1111,7 +1111,7 @@ var YT = zt({ "src/node_modules/seedrandom/lib/xorwow.js"(e, t) {
     }) : this.xorwow = o;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var QT = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
+var s$ = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this;
@@ -1157,7 +1157,7 @@ var QT = zt({ "src/node_modules/seedrandom/lib/xorshift7.js"(e, t) {
     }) : this.xorshift7 = o;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var ZT = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
+var r$ = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this;
@@ -1197,7 +1197,7 @@ var ZT = zt({ "src/node_modules/seedrandom/lib/xor4096.js"(e, t) {
     }) : this.xor4096 = o;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var JT = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
+var a$ = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
   (function(n, s, r) {
     function a(u) {
       var l = this, c = "";
@@ -1229,9 +1229,9 @@ var JT = zt({ "src/node_modules/seedrandom/lib/tychei.js"(e, t) {
     }) : this.tychei = o;
   })(e, typeof t == "object" && t, typeof define == "function" && define);
 } });
-var e$ = zt({ "(disabled):crypto"() {
+var i$ = zt({ "(disabled):crypto"() {
 } });
-var t$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
+var o$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
   (function(n, s, r) {
     var a = 256, i = 6, o = 52, u = "random", l = r.pow(a, i), c = r.pow(2, o), p = c * 2, d = a - 1, h;
     function f(k, T, N) {
@@ -1299,7 +1299,7 @@ var t$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
     if (y(r.random(), s), typeof t == "object" && t.exports) {
       t.exports = f;
       try {
-        h = e$();
+        h = i$();
       } catch (k) {
       }
     } else
@@ -1308,23 +1308,23 @@ var t$ = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
       }) : r["seed" + u] = f;
   })(typeof self != "undefined" ? self : e, [], Math);
 } });
-var Wd = zt({ "src/node_modules/seedrandom/index.js"(e, t) {
-  var n = KT(), s = XT(), r = YT(), a = QT(), i = ZT(), o = JT(), u = t$();
+var Gd = zt({ "src/node_modules/seedrandom/index.js"(e, t) {
+  var n = e$(), s = t$(), r = n$(), a = s$(), i = r$(), o = a$(), u = o$();
   u.alea = n, u.xor128 = s, u.xorwow = r, u.xorshift7 = a, u.xor4096 = i, u.tychei = o, t.exports = u;
 } });
-var Ww = zt({ "(disabled):src/node_modules/string_decoder/index.js"() {
+var qw = zt({ "(disabled):src/node_modules/string_decoder/index.js"() {
 } });
-var tg = zt({ "(disabled):fs"() {
+var sg = zt({ "(disabled):fs"() {
 } });
 var dd = zt({ "(disabled):path"() {
 } });
-var n$ = zt({ "(disabled):worker_threads"() {
+var u$ = zt({ "(disabled):worker_threads"() {
 } });
-var s$ = zt({ "(disabled):perf_hooks"() {
+var l$ = zt({ "(disabled):perf_hooks"() {
 } });
-var r$ = zt({ "(disabled):os"() {
+var c$ = zt({ "(disabled):os"() {
 } });
-var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e, t) {
+var d$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e, t) {
   var n = (() => {
     var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
     return typeof __filename != "undefined" && (s = s || __filename), function(r) {
@@ -1336,7 +1336,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return Ce.buffer != nn && rs(Ce.buffer), oc;
       }
       function o() {
-        return Ce.buffer != nn && rs(Ce.buffer), hu;
+        return Ce.buffer != nn && rs(Ce.buffer), fu;
       }
       function u() {
         return Ce.buffer != nn && rs(Ce.buffer), uc;
@@ -1364,14 +1364,14 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }
       var P, R, F, $;
       function z(C) {
-        if (C instanceof ku)
+        if (C instanceof Iu)
           return;
         ee("exiting due to exception: " + C);
       }
       var W, q, K;
       if (T) {
         k ? E = dd().dirname(E) + "/" : E = __dirname + "/", K = () => {
-          q || (W = tg(), q = dd());
+          q || (W = sg(), q = dd());
         }, P = function(B, Q) {
           return K(), B = q.normalize(B), W.readFileSync(B, Q ? void 0 : "utf8");
         }, F = (D) => {
@@ -1382,12 +1382,12 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             ue ? Q(ue) : B(pe.buffer);
           });
         }, process.argv.length > 1 && (y = process.argv[1].replace(/\\/g, "/")), b = process.argv.slice(2), process.on("uncaughtException", function(D) {
-          if (!(D instanceof ku))
+          if (!(D instanceof Iu))
             throw D;
         }), process.on("unhandledRejection", function(D) {
           throw D;
         }), v = (D, B) => {
-          if (Pr())
+          if (zr())
             throw process.exitCode = D, B;
           z(B), process.exit(D);
         }, d.inspect = function() {
@@ -1395,7 +1395,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         };
         let C;
         try {
-          C = n$();
+          C = u$();
         } catch (D) {
           throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), D;
         }
@@ -1417,7 +1417,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             B();
           }, Q.onerror = B, Q.send(null);
         }), $ = (C) => document.title = C);
-      T && typeof performance == "undefined" && (global.performance = s$().performance);
+      T && typeof performance == "undefined" && (global.performance = l$().performance);
       var Y = console.log.bind(console), Z = console.warn.bind(console);
       T && (K(), Y = (C) => W.writeSync(1, C + `
 `), Z = (C) => W.writeSync(2, C + `
@@ -1439,8 +1439,8 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         for (var ue = 0; ue < Te.length; ++ue)
           pe.push(bt[Te[ue]]);
         ye == "v" ? pe.push(0) : pe = pe.concat([1, bt[ye]]), pe[1] = pe.length - 2;
-        var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Bc = new WebAssembly.Instance(ls, { e: { f: C } }), Iu = Bc.exports.f;
-        return Iu;
+        var us = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), ls = new WebAssembly.Module(us), Bc = new WebAssembly.Instance(ls, { e: { f: C } }), Su = Bc.exports.f;
+        return Su;
       }
       var re = [], le;
       function me() {
@@ -1455,7 +1455,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }
       function ke(C, D) {
         for (var B = C; B < C + D; B++) {
-          var Q = $i(B);
+          var Q = Ai(B);
           Q && le.set(Q, B);
         }
       }
@@ -1464,10 +1464,10 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, Pe = Atomics.load, Xe = Atomics.store, Je = Atomics.compareExchange, Ye;
       d.wasmBinary && (Ye = d.wasmBinary);
       var tt = d.noExitRuntime || true;
-      typeof WebAssembly != "object" && Ci("no native wasm support detected");
+      typeof WebAssembly != "object" && Ti("no native wasm support detected");
       var Ce, ut, at = false, Jt;
       function Nt(C, D) {
-        C || Ci(D);
+        C || Ti(D);
       }
       function Cn(C) {
         var D = d["_" + C];
@@ -1475,15 +1475,15 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }
       function Et(C, D, B, Q, ue) {
         var pe = { string: function($n) {
-          var Oi = 0;
+          var zi = 0;
           if ($n != null && $n !== 0) {
-            var ex = ($n.length << 2) + 1;
-            Oi = Fi(ex), Ls($n, Oi, ex);
+            var nx = ($n.length << 2) + 1;
+            zi = Pi(nx), Ls($n, zi, nx);
           }
-          return Oi;
+          return zi;
         }, array: function($n) {
-          var Oi = Fi($n.length);
-          return Bs($n, Oi), Oi;
+          var zi = Pi($n.length);
+          return Bs($n, zi), zi;
         } };
         function ye($n) {
           return D === "string" ? tn($n) : D === "boolean" ? Boolean($n) : $n;
@@ -1492,13 +1492,13 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         if (Q)
           for (var ls = 0; ls < Q.length; ls++) {
             var Bc = pe[B[ls]];
-            Bc ? (us === 0 && (us = Ef()), bt[ls] = Bc(Q[ls])) : bt[ls] = Q[ls];
+            Bc ? (us === 0 && (us = Df()), bt[ls] = Bc(Q[ls])) : bt[ls] = Q[ls];
           }
-        var Iu = Te.apply(null, bt);
-        function MT($n) {
+        var Su = Te.apply(null, bt);
+        function GT($n) {
           return us !== 0 && Pc(us), ye($n);
         }
-        return Iu = MT(Iu), Iu;
+        return Su = GT(Su), Su;
       }
       function en(C, D, B, Q) {
         B = B || [];
@@ -1576,29 +1576,29 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       function Ls(C, D, B) {
         return Ms(C, i(), D, B);
       }
-      function ki(C) {
+      function Si(C) {
         for (var D = 0, B = 0; B < C.length; ++B) {
           var Q = C.charCodeAt(B);
           Q >= 55296 && Q <= 57343 && (Q = 65536 + ((Q & 1023) << 10) | C.charCodeAt(++B) & 1023), Q <= 127 ? ++D : Q <= 2047 ? D += 2 : Q <= 65535 ? D += 3 : D += 4;
         }
         return D;
       }
-      var Js = typeof TextDecoder != "undefined" ? new Tn("utf-16le") : void 0;
+      var er = typeof TextDecoder != "undefined" ? new Tn("utf-16le") : void 0;
       function Bs(C, D) {
         a().set(C, D);
       }
-      function pu(C, D, B) {
+      function hu(C, D, B) {
         for (var Q = 0; Q < C.length; ++Q)
           a()[D++ >> 0] = C.charCodeAt(Q);
         B || (a()[D >> 0] = 0);
       }
-      function Ii(C, D) {
+      function Ci(C, D) {
         return C % D > 0 && (C += D - C % D), C;
       }
-      var nn, ic, oc, hu, uc, lc, Fv, cc, dc;
+      var nn, ic, oc, fu, uc, lc, Pv, cc, dc;
       N && (nn = d.buffer);
       function rs(C) {
-        nn = C, d.HEAP8 = ic = new Int8Array(C), d.HEAP16 = hu = new Int16Array(C), d.HEAP32 = lc = new Int32Array(C), d.HEAPU8 = oc = new Uint8Array(C), d.HEAPU16 = uc = new Uint16Array(C), d.HEAPU32 = Fv = new Uint32Array(C), d.HEAPF32 = cc = new Float32Array(C), d.HEAPF64 = dc = new Float64Array(C);
+        nn = C, d.HEAP8 = ic = new Int8Array(C), d.HEAP16 = fu = new Int16Array(C), d.HEAP32 = lc = new Int32Array(C), d.HEAPU8 = oc = new Uint8Array(C), d.HEAPU16 = uc = new Uint16Array(C), d.HEAPU32 = Pv = new Uint32Array(C), d.HEAPF32 = cc = new Float32Array(C), d.HEAPF64 = dc = new Float64Array(C);
       }
       var pc = d.INITIAL_MEMORY || 16777216;
       if (N)
@@ -1608,58 +1608,58 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       else if (Ce = new WebAssembly.Memory({ initial: pc / 65536, maximum: 32768, shared: true }), !(Ce.buffer instanceof SharedArrayBuffer))
         throw ee("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), T && console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"), Error("bad memory");
       Ce && (nn = Ce.buffer), pc = nn.byteLength, rs(nn);
-      var Fn, Si = [], er = [], th = [], hc = [], Or = false, nh = false, fc = 0;
-      function Pr() {
+      var Fn, Ni = [], tr = [], sh = [], hc = [], Pr = false, rh = false, fc = 0;
+      function zr() {
         return tt || fc > 0;
       }
       function sn() {
         if (d.preRun)
           for (typeof d.preRun == "function" && (d.preRun = [d.preRun]); d.preRun.length; )
-            Ov(d.preRun.shift());
-        vc(Si);
+            zv(d.preRun.shift());
+        vc(Ni);
       }
-      function fu() {
-        Or = true, !N && vc(er);
+      function mu() {
+        Pr = true, !N && vc(tr);
       }
-      function sh() {
-        N || ($e.terminateAllThreads(), nh = true);
+      function ah() {
+        N || (_e.terminateAllThreads(), rh = true);
       }
-      function rh() {
+      function ih() {
         if (!N) {
           if (d.postRun)
             for (typeof d.postRun == "function" && (d.postRun = [d.postRun]); d.postRun.length; )
-              mu(d.postRun.shift());
+              gu(d.postRun.shift());
           vc(hc);
         }
       }
-      function Ov(C) {
-        Si.unshift(C);
+      function zv(C) {
+        Ni.unshift(C);
       }
-      function Pv(C) {
-        er.unshift(C);
+      function Mv(C) {
+        tr.unshift(C);
       }
-      function mu(C) {
+      function gu(C) {
         hc.unshift(C);
       }
-      var tr = 0, mc = null, as = null;
-      function gu(C) {
-        tr++, d.monitorRunDependencies && d.monitorRunDependencies(tr);
+      var nr = 0, mc = null, as = null;
+      function bu(C) {
+        nr++, d.monitorRunDependencies && d.monitorRunDependencies(nr);
       }
-      function zv(C) {
-        if (tr--, d.monitorRunDependencies && d.monitorRunDependencies(tr), tr == 0 && (mc !== null && (clearInterval(mc), mc = null), as)) {
+      function Lv(C) {
+        if (nr--, d.monitorRunDependencies && d.monitorRunDependencies(nr), nr == 0 && (mc !== null && (clearInterval(mc), mc = null), as)) {
           var D = as;
           as = null, D();
         }
       }
       d.preloadedImages = {}, d.preloadedAudios = {};
-      function Ci(C) {
+      function Ti(C) {
         N ? postMessage({ cmd: "onAbort", arg: C }) : d.onAbort && d.onAbort(C), C = "Aborted(" + C + ")", ee(C), at = true, Jt = 1, C += ". Build with -s ASSERTIONS=1 for more info.";
         var D = new WebAssembly.RuntimeError(C);
         throw f(D), D;
       }
-      var ah = "data:application/octet-stream;base64,";
+      var oh = "data:application/octet-stream;base64,";
       function gc(C) {
-        return C.startsWith(ah);
+        return C.startsWith(oh);
       }
       function bc(C) {
         return C.startsWith("file://");
@@ -1674,10 +1674,10 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             return F(C);
           throw "both async and sync fetching of the wasm failed";
         } catch (D) {
-          Ci(D);
+          Ti(D);
         }
       }
-      function Ni() {
+      function $i() {
         if (!Ye && (x || k)) {
           if (typeof fetch == "function" && !bc(rn))
             return fetch(rn, { credentials: "same-origin" }).then(function(C) {
@@ -1698,30 +1698,30 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           return yc(rn);
         });
       }
-      function ih() {
+      function uh() {
         var C = { env: Ec, wasi_snapshot_preview1: Ec };
         function D(ye, Te) {
           var bt = ye.exports;
-          if (d.asm = bt, hh(d.asm.emscripten_tls_init), Fn = d.asm.__indirect_function_table, Pv(d.asm.__wasm_call_ctors), ut = Te, !N) {
-            var us = $e.unusedWorkers.length;
-            $e.unusedWorkers.forEach(function(ls) {
-              $e.loadWasmModuleToWorker(ls, function() {
-                --us || zv("wasm-instantiate");
+          if (d.asm = bt, mh(d.asm.emscripten_tls_init), Fn = d.asm.__indirect_function_table, Mv(d.asm.__wasm_call_ctors), ut = Te, !N) {
+            var us = _e.unusedWorkers.length;
+            _e.unusedWorkers.forEach(function(ls) {
+              _e.loadWasmModuleToWorker(ls, function() {
+                --us || Lv("wasm-instantiate");
               });
             });
           }
         }
-        N || gu("wasm-instantiate");
+        N || bu("wasm-instantiate");
         function B(ye) {
           D(ye.instance, ye.module);
         }
         function Q(ye) {
-          return Ni().then(function(Te) {
+          return $i().then(function(Te) {
             return WebAssembly.instantiate(Te, C);
           }).then(function(Te) {
             return Te;
           }).then(ye, function(Te) {
-            ee("failed to asynchronously prepare wasm: " + Te), Ci(Te);
+            ee("failed to asynchronously prepare wasm: " + Te), Ti(Te);
           });
         }
         function ue() {
@@ -1741,7 +1741,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           }
         return ue().catch(f), {};
       }
-      var Mv, Lv, oh = {};
+      var Bv, Vv, lh = {};
       function vc(C) {
         for (; C.length > 0; ) {
           var D = C.shift();
@@ -1750,91 +1750,91 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             continue;
           }
           var B = D.func;
-          typeof B == "number" ? D.arg === void 0 ? $i(B)() : $i(B)(D.arg) : B(D.arg === void 0 ? null : D.arg);
+          typeof B == "number" ? D.arg === void 0 ? Ai(B)() : Ai(B)(D.arg) : B(D.arg === void 0 ? null : D.arg);
         }
       }
-      function Ti(C) {
-        var D = Ef(), B = C();
+      function _i(C) {
+        var D = Df(), B = C();
         return Pc(D), B;
       }
-      function qN(C) {
+      function ZN(C) {
         return C;
       }
-      function Bv(C) {
+      function Wv(C) {
         var D = /\b_Z[\w\d_]+/g;
         return C.replace(D, function(B) {
           var Q = B;
           return B === Q ? B : Q + " [" + B + "]";
         });
       }
-      function uh(C) {
+      function ch(C) {
         l()[C >> 2] = 0;
-        var D = $e.pthreads[C];
-        delete $e.pthreads[C], D.worker.terminate(), Af(C), $e.runningWorkers.splice($e.runningWorkers.indexOf(D.worker), 1), D.worker.pthread = void 0;
+        var D = _e.pthreads[C];
+        delete _e.pthreads[C], D.worker.terminate(), Rf(C), _e.runningWorkers.splice(_e.runningWorkers.indexOf(D.worker), 1), D.worker.pthread = void 0;
       }
-      function lh(C) {
-        var D = $e.pthreads[C];
+      function dh(C) {
+        var D = _e.pthreads[C];
         D.worker.postMessage({ cmd: "cancel" });
       }
       function xc(C) {
-        var D = $e.pthreads[C];
+        var D = _e.pthreads[C];
         if (D) {
           l()[C >> 2] = 0;
           var B = D.worker;
-          $e.returnWorkerToPool(B);
+          _e.returnWorkerToPool(B);
         }
       }
       function wc(C) {
-        OT(C);
+        VT(C);
       }
-      function ch(C) {
-        if (C instanceof ku || C == "unwind")
+      function ph(C) {
+        if (C instanceof Iu || C == "unwind")
           return Jt;
         v(1, C);
       }
-      var $e = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
-        N ? $e.initWorker() : $e.initMainThread();
+      var _e = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
+        N ? _e.initWorker() : _e.initMainThread();
       }, initMainThread: function() {
         for (var C = 8, D = 0; D < C; ++D)
-          $e.allocateUnusedWorker();
+          _e.allocateUnusedWorker();
       }, initWorker: function() {
         tt = false;
       }, pthreads: {}, setExitStatus: function(C) {
         Jt = C;
       }, terminateAllThreads: function() {
-        for (var C in $e.pthreads) {
-          var D = $e.pthreads[C];
-          D && D.worker && $e.returnWorkerToPool(D.worker);
+        for (var C in _e.pthreads) {
+          var D = _e.pthreads[C];
+          D && D.worker && _e.returnWorkerToPool(D.worker);
         }
-        for (var B = 0; B < $e.unusedWorkers.length; ++B) {
-          var Q = $e.unusedWorkers[B];
+        for (var B = 0; B < _e.unusedWorkers.length; ++B) {
+          var Q = _e.unusedWorkers[B];
           Q.terminate();
         }
-        $e.unusedWorkers = [];
+        _e.unusedWorkers = [];
       }, returnWorkerToPool: function(C) {
-        $e.runWithoutMainThreadQueuedCalls(function() {
-          delete $e.pthreads[C.pthread.threadInfoStruct], $e.unusedWorkers.push(C), $e.runningWorkers.splice($e.runningWorkers.indexOf(C), 1), Af(C.pthread.threadInfoStruct), C.pthread = void 0;
+        _e.runWithoutMainThreadQueuedCalls(function() {
+          delete _e.pthreads[C.pthread.threadInfoStruct], _e.unusedWorkers.push(C), _e.runningWorkers.splice(_e.runningWorkers.indexOf(C), 1), Rf(C.pthread.threadInfoStruct), C.pthread = void 0;
         });
       }, runWithoutMainThreadQueuedCalls: function(C) {
-        l()[Jv >> 2] = 0;
+        l()[tx >> 2] = 0;
         try {
           C();
         } finally {
-          l()[Jv >> 2] = 1;
+          l()[tx >> 2] = 1;
         }
       }, receiveObjectTransfer: function(C) {
       }, threadInit: function() {
-        for (var C in $e.tlsInitFunctions)
-          $e.tlsInitFunctions[C]();
+        for (var C in _e.tlsInitFunctions)
+          _e.tlsInitFunctions[C]();
       }, loadWasmModuleToWorker: function(C, D) {
         C.onmessage = (B) => {
           var Q = B.data, ue = Q.cmd;
-          if (C.pthread && ($e.currentProxiedOperationCallerThread = C.pthread.threadInfoStruct), Q.targetThread && Q.targetThread != Oc()) {
-            var pe = $e.pthreads[Q.targetThread];
-            pe ? pe.worker.postMessage(Q, Q.transferList) : ee('Internal error! Worker sent a message "' + ue + '" to target pthread ' + Q.targetThread + ", but that thread no longer exists!"), $e.currentProxiedOperationCallerThread = void 0;
+          if (C.pthread && (_e.currentProxiedOperationCallerThread = C.pthread.threadInfoStruct), Q.targetThread && Q.targetThread != Oc()) {
+            var pe = _e.pthreads[Q.targetThread];
+            pe ? pe.worker.postMessage(Q, Q.transferList) : ee('Internal error! Worker sent a message "' + ue + '" to target pthread ' + Q.targetThread + ", but that thread no longer exists!"), _e.currentProxiedOperationCallerThread = void 0;
             return;
           }
-          ue === "processQueuedMainThreadWork" ? Kv() : ue === "spawnThread" ? Ic(Q) : ue === "cleanupThread" ? xc(Q.thread) : ue === "killThread" ? uh(Q.thread) : ue === "cancelThread" ? lh(Q.thread) : ue === "loaded" ? (C.loaded = true, D && D(C), C.runPthread && (C.runPthread(), delete C.runPthread)) : ue === "print" ? te("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? ee("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? C.postMessage(Q) : ue === "onAbort" ? d.onAbort && d.onAbort(Q.arg) : ee("worker sent an unknown command " + ue), $e.currentProxiedOperationCallerThread = void 0;
+          ue === "processQueuedMainThreadWork" ? Yv() : ue === "spawnThread" ? Ic(Q) : ue === "cleanupThread" ? xc(Q.thread) : ue === "killThread" ? ch(Q.thread) : ue === "cancelThread" ? dh(Q.thread) : ue === "loaded" ? (C.loaded = true, D && D(C), C.runPthread && (C.runPthread(), delete C.runPthread)) : ue === "print" ? te("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? ee("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? C.postMessage(Q) : ue === "onAbort" ? d.onAbort && d.onAbort(Q.arg) : ee("worker sent an unknown command " + ue), _e.currentProxiedOperationCallerThread = void 0;
         }, C.onerror = (B) => {
           var Q = "worker sent an error!";
           throw ee(Q + " " + B.filename + ":" + B.lineno + ": " + B.message), B;
@@ -1846,34 +1846,34 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         })), C.postMessage({ cmd: "load", urlOrBlob: d.mainScriptUrlOrBlob || s, wasmMemory: Ce, wasmModule: ut });
       }, allocateUnusedWorker: function() {
         var C = A("tfjs-backend-wasm-threaded-simd.worker.js");
-        $e.unusedWorkers.push(new Worker(C));
+        _e.unusedWorkers.push(new Worker(C));
       }, getNewWorker: function() {
-        return $e.unusedWorkers.length == 0 && ($e.allocateUnusedWorker(), $e.loadWasmModuleToWorker($e.unusedWorkers[0])), $e.unusedWorkers.pop();
+        return _e.unusedWorkers.length == 0 && (_e.allocateUnusedWorker(), _e.loadWasmModuleToWorker(_e.unusedWorkers[0])), _e.unusedWorkers.pop();
       } };
-      function dh() {
+      function hh() {
         var C = Oc(), D = l()[C + 44 >> 2], B = l()[C + 48 >> 2], Q = D - B;
-        Zv(D, Q), Pc(D);
+        ex(D, Q), Pc(D);
       }
-      d.establishStackSpace = dh;
+      d.establishStackSpace = hh;
       function kc(C) {
         if (N)
-          return Lr(1, 0, C);
+          return Br(1, 0, C);
         try {
           wc(C);
         } catch (D) {
-          ch(D);
+          ph(D);
         }
       }
-      var zr = [];
-      function $i(C) {
-        var D = zr[C];
-        return D || (C >= zr.length && (zr.length = C + 1), zr[C] = D = Fn.get(C)), D;
+      var Mr = [];
+      function Ai(C) {
+        var D = Mr[C];
+        return D || (C >= Mr.length && (Mr.length = C + 1), Mr[C] = D = Fn.get(C)), D;
       }
-      function ph(C, D) {
-        return $i(C)(D);
+      function fh(C, D) {
+        return Ai(C)(D);
       }
-      d.invokeEntryPoint = ph;
-      function Vv() {
+      d.invokeEntryPoint = fh;
+      function Uv() {
         var C = new Error();
         if (!C.stack) {
           try {
@@ -1886,119 +1886,119 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
         return C.stack.toString();
       }
-      function hh(C, D, B) {
-        $e.tlsInitFunctions.push(C);
+      function mh(C, D, B) {
+        _e.tlsInitFunctions.push(C);
       }
-      function Wv(C, D) {
-        Fn.set(C, D), zr[C] = D;
+      function Gv(C, D) {
+        Fn.set(C, D), Mr[C] = D;
       }
-      var Mr;
-      T ? Mr = () => {
+      var Lr;
+      T ? Lr = () => {
         var C = process.hrtime();
         return C[0] * 1e3 + C[1] / 1e6;
-      } : N ? Mr = () => performance.now() - d.__performance_now_clock_drift : Mr = () => performance.now();
-      var fh = true;
-      function mh(C) {
-        return l()[jv() >> 2] = C, C;
+      } : N ? Lr = () => performance.now() - d.__performance_now_clock_drift : Lr = () => performance.now();
+      var gh = true;
+      function bh(C) {
+        return l()[Xv() >> 2] = C, C;
       }
-      function gh(C, D) {
+      function yh(C, D) {
         var B;
         if (C === 0)
           B = Date.now();
-        else if ((C === 1 || C === 4) && fh)
-          B = Mr();
+        else if ((C === 1 || C === 4) && gh)
+          B = Lr();
         else
-          return mh(28), -1;
+          return bh(28), -1;
         return l()[D >> 2] = B / 1e3 | 0, l()[D + 4 >> 2] = B % 1e3 * 1e3 * 1e3 | 0, 0;
       }
-      function bh(C, D) {
-        return gh(C, D);
+      function vh(C, D) {
+        return yh(C, D);
       }
-      function yh(C) {
-        Xv(C, !k, 1, !x), $e.threadInit();
+      function xh(C) {
+        Qv(C, !k, 1, !x), _e.threadInit();
       }
-      function vh(C) {
+      function wh(C) {
         N ? postMessage({ cmd: "cleanupThread", thread: C }) : xc(C);
       }
       function Ic(C) {
-        var D = $e.getNewWorker();
+        var D = _e.getNewWorker();
         if (!D)
           return 6;
-        $e.runningWorkers.push(D);
-        var B = $e.pthreads[C.pthread_ptr] = { worker: D, threadInfoStruct: C.pthread_ptr };
+        _e.runningWorkers.push(D);
+        var B = _e.pthreads[C.pthread_ptr] = { worker: D, threadInfoStruct: C.pthread_ptr };
         D.pthread = B;
         var Q = { cmd: "run", start_routine: C.startRoutine, arg: C.arg, threadInfoStruct: C.pthread_ptr };
         return D.runPthread = () => {
           Q.time = performance.now(), D.postMessage(Q, C.transferList);
         }, D.loaded && (D.runPthread(), delete D.runPthread), 0;
       }
-      function xh(C, D, B, Q) {
+      function kh(C, D, B, Q) {
         if (typeof SharedArrayBuffer == "undefined")
           return ee("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
         var ue = [], pe = 0;
         if (N && (ue.length === 0 || pe))
-          return Yv(687865856, C, D, B, Q);
+          return Zv(687865856, C, D, B, Q);
         if (pe)
           return pe;
         var ye = { startRoutine: B, pthread_ptr: C, arg: Q, transferList: ue };
         return N ? (ye.cmd = "spawnThread", postMessage(ye, ue), 0) : Ic(ye);
       }
-      function wh() {
+      function Ih() {
         return 2097152;
       }
-      function kh(C, D) {
+      function Sh(C, D) {
         if (C == D)
           postMessage({ cmd: "processQueuedMainThreadWork" });
         else if (N)
           postMessage({ targetThread: C, cmd: "processThreadQueue" });
         else {
-          var B = $e.pthreads[C], Q = B && B.worker;
+          var B = _e.pthreads[C], Q = B && B.worker;
           if (!Q)
             return;
           Q.postMessage({ cmd: "processThreadQueue" });
         }
         return 1;
       }
-      function Ih() {
-        Ci("");
+      function Ch() {
+        Ti("");
       }
-      function Sh() {
+      function Nh() {
         T || k || ne("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
       }
       function Sc() {
         return 2147483648;
       }
-      function Ch(C, D, B) {
+      function Th(C, D, B) {
         i().copyWithin(C, D, D + B);
       }
-      function Nh() {
-        return T ? r$().cpus().length : navigator.hardwareConcurrency;
+      function $h() {
+        return T ? c$().cpus().length : navigator.hardwareConcurrency;
       }
-      function Lr(C, D) {
+      function Br(C, D) {
         var B = arguments.length - 2, Q = arguments;
-        return Ti(function() {
-          for (var ue = B, pe = Fi(ue * 8), ye = pe >> 3, Te = 0; Te < B; Te++) {
+        return _i(function() {
+          for (var ue = B, pe = Pi(ue * 8), ye = pe >> 3, Te = 0; Te < B; Te++) {
             var bt = Q[2 + Te];
             p()[ye + Te] = bt;
           }
-          return Qv(C, ue, pe, D);
+          return Jv(C, ue, pe, D);
         });
       }
-      var bu = [];
-      function Th(C, D, B) {
-        bu.length = D;
+      var yu = [];
+      function _h(C, D, B) {
+        yu.length = D;
         for (var Q = B >> 3, ue = 0; ue < D; ue++)
-          bu[ue] = p()[Q + ue];
-        var pe = C < 0, ye = pe ? oh[-C - 1] : qh[C];
-        return ye.apply(null, bu);
+          yu[ue] = p()[Q + ue];
+        var pe = C < 0, ye = pe ? lh[-C - 1] : Kh[C];
+        return ye.apply(null, yu);
       }
-      function $h(C) {
+      function Ah(C) {
         try {
           return Ce.grow(C - nn.byteLength + 65535 >>> 16), rs(Ce.buffer), 1;
         } catch (D) {
         }
       }
-      function _h(C) {
+      function Eh(C) {
         var D = i().length;
         if (C = C >>> 0, C <= D)
           return false;
@@ -2008,18 +2008,18 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         for (var Q = 1; Q <= 4; Q *= 2) {
           var ue = D * (1 + 0.2 / Q);
           ue = Math.min(ue, C + 100663296);
-          var pe = Math.min(B, Ii(Math.max(C, ue), 65536)), ye = $h(pe);
+          var pe = Math.min(B, Ci(Math.max(C, ue), 65536)), ye = Ah(pe);
           if (ye)
             return true;
         }
         return false;
       }
-      var Le = { inEventHandler: 0, removeAllEventListeners: function() {
-        for (var C = Le.eventHandlers.length - 1; C >= 0; --C)
-          Le._removeHandler(C);
-        Le.eventHandlers = [], Le.deferredCalls = [];
+      var Be = { inEventHandler: 0, removeAllEventListeners: function() {
+        for (var C = Be.eventHandlers.length - 1; C >= 0; --C)
+          Be._removeHandler(C);
+        Be.eventHandlers = [], Be.deferredCalls = [];
       }, registerRemoveEventListeners: function() {
-        Le.removeEventListenersRegistered || (th.push(Le.removeAllEventListeners), Le.removeEventListenersRegistered = true);
+        Be.removeEventListenersRegistered || (sh.push(Be.removeAllEventListeners), Be.removeEventListenersRegistered = true);
       }, deferredCalls: [], deferCall: function(C, D, B) {
         function Q(ye, Te) {
           if (ye.length != Te.length)
@@ -2029,51 +2029,51 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
               return false;
           return true;
         }
-        for (var ue in Le.deferredCalls) {
-          var pe = Le.deferredCalls[ue];
+        for (var ue in Be.deferredCalls) {
+          var pe = Be.deferredCalls[ue];
           if (pe.targetFunction == C && Q(pe.argsList, B))
             return;
         }
-        Le.deferredCalls.push({ targetFunction: C, precedence: D, argsList: B }), Le.deferredCalls.sort(function(ye, Te) {
+        Be.deferredCalls.push({ targetFunction: C, precedence: D, argsList: B }), Be.deferredCalls.sort(function(ye, Te) {
           return ye.precedence < Te.precedence;
         });
       }, removeDeferredCalls: function(C) {
-        for (var D = 0; D < Le.deferredCalls.length; ++D)
-          Le.deferredCalls[D].targetFunction == C && (Le.deferredCalls.splice(D, 1), --D);
+        for (var D = 0; D < Be.deferredCalls.length; ++D)
+          Be.deferredCalls[D].targetFunction == C && (Be.deferredCalls.splice(D, 1), --D);
       }, canPerformEventHandlerRequests: function() {
-        return Le.inEventHandler && Le.currentEventHandler.allowsDeferredCalls;
+        return Be.inEventHandler && Be.currentEventHandler.allowsDeferredCalls;
       }, runDeferredCalls: function() {
-        if (!!Le.canPerformEventHandlerRequests())
-          for (var C = 0; C < Le.deferredCalls.length; ++C) {
-            var D = Le.deferredCalls[C];
-            Le.deferredCalls.splice(C, 1), --C, D.targetFunction.apply(null, D.argsList);
+        if (!!Be.canPerformEventHandlerRequests())
+          for (var C = 0; C < Be.deferredCalls.length; ++C) {
+            var D = Be.deferredCalls[C];
+            Be.deferredCalls.splice(C, 1), --C, D.targetFunction.apply(null, D.argsList);
           }
       }, eventHandlers: [], removeAllHandlersOnTarget: function(C, D) {
-        for (var B = 0; B < Le.eventHandlers.length; ++B)
-          Le.eventHandlers[B].target == C && (!D || D == Le.eventHandlers[B].eventTypeString) && Le._removeHandler(B--);
+        for (var B = 0; B < Be.eventHandlers.length; ++B)
+          Be.eventHandlers[B].target == C && (!D || D == Be.eventHandlers[B].eventTypeString) && Be._removeHandler(B--);
       }, _removeHandler: function(C) {
-        var D = Le.eventHandlers[C];
-        D.target.removeEventListener(D.eventTypeString, D.eventListenerFunc, D.useCapture), Le.eventHandlers.splice(C, 1);
+        var D = Be.eventHandlers[C];
+        D.target.removeEventListener(D.eventTypeString, D.eventListenerFunc, D.useCapture), Be.eventHandlers.splice(C, 1);
       }, registerOrRemoveHandler: function(C) {
         var D = function(ue) {
-          ++Le.inEventHandler, Le.currentEventHandler = C, Le.runDeferredCalls(), C.handlerFunc(ue), Le.runDeferredCalls(), --Le.inEventHandler;
+          ++Be.inEventHandler, Be.currentEventHandler = C, Be.runDeferredCalls(), C.handlerFunc(ue), Be.runDeferredCalls(), --Be.inEventHandler;
         };
         if (C.callbackfunc)
-          C.eventListenerFunc = D, C.target.addEventListener(C.eventTypeString, D, C.useCapture), Le.eventHandlers.push(C), Le.registerRemoveEventListeners();
+          C.eventListenerFunc = D, C.target.addEventListener(C.eventTypeString, D, C.useCapture), Be.eventHandlers.push(C), Be.registerRemoveEventListeners();
         else
-          for (var B = 0; B < Le.eventHandlers.length; ++B)
-            Le.eventHandlers[B].target == C.target && Le.eventHandlers[B].eventTypeString == C.eventTypeString && Le._removeHandler(B--);
+          for (var B = 0; B < Be.eventHandlers.length; ++B)
+            Be.eventHandlers[B].target == C.target && Be.eventHandlers[B].eventTypeString == C.eventTypeString && Be._removeHandler(B--);
       }, queueEventHandlerOnThread_iiii: function(C, D, B, Q, ue) {
-        Ti(function() {
-          var pe = Fi(12);
-          l()[pe >> 2] = B, l()[pe + 4 >> 2] = Q, l()[pe + 8 >> 2] = ue, _f(C, 637534208, D, Q, pe);
+        _i(function() {
+          var pe = Pi(12);
+          l()[pe >> 2] = B, l()[pe + 4 >> 2] = Q, l()[pe + 8 >> 2] = ue, Ef(C, 637534208, D, Q, pe);
         });
       }, getTargetThreadForEventCallback: function(C) {
         switch (C) {
           case 1:
             return 0;
           case 2:
-            return $e.currentProxiedOperationCallerThread;
+            return _e.currentProxiedOperationCallerThread;
           default:
             return C;
         }
@@ -2082,33 +2082,33 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, fullscreenEnabled: function() {
         return document.fullscreenEnabled || document.webkitFullscreenEnabled;
       } };
-      function Ah(C) {
-        var D = ki(C) + 1, B = $f(D);
+      function Rh(C) {
+        var D = Si(C) + 1, B = Af(D);
         return Ls(C, B, D), B;
       }
-      function Eh(C, D, B, Q) {
-        Ti(function() {
-          var ue = Fi(12), pe = 0;
-          D && (pe = Ah(D)), l()[ue >> 2] = pe, l()[ue + 4 >> 2] = B, l()[ue + 8 >> 2] = Q, _f(C, 657457152, 0, pe, ue);
+      function Dh(C, D, B, Q) {
+        _i(function() {
+          var ue = Pi(12), pe = 0;
+          D && (pe = Rh(D)), l()[ue >> 2] = pe, l()[ue + 4 >> 2] = B, l()[ue + 8 >> 2] = Q, Ef(C, 657457152, 0, pe, ue);
         });
       }
-      function Rh(C, D, B, Q) {
-        D = D ? tn(D) : "", Eh(C, D, B, Q);
+      function Fh(C, D, B, Q) {
+        D = D ? tn(D) : "", Dh(C, D, B, Q);
       }
-      function Dh(C) {
+      function Oh(C) {
         return C > 2 ? tn(C) : C;
       }
-      var Fh = [0, typeof document != "undefined" ? document : 0, typeof window != "undefined" ? window : 0];
-      function Oh(C) {
-        C = Dh(C);
-        var D = Fh[C] || (typeof document != "undefined" ? document.querySelector(C) : void 0);
+      var Ph = [0, typeof document != "undefined" ? document : 0, typeof window != "undefined" ? window : 0];
+      function zh(C) {
+        C = Oh(C);
+        var D = Ph[C] || (typeof document != "undefined" ? document.querySelector(C) : void 0);
         return D;
       }
-      function yu(C) {
-        return Oh(C);
+      function vu(C) {
+        return zh(C);
       }
       function Cc(C, D, B) {
-        var Q = yu(C);
+        var Q = vu(C);
         if (!Q)
           return -4;
         if (Q.canvasSharedPtr && (l()[Q.canvasSharedPtr >> 2] = D, l()[Q.canvasSharedPtr + 4 >> 2] = B), Q.offscreenCanvas || !Q.controlTransferredOffscreen) {
@@ -2121,22 +2121,22 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           Q.width = D, Q.height = B, ue && Q.GLctxObject.GLctx.viewport(0, 0, D, B);
         } else if (Q.canvasSharedPtr) {
           var ye = l()[Q.canvasSharedPtr + 8 >> 2];
-          return Rh(ye, C, D, B), 1;
+          return Fh(ye, C, D, B), 1;
         } else
           return -4;
         return 0;
       }
       function Nc(C, D, B) {
-        return N ? Lr(2, 1, C, D, B) : Cc(C, D, B);
+        return N ? Br(2, 1, C, D, B) : Cc(C, D, B);
       }
-      function Ph(C, D, B) {
-        var Q = yu(C);
+      function Mh(C, D, B) {
+        var Q = vu(C);
         return Q ? Cc(C, D, B) : Nc(C, D, B);
       }
-      function zh() {
+      function Lh() {
         throw "unwind";
       }
-      function Mh(C) {
+      function Bh(C) {
         var D = C.getExtension("ANGLE_instanced_arrays");
         if (D)
           return C.vertexAttribDivisor = function(B, Q) {
@@ -2147,7 +2147,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             D.drawElementsInstancedANGLE(B, Q, ue, pe, ye);
           }, 1;
       }
-      function Lh(C) {
+      function Vh(C) {
         var D = C.getExtension("OES_vertex_array_object");
         if (D)
           return C.createVertexArray = function() {
@@ -2160,14 +2160,14 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             return D.isVertexArrayOES(B);
           }, 1;
       }
-      function Bh(C) {
+      function Wh(C) {
         var D = C.getExtension("WEBGL_draw_buffers");
         if (D)
           return C.drawBuffers = function(B, Q) {
             D.drawBuffersWEBGL(B, Q);
           }, 1;
       }
-      function Vh(C) {
+      function Uh(C) {
         return !!(C.multiDrawWebgl = C.getExtension("WEBGL_multi_draw"));
       }
       var gt = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function(D) {
@@ -2193,7 +2193,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var Q = gt.registerContext(B, D);
         return Q;
       }, registerContext: function(C, D) {
-        var B = $f(8);
+        var B = Af(8);
         l()[B + 4 >> 2] = Oc();
         var Q = { handle: B, attributes: D, version: D.majorVersion, GLctx: C };
         return C.canvas && (C.canvas.GLctxObject = Q), gt.contexts[B] = Q, (typeof D.enableExtensionsByDefault == "undefined" || D.enableExtensionsByDefault) && gt.initExtensions(Q), B;
@@ -2202,34 +2202,34 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, getContext: function(C) {
         return gt.contexts[C];
       }, deleteContext: function(C) {
-        gt.currentContext === gt.contexts[C] && (gt.currentContext = null), typeof Le == "object" && Le.removeAllHandlersOnTarget(gt.contexts[C].GLctx.canvas), gt.contexts[C] && gt.contexts[C].GLctx.canvas && (gt.contexts[C].GLctx.canvas.GLctxObject = void 0), qv(gt.contexts[C].handle), gt.contexts[C] = null;
+        gt.currentContext === gt.contexts[C] && (gt.currentContext = null), typeof Be == "object" && Be.removeAllHandlersOnTarget(gt.contexts[C].GLctx.canvas), gt.contexts[C] && gt.contexts[C].GLctx.canvas && (gt.contexts[C].GLctx.canvas.GLctxObject = void 0), Kv(gt.contexts[C].handle), gt.contexts[C] = null;
       }, initExtensions: function(C) {
         if (C || (C = gt.currentContext), !C.initExtensionsDone) {
           C.initExtensionsDone = true;
           var D = C.GLctx;
-          Mh(D), Lh(D), Bh(D), D.disjointTimerQueryExt = D.getExtension("EXT_disjoint_timer_query"), Vh(D);
+          Bh(D), Vh(D), Wh(D), D.disjointTimerQueryExt = D.getExtension("EXT_disjoint_timer_query"), Uh(D);
           var B = D.getSupportedExtensions() || [];
           B.forEach(function(Q) {
             !Q.includes("lose_context") && !Q.includes("debug") && D.getExtension(Q);
           });
         }
-      } }, Wh = ["default", "low-power", "high-performance"];
-      function Uh(C, D) {
-        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Wh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = yu(C);
+      } }, Gh = ["default", "low-power", "high-performance"];
+      function Hh(C, D) {
+        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Gh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = vu(C);
         if (!pe || ue.explicitSwapControl)
           return 0;
         var ye = gt.createContext(pe, ue);
         return ye;
       }
-      function Gh(C, D) {
-        return Uh(C, D);
+      function qh(C, D) {
+        return Hh(C, D);
       }
-      var _i = { mappings: {}, buffers: [null, [], []], printChar: function(C, D) {
-        var B = _i.buffers[C];
+      var Ei = { mappings: {}, buffers: [null, [], []], printChar: function(C, D) {
+        var B = Ei.buffers[C];
         D === 0 || D === 10 ? ((C === 1 ? te : ee)(Dn(B, 0)), B.length = 0) : B.push(D);
       }, varargs: void 0, get: function() {
-        _i.varargs += 4;
-        var C = l()[_i.varargs - 4 >> 2];
+        Ei.varargs += 4;
+        var C = l()[Ei.varargs - 4 >> 2];
         return C;
       }, getStr: function(C) {
         var D = tn(C);
@@ -2238,112 +2238,112 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return C;
       } };
       function Tc(C) {
-        return N ? Lr(3, 1, C) : 0;
+        return N ? Br(3, 1, C) : 0;
       }
       function $c(C, D, B, Q, ue) {
         if (N)
-          return Lr(4, 1, C, D, B, Q, ue);
+          return Br(4, 1, C, D, B, Q, ue);
       }
       function _c(C, D, B, Q) {
         if (N)
-          return Lr(5, 1, C, D, B, Q);
+          return Br(5, 1, C, D, B, Q);
         for (var ue = 0, pe = 0; pe < B; pe++) {
           var ye = l()[D >> 2], Te = l()[D + 4 >> 2];
           D += 8;
           for (var bt = 0; bt < Te; bt++)
-            _i.printChar(C, i()[ye + bt]);
+            Ei.printChar(C, i()[ye + bt]);
           ue += Te;
         }
         return l()[Q >> 2] = ue, 0;
       }
-      function Hh(C) {
+      function jh(C) {
         Ee(C);
       }
-      $e.init();
-      var Ac, qh = [null, kc, Nc, Tc, $c, _c], Uv = false, Ec = { __clock_gettime: bh, __emscripten_init_main_thread_js: yh, __emscripten_thread_cleanup: vh, __pthread_create_js: xh, _emscripten_default_pthread_stack_size: wh, _emscripten_notify_thread_queue: kh, abort: Ih, emscripten_check_blocking_allowed: Sh, emscripten_get_heap_max: Sc, emscripten_get_now: Mr, emscripten_memcpy_big: Ch, emscripten_num_logical_cores: Nh, emscripten_receive_on_main_thread_js: Th, emscripten_resize_heap: _h, emscripten_set_canvas_element_size: Ph, emscripten_unwind_to_js_event_loop: zh, emscripten_webgl_create_context: Gh, exit: wc, fd_close: Tc, fd_seek: $c, fd_write: _c, memory: Ce || d.wasmMemory, setTempRet0: Hh }, Gv = ih(), jh = d.___wasm_call_ctors = function() {
-        return (jh = d.___wasm_call_ctors = d.asm.__wasm_call_ctors).apply(null, arguments);
-      }, Kh = d._init = function() {
-        return (Kh = d._init = d.asm.init).apply(null, arguments);
-      }, Xh = d._init_with_threads_count = function() {
-        return (Xh = d._init_with_threads_count = d.asm.init_with_threads_count).apply(null, arguments);
-      }, Yh = d._get_threads_count = function() {
-        return (Yh = d._get_threads_count = d.asm.get_threads_count).apply(null, arguments);
-      }, Qh = d._register_tensor = function() {
-        return (Qh = d._register_tensor = d.asm.register_tensor).apply(null, arguments);
-      }, Zh = d._dispose_data = function() {
-        return (Zh = d._dispose_data = d.asm.dispose_data).apply(null, arguments);
-      }, Jh = d._dispose = function() {
-        return (Jh = d._dispose = d.asm.dispose).apply(null, arguments);
-      }, ef = d._Abs = function() {
-        return (ef = d._Abs = d.asm.Abs).apply(null, arguments);
-      }, tf = d._Add = function() {
-        return (tf = d._Add = d.asm.Add).apply(null, arguments);
-      }, nf = d._AddN = function() {
-        return (nf = d._AddN = d.asm.AddN).apply(null, arguments);
-      }, sf = d._All = function() {
-        return (sf = d._All = d.asm.All).apply(null, arguments);
-      }, rf = d._Any = function() {
-        return (rf = d._Any = d.asm.Any).apply(null, arguments);
-      }, af = d._ArgMax = function() {
-        return (af = d._ArgMax = d.asm.ArgMax).apply(null, arguments);
-      }, of = d._AvgPool = function() {
-        return (of = d._AvgPool = d.asm.AvgPool).apply(null, arguments);
-      }, uf = d._BatchMatMul = function() {
-        return (uf = d._BatchMatMul = d.asm.BatchMatMul).apply(null, arguments);
-      }, lf = d._Ceil = function() {
-        return (lf = d._Ceil = d.asm.Ceil).apply(null, arguments);
-      }, cf = d._ClipByValue = function() {
-        return (cf = d._ClipByValue = d.asm.ClipByValue).apply(null, arguments);
-      }, df = d._Conv2D = function() {
-        return (df = d._Conv2D = d.asm.Conv2D).apply(null, arguments);
-      }, pf = d._Conv2DBackpropInput = function() {
-        return (pf = d._Conv2DBackpropInput = d.asm.Conv2DBackpropInput).apply(null, arguments);
-      }, hf = d._Cos = function() {
-        return (hf = d._Cos = d.asm.Cos).apply(null, arguments);
-      }, ff = d._Cosh = function() {
-        return (ff = d._Cosh = d.asm.Cosh).apply(null, arguments);
-      }, mf = d._CropAndResize = function() {
-        return (mf = d._CropAndResize = d.asm.CropAndResize).apply(null, arguments);
-      }, gf = d._Cumprod = function() {
-        return (gf = d._Cumprod = d.asm.Cumprod).apply(null, arguments);
-      }, bf = d._Cumsum = function() {
-        return (bf = d._Cumsum = d.asm.Cumsum).apply(null, arguments);
-      }, yf = d._DepthToSpace = function() {
-        return (yf = d._DepthToSpace = d.asm.DepthToSpace).apply(null, arguments);
-      }, vf = d._DepthwiseConv2dNative = function() {
-        return (vf = d._DepthwiseConv2dNative = d.asm.DepthwiseConv2dNative).apply(null, arguments);
-      }, xf = d._Elu = function() {
-        return (xf = d._Elu = d.asm.Elu).apply(null, arguments);
-      }, wf = d._Equal = function() {
-        return (wf = d._Equal = d.asm.Equal).apply(null, arguments);
-      }, kf = d._Exp = function() {
-        return (kf = d._Exp = d.asm.Exp).apply(null, arguments);
-      }, If = d._FlipLeftRight = function() {
-        return (If = d._FlipLeftRight = d.asm.FlipLeftRight).apply(null, arguments);
+      _e.init();
+      var Ac, Kh = [null, kc, Nc, Tc, $c, _c], Hv = false, Ec = { __clock_gettime: vh, __emscripten_init_main_thread_js: xh, __emscripten_thread_cleanup: wh, __pthread_create_js: kh, _emscripten_default_pthread_stack_size: Ih, _emscripten_notify_thread_queue: Sh, abort: Ch, emscripten_check_blocking_allowed: Nh, emscripten_get_heap_max: Sc, emscripten_get_now: Lr, emscripten_memcpy_big: Th, emscripten_num_logical_cores: $h, emscripten_receive_on_main_thread_js: _h, emscripten_resize_heap: Eh, emscripten_set_canvas_element_size: Mh, emscripten_unwind_to_js_event_loop: Lh, emscripten_webgl_create_context: qh, exit: wc, fd_close: Tc, fd_seek: $c, fd_write: _c, memory: Ce || d.wasmMemory, setTempRet0: jh }, qv = uh(), Xh = d.___wasm_call_ctors = function() {
+        return (Xh = d.___wasm_call_ctors = d.asm.__wasm_call_ctors).apply(null, arguments);
+      }, Yh = d._init = function() {
+        return (Yh = d._init = d.asm.init).apply(null, arguments);
+      }, Qh = d._init_with_threads_count = function() {
+        return (Qh = d._init_with_threads_count = d.asm.init_with_threads_count).apply(null, arguments);
+      }, Zh = d._get_threads_count = function() {
+        return (Zh = d._get_threads_count = d.asm.get_threads_count).apply(null, arguments);
+      }, Jh = d._register_tensor = function() {
+        return (Jh = d._register_tensor = d.asm.register_tensor).apply(null, arguments);
+      }, ef = d._dispose_data = function() {
+        return (ef = d._dispose_data = d.asm.dispose_data).apply(null, arguments);
+      }, tf = d._dispose = function() {
+        return (tf = d._dispose = d.asm.dispose).apply(null, arguments);
+      }, nf = d._Abs = function() {
+        return (nf = d._Abs = d.asm.Abs).apply(null, arguments);
+      }, sf = d._Add = function() {
+        return (sf = d._Add = d.asm.Add).apply(null, arguments);
+      }, rf = d._AddN = function() {
+        return (rf = d._AddN = d.asm.AddN).apply(null, arguments);
+      }, af = d._All = function() {
+        return (af = d._All = d.asm.All).apply(null, arguments);
+      }, of = d._Any = function() {
+        return (of = d._Any = d.asm.Any).apply(null, arguments);
+      }, uf = d._ArgMax = function() {
+        return (uf = d._ArgMax = d.asm.ArgMax).apply(null, arguments);
+      }, lf = d._AvgPool = function() {
+        return (lf = d._AvgPool = d.asm.AvgPool).apply(null, arguments);
+      }, cf = d._BatchMatMul = function() {
+        return (cf = d._BatchMatMul = d.asm.BatchMatMul).apply(null, arguments);
+      }, df = d._Ceil = function() {
+        return (df = d._Ceil = d.asm.Ceil).apply(null, arguments);
+      }, pf = d._ClipByValue = function() {
+        return (pf = d._ClipByValue = d.asm.ClipByValue).apply(null, arguments);
+      }, hf = d._Conv2D = function() {
+        return (hf = d._Conv2D = d.asm.Conv2D).apply(null, arguments);
+      }, ff = d._Conv2DBackpropInput = function() {
+        return (ff = d._Conv2DBackpropInput = d.asm.Conv2DBackpropInput).apply(null, arguments);
+      }, mf = d._Cos = function() {
+        return (mf = d._Cos = d.asm.Cos).apply(null, arguments);
+      }, gf = d._Cosh = function() {
+        return (gf = d._Cosh = d.asm.Cosh).apply(null, arguments);
+      }, bf = d._CropAndResize = function() {
+        return (bf = d._CropAndResize = d.asm.CropAndResize).apply(null, arguments);
+      }, yf = d._Cumprod = function() {
+        return (yf = d._Cumprod = d.asm.Cumprod).apply(null, arguments);
+      }, vf = d._Cumsum = function() {
+        return (vf = d._Cumsum = d.asm.Cumsum).apply(null, arguments);
+      }, xf = d._DepthToSpace = function() {
+        return (xf = d._DepthToSpace = d.asm.DepthToSpace).apply(null, arguments);
+      }, wf = d._DepthwiseConv2dNative = function() {
+        return (wf = d._DepthwiseConv2dNative = d.asm.DepthwiseConv2dNative).apply(null, arguments);
+      }, kf = d._Elu = function() {
+        return (kf = d._Elu = d.asm.Elu).apply(null, arguments);
+      }, If = d._Equal = function() {
+        return (If = d._Equal = d.asm.Equal).apply(null, arguments);
+      }, Sf = d._Exp = function() {
+        return (Sf = d._Exp = d.asm.Exp).apply(null, arguments);
+      }, Cf = d._FlipLeftRight = function() {
+        return (Cf = d._FlipLeftRight = d.asm.FlipLeftRight).apply(null, arguments);
       }, Rc = d._Floor = function() {
         return (Rc = d._Floor = d.asm.Floor).apply(null, arguments);
       }, Dc = d._FloorDiv = function() {
         return (Dc = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments);
-      }, vu = d._FusedBatchNorm = function() {
-        return (vu = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
-      }, Sf = d._FusedConv2D = function() {
-        return (Sf = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
-      }, Cf = d._FusedDepthwiseConv2D = function() {
-        return (Cf = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
-      }, Ai = d._Gather = function() {
-        return (Ai = d._Gather = d.asm.Gather).apply(null, arguments);
-      }, xu = d._GatherNd = function() {
-        return (xu = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
-      }, wu = d._Greater = function() {
-        return (wu = d._Greater = d.asm.Greater).apply(null, arguments);
-      }, Hv = d._GreaterEqual = function() {
-        return (Hv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
-      }, Ei = d._LeakyRelu = function() {
-        return (Ei = d._LeakyRelu = d.asm.LeakyRelu).apply(null, arguments);
-      }, Ri = d._Less = function() {
-        return (Ri = d._Less = d.asm.Less).apply(null, arguments);
-      }, Nf = d._LessEqual = function() {
-        return (Nf = d._LessEqual = d.asm.LessEqual).apply(null, arguments);
+      }, xu = d._FusedBatchNorm = function() {
+        return (xu = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
+      }, Nf = d._FusedConv2D = function() {
+        return (Nf = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
+      }, Tf = d._FusedDepthwiseConv2D = function() {
+        return (Tf = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
+      }, Ri = d._Gather = function() {
+        return (Ri = d._Gather = d.asm.Gather).apply(null, arguments);
+      }, wu = d._GatherNd = function() {
+        return (wu = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
+      }, ku = d._Greater = function() {
+        return (ku = d._Greater = d.asm.Greater).apply(null, arguments);
+      }, jv = d._GreaterEqual = function() {
+        return (jv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
+      }, Di = d._LeakyRelu = function() {
+        return (Di = d._LeakyRelu = d.asm.LeakyRelu).apply(null, arguments);
+      }, Fi = d._Less = function() {
+        return (Fi = d._Less = d.asm.Less).apply(null, arguments);
+      }, $f = d._LessEqual = function() {
+        return ($f = d._LessEqual = d.asm.LessEqual).apply(null, arguments);
       }, H = d._Log = function() {
         return (H = d._Log = d.asm.Log).apply(null, arguments);
       }, J = d._LogicalAnd = function() {
@@ -2366,150 +2366,150 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return (is = d._Multiply = d.asm.Multiply).apply(null, arguments);
       }, os = d._Neg = function() {
         return (os = d._Neg = d.asm.Neg).apply(null, arguments);
-      }, Di = d._NonMaxSuppressionV3 = function() {
-        return (Di = d._NonMaxSuppressionV3 = d.asm.NonMaxSuppressionV3).apply(null, arguments);
-      }, Br = d._NonMaxSuppressionV4 = function() {
-        return (Br = d._NonMaxSuppressionV4 = d.asm.NonMaxSuppressionV4).apply(null, arguments);
-      }, Tf = d._NonMaxSuppressionV5 = function() {
-        return (Tf = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments);
+      }, Oi = d._NonMaxSuppressionV3 = function() {
+        return (Oi = d._NonMaxSuppressionV3 = d.asm.NonMaxSuppressionV3).apply(null, arguments);
+      }, Vr = d._NonMaxSuppressionV4 = function() {
+        return (Vr = d._NonMaxSuppressionV4 = d.asm.NonMaxSuppressionV4).apply(null, arguments);
+      }, _f = d._NonMaxSuppressionV5 = function() {
+        return (_f = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments);
       }, an = d._NotEqual = function() {
         return (an = d._NotEqual = d.asm.NotEqual).apply(null, arguments);
-      }, nr = d._OneHot = function() {
-        return (nr = d._OneHot = d.asm.OneHot).apply(null, arguments);
+      }, sr = d._OneHot = function() {
+        return (sr = d._OneHot = d.asm.OneHot).apply(null, arguments);
       }, Fc = d._PadV2 = function() {
         return (Fc = d._PadV2 = d.asm.PadV2).apply(null, arguments);
-      }, jN = d._Pow = function() {
-        return (jN = d._Pow = d.asm.Pow).apply(null, arguments);
-      }, KN = d._Prelu = function() {
-        return (KN = d._Prelu = d.asm.Prelu).apply(null, arguments);
-      }, XN = d._Prod = function() {
-        return (XN = d._Prod = d.asm.Prod).apply(null, arguments);
-      }, YN = d._RealDiv = function() {
-        return (YN = d._RealDiv = d.asm.RealDiv).apply(null, arguments);
-      }, QN = d._Relu = function() {
-        return (QN = d._Relu = d.asm.Relu).apply(null, arguments);
-      }, ZN = d._Relu6 = function() {
-        return (ZN = d._Relu6 = d.asm.Relu6).apply(null, arguments);
-      }, JN = d._ResizeBilinear = function() {
-        return (JN = d._ResizeBilinear = d.asm.ResizeBilinear).apply(null, arguments);
-      }, eT = d._Reverse = function() {
-        return (eT = d._Reverse = d.asm.Reverse).apply(null, arguments);
-      }, tT = d._RotateWithOffset = function() {
-        return (tT = d._RotateWithOffset = d.asm.RotateWithOffset).apply(null, arguments);
-      }, nT = d._Round = function() {
-        return (nT = d._Round = d.asm.Round).apply(null, arguments);
-      }, sT = d._Rsqrt = function() {
-        return (sT = d._Rsqrt = d.asm.Rsqrt).apply(null, arguments);
-      }, rT = d._ScatterNd = function() {
-        return (rT = d._ScatterNd = d.asm.ScatterNd).apply(null, arguments);
-      }, aT = d._SelectV2 = function() {
-        return (aT = d._SelectV2 = d.asm.SelectV2).apply(null, arguments);
-      }, iT = d._Sigmoid = function() {
-        return (iT = d._Sigmoid = d.asm.Sigmoid).apply(null, arguments);
-      }, oT = d._Sin = function() {
-        return (oT = d._Sin = d.asm.Sin).apply(null, arguments);
-      }, uT = d._Softmax = function() {
-        return (uT = d._Softmax = d.asm.Softmax).apply(null, arguments);
-      }, lT = d._SparseFillEmptyRows = function() {
-        return (lT = d._SparseFillEmptyRows = d.asm.SparseFillEmptyRows).apply(null, arguments);
-      }, cT = d._SparseReshape = function() {
-        return (cT = d._SparseReshape = d.asm.SparseReshape).apply(null, arguments);
-      }, dT = d._SparseSegmentReduction = function() {
-        return (dT = d._SparseSegmentReduction = d.asm.SparseSegmentReduction).apply(null, arguments);
-      }, pT = d._Sqrt = function() {
-        return (pT = d._Sqrt = d.asm.Sqrt).apply(null, arguments);
-      }, hT = d._Square = function() {
-        return (hT = d._Square = d.asm.Square).apply(null, arguments);
-      }, fT = d._SquaredDifference = function() {
-        return (fT = d._SquaredDifference = d.asm.SquaredDifference).apply(null, arguments);
-      }, mT = d._Step = function() {
-        return (mT = d._Step = d.asm.Step).apply(null, arguments);
-      }, gT = d._StridedSlice = function() {
-        return (gT = d._StridedSlice = d.asm.StridedSlice).apply(null, arguments);
-      }, bT = d._Sub = function() {
-        return (bT = d._Sub = d.asm.Sub).apply(null, arguments);
-      }, yT = d._Sum = function() {
-        return (yT = d._Sum = d.asm.Sum).apply(null, arguments);
-      }, vT = d._Tan = function() {
-        return (vT = d._Tan = d.asm.Tan).apply(null, arguments);
-      }, xT = d._Tanh = function() {
-        return (xT = d._Tanh = d.asm.Tanh).apply(null, arguments);
-      }, wT = d._Tile = function() {
-        return (wT = d._Tile = d.asm.Tile).apply(null, arguments);
-      }, kT = d._TopK = function() {
-        return (kT = d._TopK = d.asm.TopK).apply(null, arguments);
-      }, IT = d._Transform = function() {
-        return (IT = d._Transform = d.asm.Transform).apply(null, arguments);
-      }, ST = d._Transpose = function() {
-        return (ST = d._Transpose = d.asm.Transpose).apply(null, arguments);
-      }, CT = d.__FusedMatMul = function() {
-        return (CT = d.__FusedMatMul = d.asm._FusedMatMul).apply(null, arguments);
-      }, $f = d._malloc = function() {
-        return ($f = d._malloc = d.asm.malloc).apply(null, arguments);
-      }, qv = d._free = function() {
-        return (qv = d._free = d.asm.free).apply(null, arguments);
-      }, NT = d._emscripten_tls_init = function() {
-        return (NT = d._emscripten_tls_init = d.asm.emscripten_tls_init).apply(null, arguments);
-      }, jv = d.___errno_location = function() {
-        return (jv = d.___errno_location = d.asm.__errno_location).apply(null, arguments);
+      }, JN = d._Pow = function() {
+        return (JN = d._Pow = d.asm.Pow).apply(null, arguments);
+      }, eT = d._Prelu = function() {
+        return (eT = d._Prelu = d.asm.Prelu).apply(null, arguments);
+      }, tT = d._Prod = function() {
+        return (tT = d._Prod = d.asm.Prod).apply(null, arguments);
+      }, nT = d._RealDiv = function() {
+        return (nT = d._RealDiv = d.asm.RealDiv).apply(null, arguments);
+      }, sT = d._Relu = function() {
+        return (sT = d._Relu = d.asm.Relu).apply(null, arguments);
+      }, rT = d._Relu6 = function() {
+        return (rT = d._Relu6 = d.asm.Relu6).apply(null, arguments);
+      }, aT = d._ResizeBilinear = function() {
+        return (aT = d._ResizeBilinear = d.asm.ResizeBilinear).apply(null, arguments);
+      }, iT = d._Reverse = function() {
+        return (iT = d._Reverse = d.asm.Reverse).apply(null, arguments);
+      }, oT = d._RotateWithOffset = function() {
+        return (oT = d._RotateWithOffset = d.asm.RotateWithOffset).apply(null, arguments);
+      }, uT = d._Round = function() {
+        return (uT = d._Round = d.asm.Round).apply(null, arguments);
+      }, lT = d._Rsqrt = function() {
+        return (lT = d._Rsqrt = d.asm.Rsqrt).apply(null, arguments);
+      }, cT = d._ScatterNd = function() {
+        return (cT = d._ScatterNd = d.asm.ScatterNd).apply(null, arguments);
+      }, dT = d._SelectV2 = function() {
+        return (dT = d._SelectV2 = d.asm.SelectV2).apply(null, arguments);
+      }, pT = d._Sigmoid = function() {
+        return (pT = d._Sigmoid = d.asm.Sigmoid).apply(null, arguments);
+      }, hT = d._Sin = function() {
+        return (hT = d._Sin = d.asm.Sin).apply(null, arguments);
+      }, fT = d._Softmax = function() {
+        return (fT = d._Softmax = d.asm.Softmax).apply(null, arguments);
+      }, mT = d._SparseFillEmptyRows = function() {
+        return (mT = d._SparseFillEmptyRows = d.asm.SparseFillEmptyRows).apply(null, arguments);
+      }, gT = d._SparseReshape = function() {
+        return (gT = d._SparseReshape = d.asm.SparseReshape).apply(null, arguments);
+      }, bT = d._SparseSegmentReduction = function() {
+        return (bT = d._SparseSegmentReduction = d.asm.SparseSegmentReduction).apply(null, arguments);
+      }, yT = d._Sqrt = function() {
+        return (yT = d._Sqrt = d.asm.Sqrt).apply(null, arguments);
+      }, vT = d._Square = function() {
+        return (vT = d._Square = d.asm.Square).apply(null, arguments);
+      }, xT = d._SquaredDifference = function() {
+        return (xT = d._SquaredDifference = d.asm.SquaredDifference).apply(null, arguments);
+      }, wT = d._Step = function() {
+        return (wT = d._Step = d.asm.Step).apply(null, arguments);
+      }, kT = d._StridedSlice = function() {
+        return (kT = d._StridedSlice = d.asm.StridedSlice).apply(null, arguments);
+      }, IT = d._Sub = function() {
+        return (IT = d._Sub = d.asm.Sub).apply(null, arguments);
+      }, ST = d._Sum = function() {
+        return (ST = d._Sum = d.asm.Sum).apply(null, arguments);
+      }, CT = d._Tan = function() {
+        return (CT = d._Tan = d.asm.Tan).apply(null, arguments);
+      }, NT = d._Tanh = function() {
+        return (NT = d._Tanh = d.asm.Tanh).apply(null, arguments);
+      }, TT = d._Tile = function() {
+        return (TT = d._Tile = d.asm.Tile).apply(null, arguments);
+      }, $T = d._TopK = function() {
+        return ($T = d._TopK = d.asm.TopK).apply(null, arguments);
+      }, _T = d._Transform = function() {
+        return (_T = d._Transform = d.asm.Transform).apply(null, arguments);
+      }, AT = d._Transpose = function() {
+        return (AT = d._Transpose = d.asm.Transpose).apply(null, arguments);
+      }, ET = d.__FusedMatMul = function() {
+        return (ET = d.__FusedMatMul = d.asm._FusedMatMul).apply(null, arguments);
+      }, Af = d._malloc = function() {
+        return (Af = d._malloc = d.asm.malloc).apply(null, arguments);
+      }, Kv = d._free = function() {
+        return (Kv = d._free = d.asm.free).apply(null, arguments);
+      }, RT = d._emscripten_tls_init = function() {
+        return (RT = d._emscripten_tls_init = d.asm.emscripten_tls_init).apply(null, arguments);
+      }, Xv = d.___errno_location = function() {
+        return (Xv = d.___errno_location = d.asm.__errno_location).apply(null, arguments);
       }, Oc = d._pthread_self = function() {
         return (Oc = d._pthread_self = d.asm.pthread_self).apply(null, arguments);
-      }, Kv = d._emscripten_main_thread_process_queued_calls = function() {
-        return (Kv = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
-      }, TT = d.__emscripten_thread_crashed = function() {
-        return (TT = d.__emscripten_thread_crashed = d.asm._emscripten_thread_crashed).apply(null, arguments);
-      }, Xv = d.__emscripten_thread_init = function() {
-        return (Xv = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments);
-      }, $T = d._emscripten_current_thread_process_queued_calls = function() {
-        return ($T = d._emscripten_current_thread_process_queued_calls = d.asm.emscripten_current_thread_process_queued_calls).apply(null, arguments);
-      }, _T = d._emscripten_main_browser_thread_id = function() {
-        return (_T = d._emscripten_main_browser_thread_id = d.asm.emscripten_main_browser_thread_id).apply(null, arguments);
-      }, AT = d._emscripten_sync_run_in_main_thread_2 = function() {
-        return (AT = d._emscripten_sync_run_in_main_thread_2 = d.asm.emscripten_sync_run_in_main_thread_2).apply(null, arguments);
-      }, Yv = d._emscripten_sync_run_in_main_thread_4 = function() {
-        return (Yv = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments);
-      }, Qv = d._emscripten_run_in_main_runtime_thread_js = function() {
-        return (Qv = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
-      }, _f = d._emscripten_dispatch_to_thread_ = function() {
-        return (_f = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
-      }, Af = d.__emscripten_thread_free_data = function() {
-        return (Af = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments);
-      }, ET = d.__emscripten_thread_exit = function() {
-        return (ET = d.__emscripten_thread_exit = d.asm._emscripten_thread_exit).apply(null, arguments);
-      }, RT = d._memalign = function() {
-        return (RT = d._memalign = d.asm.memalign).apply(null, arguments);
-      }, Zv = d._emscripten_stack_set_limits = function() {
-        return (Zv = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments);
-      }, Ef = d.stackSave = function() {
-        return (Ef = d.stackSave = d.asm.stackSave).apply(null, arguments);
+      }, Yv = d._emscripten_main_thread_process_queued_calls = function() {
+        return (Yv = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
+      }, DT = d.__emscripten_thread_crashed = function() {
+        return (DT = d.__emscripten_thread_crashed = d.asm._emscripten_thread_crashed).apply(null, arguments);
+      }, Qv = d.__emscripten_thread_init = function() {
+        return (Qv = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments);
+      }, FT = d._emscripten_current_thread_process_queued_calls = function() {
+        return (FT = d._emscripten_current_thread_process_queued_calls = d.asm.emscripten_current_thread_process_queued_calls).apply(null, arguments);
+      }, OT = d._emscripten_main_browser_thread_id = function() {
+        return (OT = d._emscripten_main_browser_thread_id = d.asm.emscripten_main_browser_thread_id).apply(null, arguments);
+      }, PT = d._emscripten_sync_run_in_main_thread_2 = function() {
+        return (PT = d._emscripten_sync_run_in_main_thread_2 = d.asm.emscripten_sync_run_in_main_thread_2).apply(null, arguments);
+      }, Zv = d._emscripten_sync_run_in_main_thread_4 = function() {
+        return (Zv = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments);
+      }, Jv = d._emscripten_run_in_main_runtime_thread_js = function() {
+        return (Jv = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
+      }, Ef = d._emscripten_dispatch_to_thread_ = function() {
+        return (Ef = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
+      }, Rf = d.__emscripten_thread_free_data = function() {
+        return (Rf = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments);
+      }, zT = d.__emscripten_thread_exit = function() {
+        return (zT = d.__emscripten_thread_exit = d.asm._emscripten_thread_exit).apply(null, arguments);
+      }, MT = d._memalign = function() {
+        return (MT = d._memalign = d.asm.memalign).apply(null, arguments);
+      }, ex = d._emscripten_stack_set_limits = function() {
+        return (ex = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments);
+      }, Df = d.stackSave = function() {
+        return (Df = d.stackSave = d.asm.stackSave).apply(null, arguments);
       }, Pc = d.stackRestore = function() {
         return (Pc = d.stackRestore = d.asm.stackRestore).apply(null, arguments);
-      }, Fi = d.stackAlloc = function() {
-        return (Fi = d.stackAlloc = d.asm.stackAlloc).apply(null, arguments);
-      }, DT = d.dynCall_iijjiiii = function() {
-        return (DT = d.dynCall_iijjiiii = d.asm.dynCall_iijjiiii).apply(null, arguments);
-      }, FT = d.dynCall_jiji = function() {
-        return (FT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments);
-      }, Jv = d.__emscripten_allow_main_runtime_queued_calls = 21456;
-      d.cwrap = en, d.keepRuntimeAlive = Pr, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = ku;
+      }, Pi = d.stackAlloc = function() {
+        return (Pi = d.stackAlloc = d.asm.stackAlloc).apply(null, arguments);
+      }, LT = d.dynCall_iijjiiii = function() {
+        return (LT = d.dynCall_iijjiiii = d.asm.dynCall_iijjiiii).apply(null, arguments);
+      }, BT = d.dynCall_jiji = function() {
+        return (BT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments);
+      }, tx = d.__emscripten_allow_main_runtime_queued_calls = 21456;
+      d.cwrap = en, d.keepRuntimeAlive = zr, d.PThread = _e, d.PThread = _e, d.wasmMemory = Ce, d.ExitStatus = Iu;
       var zc;
-      function ku(C) {
+      function Iu(C) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + C + ")", this.status = C;
       }
       as = function C() {
-        zc || Rf(), zc || (as = C);
+        zc || Ff(), zc || (as = C);
       };
-      function Rf(C) {
-        if (C = C || b, tr > 0)
+      function Ff(C) {
+        if (C = C || b, nr > 0)
           return;
         if (N) {
-          h(d), fu(), postMessage({ cmd: "loaded" });
+          h(d), mu(), postMessage({ cmd: "loaded" });
           return;
         }
-        if (sn(), tr > 0)
+        if (sn(), nr > 0)
           return;
         function D() {
-          zc || (zc = true, d.calledRun = true, !at && (fu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), rh()));
+          zc || (zc = true, d.calledRun = true, !at && (mu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), ih()));
         }
         d.setStatus ? (d.setStatus("Running..."), setTimeout(function() {
           setTimeout(function() {
@@ -2517,19 +2517,19 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           }, 1), D();
         }, 1)) : D();
       }
-      d.run = Rf;
-      function OT(C, D) {
+      d.run = Ff;
+      function VT(C, D) {
         if (Jt = C, !D && N)
           throw kc(C), "unwind";
-        Pr() || sh(), PT(C);
+        zr() || ah(), WT(C);
       }
-      function PT(C) {
-        Jt = C, Pr() || ($e.terminateAllThreads(), d.onExit && d.onExit(C), at = true), v(C, new ku(C));
+      function WT(C) {
+        Jt = C, zr() || (_e.terminateAllThreads(), d.onExit && d.onExit(C), at = true), v(C, new Iu(C));
       }
       if (d.preInit)
         for (typeof d.preInit == "function" && (d.preInit = [d.preInit]); d.preInit.length > 0; )
           d.preInit.pop()();
-      Rf();
+      Ff();
       var Mc;
       m && (Mc = { uncaughtException: process.listeners("uncaughtException").filter(function(C) {
         return !m.uncaughtException.indexOf(C) > -1;
@@ -2544,9 +2544,9 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       else
         throw new Error("Could not find wasm module in post.js");
       if (Mc) {
-        var zT = Lc._dispose;
+        var UT = Lc._dispose;
         Lc._dispose = function() {
-          zT(), Mc.uncaughtException.forEach(function(C) {
+          UT(), Mc.uncaughtException.forEach(function(C) {
             process.removeListener("uncaughtException", C);
           }), Mc.unhandledRejection.forEach(function(C) {
             process.removeListener("unhandledRejection", C);
@@ -2560,7 +2560,7 @@ var a$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
     return n;
   }) : typeof e == "object" && (e.WasmBackendModuleThreadedSimd = n);
 } });
-var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
+var p$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
   var n = (() => {
     var s = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0;
     return typeof __filename != "undefined" && (s = s || __filename), function(r) {
@@ -2579,13 +2579,13 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       var y, v, x, k;
       function T(H) {
-        if (H instanceof xu)
+        if (H instanceof wu)
           return;
         R("exiting due to exception: " + H);
       }
       var N, E, A;
       m ? (f ? g = dd().dirname(g) + "/" : g = __dirname + "/", A = () => {
-        E || (N = tg(), E = dd());
+        E || (N = sg(), E = dd());
       }, y = function(J, de) {
         return A(), J = E.normalize(J), N.readFileSync(J, de ? void 0 : "utf8");
       }, x = (H) => {
@@ -2596,12 +2596,12 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           Ie ? de(Ie) : J(Qe.buffer);
         });
       }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), c = process.argv.slice(2), process.on("uncaughtException", function(H) {
-        if (!(H instanceof xu))
+        if (!(H instanceof wu))
           throw H;
       }), process.on("unhandledRejection", function(H) {
         throw H;
       }), d = (H, J) => {
-        if (hu())
+        if (fu())
           throw process.exitCode = H, J;
         T(J), process.exit(H);
       }, a.inspect = function() {
@@ -2639,23 +2639,23 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         for (var Qe = 0; Qe < ze.length; ++Qe)
           Ze.push(Tt[ze[Qe]]);
         Ve == "v" ? Ze.push(0) : Ze = Ze.concat([1, Tt[Ve]]), Ze[1] = Ze.length - 2;
-        var is = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(Ze, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), os = new WebAssembly.Module(is), Di = new WebAssembly.Instance(os, { e: { f: H } }), Br = Di.exports.f;
-        return Br;
+        var is = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(Ze, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), os = new WebAssembly.Module(is), Oi = new WebAssembly.Instance(os, { e: { f: H } }), Vr = Oi.exports.f;
+        return Vr;
       }
       var W = [], q;
       function K() {
         if (W.length)
           return W.pop();
         try {
-          Js.grow(1);
+          er.grow(1);
         } catch (H) {
           throw H instanceof RangeError ? "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH." : H;
         }
-        return Js.length - 1;
+        return er.length - 1;
       }
       function Y(H, J) {
         for (var de = H; de < H + J; de++) {
-          var Ie = gu(de);
+          var Ie = bu(de);
           Ie && q.set(Ie, de);
         }
       }
@@ -2664,10 +2664,10 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }, ee;
       a.wasmBinary && (ee = a.wasmBinary);
       var se = a.noExitRuntime || true;
-      typeof WebAssembly != "object" && Or("no native wasm support detected");
+      typeof WebAssembly != "object" && Pr("no native wasm support detected");
       var ne, oe = false, re;
       function le(H, J) {
-        H || Or(J);
+        H || Pr(J);
       }
       function me(H) {
         var J = a["_" + H];
@@ -2675,15 +2675,15 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       function ke(H, J, de, Ie, Qe) {
         var Ze = { string: function(an) {
-          var nr = 0;
+          var sr = 0;
           if (an != null && an !== 0) {
             var Fc = (an.length << 2) + 1;
-            nr = vu(Fc), tt(an, nr, Fc);
+            sr = xu(Fc), tt(an, sr, Fc);
           }
-          return nr;
+          return sr;
         }, array: function(an) {
-          var nr = vu(an.length);
-          return at(an, nr), nr;
+          var sr = xu(an.length);
+          return at(an, sr), sr;
         } };
         function Ve(an) {
           return J === "string" ? Je(an) : J === "boolean" ? Boolean(an) : an;
@@ -2691,14 +2691,14 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         var ze = me(H), Tt = [], is = 0;
         if (Ie)
           for (var os = 0; os < Ie.length; os++) {
-            var Di = Ze[de[os]];
-            Di ? (is === 0 && (is = Rc()), Tt[os] = Di(Ie[os])) : Tt[os] = Ie[os];
+            var Oi = Ze[de[os]];
+            Oi ? (is === 0 && (is = Rc()), Tt[os] = Oi(Ie[os])) : Tt[os] = Ie[os];
           }
-        var Br = ze.apply(null, Tt);
-        function Tf(an) {
+        var Vr = ze.apply(null, Tt);
+        function _f(an) {
           return is !== 0 && Dc(is), Ve(an);
         }
-        return Br = Tf(Br), Br;
+        return Vr = _f(Vr), Vr;
       }
       function Se(H, J, de, Ie) {
         de = de || [];
@@ -2794,63 +2794,63 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       function Ls(H) {
         Cn = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Nn = new Int16Array(H), a.HEAP32 = Yt = new Int32Array(H), a.HEAPU8 = en = new Uint8Array(H), a.HEAPU16 = Tn = new Uint16Array(H), a.HEAPU32 = Dn = new Uint32Array(H), a.HEAPF32 = tn = new Float32Array(H), a.HEAPF64 = Ms = new Float64Array(H);
       }
-      var ki = a.INITIAL_MEMORY || 16777216, Js, Bs = [], pu = [], Ii = [], nn = false, ic = false, oc = 0;
-      function hu() {
+      var Si = a.INITIAL_MEMORY || 16777216, er, Bs = [], hu = [], Ci = [], nn = false, ic = false, oc = 0;
+      function fu() {
         return se || oc > 0;
       }
       function uc() {
         if (a.preRun)
           for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
             dc(a.preRun.shift());
-        mu(Bs);
+        gu(Bs);
       }
       function lc() {
-        nn = true, mu(pu);
+        nn = true, gu(hu);
       }
-      function Fv() {
+      function Pv() {
         ic = true;
       }
       function cc() {
         if (a.postRun)
           for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; )
             pc(a.postRun.shift());
-        mu(Ii);
+        gu(Ci);
       }
       function dc(H) {
         Bs.unshift(H);
       }
       function rs(H) {
-        pu.unshift(H);
+        hu.unshift(H);
       }
       function pc(H) {
-        Ii.unshift(H);
+        Ci.unshift(H);
       }
-      var Fn = 0, Si = null, er = null;
-      function th(H) {
+      var Fn = 0, Ni = null, tr = null;
+      function sh(H) {
         Fn++, a.monitorRunDependencies && a.monitorRunDependencies(Fn);
       }
       function hc(H) {
-        if (Fn--, a.monitorRunDependencies && a.monitorRunDependencies(Fn), Fn == 0 && (Si !== null && (clearInterval(Si), Si = null), er)) {
-          var J = er;
-          er = null, J();
+        if (Fn--, a.monitorRunDependencies && a.monitorRunDependencies(Fn), Fn == 0 && (Ni !== null && (clearInterval(Ni), Ni = null), tr)) {
+          var J = tr;
+          tr = null, J();
         }
       }
       a.preloadedImages = {}, a.preloadedAudios = {};
-      function Or(H) {
+      function Pr(H) {
         a.onAbort && a.onAbort(H), H = "Aborted(" + H + ")", R(H), oe = true, re = 1, H += ". Build with -s ASSERTIONS=1 for more info.";
         var J = new WebAssembly.RuntimeError(H);
         throw o(J), J;
       }
-      var nh = "data:application/octet-stream;base64,";
+      var rh = "data:application/octet-stream;base64,";
       function fc(H) {
-        return H.startsWith(nh);
+        return H.startsWith(rh);
       }
-      function Pr(H) {
+      function zr(H) {
         return H.startsWith("file://");
       }
       var sn;
       sn = "tfjs-backend-wasm.wasm", fc(sn) || (sn = b(sn));
-      function fu(H) {
+      function mu(H) {
         try {
           if (H == sn && ee)
             return new Uint8Array(ee);
@@ -2858,18 +2858,18 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             return x(H);
           throw "both async and sync fetching of the wasm failed";
         } catch (J) {
-          Or(J);
+          Pr(J);
         }
       }
-      function sh() {
+      function ah() {
         if (!ee && (h || f)) {
-          if (typeof fetch == "function" && !Pr(sn))
+          if (typeof fetch == "function" && !zr(sn))
             return fetch(sn, { credentials: "same-origin" }).then(function(H) {
               if (!H.ok)
                 throw "failed to load wasm binary file at '" + sn + "'";
               return H.arrayBuffer();
             }).catch(function() {
-              return fu(sn);
+              return mu(sn);
             });
           if (v)
             return new Promise(function(H, J) {
@@ -2879,30 +2879,30 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             });
         }
         return Promise.resolve().then(function() {
-          return fu(sn);
+          return mu(sn);
         });
       }
-      function rh() {
-        var H = { env: Ti, wasi_snapshot_preview1: Ti };
+      function ih() {
+        var H = { env: _i, wasi_snapshot_preview1: _i };
         function J(Ve, ze) {
           var Tt = Ve.exports;
-          a.asm = Tt, ne = a.asm.memory, Ls(ne.buffer), Js = a.asm.__indirect_function_table, rs(a.asm.__wasm_call_ctors), hc("wasm-instantiate");
+          a.asm = Tt, ne = a.asm.memory, Ls(ne.buffer), er = a.asm.__indirect_function_table, rs(a.asm.__wasm_call_ctors), hc("wasm-instantiate");
         }
-        th("wasm-instantiate");
+        sh("wasm-instantiate");
         function de(Ve) {
           J(Ve.instance);
         }
         function Ie(Ve) {
-          return sh().then(function(ze) {
+          return ah().then(function(ze) {
             return WebAssembly.instantiate(ze, H);
           }).then(function(ze) {
             return ze;
           }).then(Ve, function(ze) {
-            R("failed to asynchronously prepare wasm: " + ze), Or(ze);
+            R("failed to asynchronously prepare wasm: " + ze), Pr(ze);
           });
         }
         function Qe() {
-          return !ee && typeof WebAssembly.instantiateStreaming == "function" && !fc(sn) && !Pr(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(Ve) {
+          return !ee && typeof WebAssembly.instantiateStreaming == "function" && !fc(sn) && !zr(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(Ve) {
             var ze = WebAssembly.instantiateStreaming(Ve, H);
             return ze.then(de, function(Tt) {
               return R("wasm streaming compile failed: " + Tt), R("falling back to ArrayBuffer instantiation"), Ie(de);
@@ -2918,8 +2918,8 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           }
         return Qe().catch(o), {};
       }
-      var Ov, Pv;
-      function mu(H) {
+      var zv, Mv;
+      function gu(H) {
         for (; H.length > 0; ) {
           var J = H.shift();
           if (typeof J == "function") {
@@ -2927,10 +2927,10 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             continue;
           }
           var de = J.func;
-          typeof de == "number" ? J.arg === void 0 ? gu(de)() : gu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg);
+          typeof de == "number" ? J.arg === void 0 ? bu(de)() : bu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg);
         }
       }
-      function tr(H) {
+      function nr(H) {
         return H;
       }
       function mc(H) {
@@ -2941,11 +2941,11 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         });
       }
       var as = [];
-      function gu(H) {
+      function bu(H) {
         var J = as[H];
-        return J || (H >= as.length && (as.length = H + 1), as[H] = J = Js.get(H)), J;
+        return J || (H >= as.length && (as.length = H + 1), as[H] = J = er.get(H)), J;
       }
-      function zv() {
+      function Lv() {
         var H = new Error();
         if (!H.stack) {
           try {
@@ -2958,11 +2958,11 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         }
         return H.stack.toString();
       }
-      function Ci(H, J) {
-        Js.set(H, J), as[H] = J;
+      function Ti(H, J) {
+        er.set(H, J), as[H] = J;
       }
-      function ah() {
-        Or("");
+      function oh() {
+        Pr("");
       }
       function gc(H, J, de) {
         en.copyWithin(H, J, J + de);
@@ -2991,12 +2991,12 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         }
         return false;
       }
-      var Ni = { mappings: {}, buffers: [null, [], []], printChar: function(H, J) {
-        var de = Ni.buffers[H];
+      var $i = { mappings: {}, buffers: [null, [], []], printChar: function(H, J) {
+        var de = $i.buffers[H];
         J === 0 || J === 10 ? ((H === 1 ? P : R)(Xe(de, 0)), de.length = 0) : de.push(J);
       }, varargs: void 0, get: function() {
-        Ni.varargs += 4;
-        var H = Yt[Ni.varargs - 4 >> 2];
+        $i.varargs += 4;
+        var H = Yt[$i.varargs - 4 >> 2];
         return H;
       }, getStr: function(H) {
         var J = Je(H);
@@ -3004,240 +3004,240 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }, get64: function(H, J) {
         return H;
       } };
-      function ih(H) {
+      function uh(H) {
         return 0;
       }
-      function Mv(H, J, de, Ie, Qe) {
+      function Bv(H, J, de, Ie, Qe) {
       }
-      function Lv(H, J, de, Ie) {
+      function Vv(H, J, de, Ie) {
         for (var Qe = 0, Ze = 0; Ze < de; Ze++) {
           var Ve = Yt[J >> 2], ze = Yt[J + 4 >> 2];
           J += 8;
           for (var Tt = 0; Tt < ze; Tt++)
-            Ni.printChar(H, en[Ve + Tt]);
+            $i.printChar(H, en[Ve + Tt]);
           Qe += ze;
         }
         return Yt[Ie >> 2] = Qe, 0;
       }
-      function oh(H) {
+      function lh(H) {
         te(H);
       }
-      var vc = false, Ti = { abort: ah, emscripten_memcpy_big: gc, emscripten_resize_heap: yc, fd_close: ih, fd_seek: Mv, fd_write: Lv, setTempRet0: oh }, qN = rh(), Bv = a.___wasm_call_ctors = function() {
-        return (Bv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
-      }, uh = a._init = function() {
-        return (uh = a._init = a.asm.init).apply(null, arguments);
-      }, lh = a._init_with_threads_count = function() {
-        return (lh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
+      var vc = false, _i = { abort: oh, emscripten_memcpy_big: gc, emscripten_resize_heap: yc, fd_close: uh, fd_seek: Bv, fd_write: Vv, setTempRet0: lh }, ZN = ih(), Wv = a.___wasm_call_ctors = function() {
+        return (Wv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
+      }, ch = a._init = function() {
+        return (ch = a._init = a.asm.init).apply(null, arguments);
+      }, dh = a._init_with_threads_count = function() {
+        return (dh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
       }, xc = a._get_threads_count = function() {
         return (xc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
       }, wc = a._register_tensor = function() {
         return (wc = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
-      }, ch = a._dispose_data = function() {
-        return (ch = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
-      }, $e = a._dispose = function() {
-        return ($e = a._dispose = a.asm.dispose).apply(null, arguments);
-      }, dh = a._Abs = function() {
-        return (dh = a._Abs = a.asm.Abs).apply(null, arguments);
+      }, ph = a._dispose_data = function() {
+        return (ph = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
+      }, _e = a._dispose = function() {
+        return (_e = a._dispose = a.asm.dispose).apply(null, arguments);
+      }, hh = a._Abs = function() {
+        return (hh = a._Abs = a.asm.Abs).apply(null, arguments);
       }, kc = a._Add = function() {
         return (kc = a._Add = a.asm.Add).apply(null, arguments);
-      }, zr = a._AddN = function() {
-        return (zr = a._AddN = a.asm.AddN).apply(null, arguments);
-      }, $i = a._All = function() {
-        return ($i = a._All = a.asm.All).apply(null, arguments);
-      }, ph = a._Any = function() {
-        return (ph = a._Any = a.asm.Any).apply(null, arguments);
-      }, Vv = a._ArgMax = function() {
-        return (Vv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
-      }, hh = a._AvgPool = function() {
-        return (hh = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
-      }, Wv = a._BatchMatMul = function() {
-        return (Wv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
-      }, Mr = a._Ceil = function() {
-        return (Mr = a._Ceil = a.asm.Ceil).apply(null, arguments);
-      }, fh = a._ClipByValue = function() {
-        return (fh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments);
-      }, mh = a._Conv2D = function() {
-        return (mh = a._Conv2D = a.asm.Conv2D).apply(null, arguments);
-      }, gh = a._Conv2DBackpropInput = function() {
-        return (gh = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments);
-      }, bh = a._Cos = function() {
-        return (bh = a._Cos = a.asm.Cos).apply(null, arguments);
-      }, yh = a._Cosh = function() {
-        return (yh = a._Cosh = a.asm.Cosh).apply(null, arguments);
-      }, vh = a._CropAndResize = function() {
-        return (vh = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
+      }, Mr = a._AddN = function() {
+        return (Mr = a._AddN = a.asm.AddN).apply(null, arguments);
+      }, Ai = a._All = function() {
+        return (Ai = a._All = a.asm.All).apply(null, arguments);
+      }, fh = a._Any = function() {
+        return (fh = a._Any = a.asm.Any).apply(null, arguments);
+      }, Uv = a._ArgMax = function() {
+        return (Uv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
+      }, mh = a._AvgPool = function() {
+        return (mh = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
+      }, Gv = a._BatchMatMul = function() {
+        return (Gv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
+      }, Lr = a._Ceil = function() {
+        return (Lr = a._Ceil = a.asm.Ceil).apply(null, arguments);
+      }, gh = a._ClipByValue = function() {
+        return (gh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments);
+      }, bh = a._Conv2D = function() {
+        return (bh = a._Conv2D = a.asm.Conv2D).apply(null, arguments);
+      }, yh = a._Conv2DBackpropInput = function() {
+        return (yh = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments);
+      }, vh = a._Cos = function() {
+        return (vh = a._Cos = a.asm.Cos).apply(null, arguments);
+      }, xh = a._Cosh = function() {
+        return (xh = a._Cosh = a.asm.Cosh).apply(null, arguments);
+      }, wh = a._CropAndResize = function() {
+        return (wh = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
       }, Ic = a._Cumprod = function() {
         return (Ic = a._Cumprod = a.asm.Cumprod).apply(null, arguments);
-      }, xh = a._Cumsum = function() {
-        return (xh = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
-      }, wh = a._DepthToSpace = function() {
-        return (wh = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments);
-      }, kh = a._DepthwiseConv2dNative = function() {
-        return (kh = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments);
-      }, Ih = a._Elu = function() {
-        return (Ih = a._Elu = a.asm.Elu).apply(null, arguments);
-      }, Sh = a._Equal = function() {
-        return (Sh = a._Equal = a.asm.Equal).apply(null, arguments);
+      }, kh = a._Cumsum = function() {
+        return (kh = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
+      }, Ih = a._DepthToSpace = function() {
+        return (Ih = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments);
+      }, Sh = a._DepthwiseConv2dNative = function() {
+        return (Sh = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments);
+      }, Ch = a._Elu = function() {
+        return (Ch = a._Elu = a.asm.Elu).apply(null, arguments);
+      }, Nh = a._Equal = function() {
+        return (Nh = a._Equal = a.asm.Equal).apply(null, arguments);
       }, Sc = a._Exp = function() {
         return (Sc = a._Exp = a.asm.Exp).apply(null, arguments);
-      }, Ch = a._FlipLeftRight = function() {
-        return (Ch = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
-      }, Nh = a._Floor = function() {
-        return (Nh = a._Floor = a.asm.Floor).apply(null, arguments);
-      }, Lr = a._FloorDiv = function() {
-        return (Lr = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
-      }, bu = a._FusedBatchNorm = function() {
-        return (bu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
-      }, Th = a._FusedConv2D = function() {
-        return (Th = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
-      }, $h = a._FusedDepthwiseConv2D = function() {
-        return ($h = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
-      }, _h = a._Gather = function() {
-        return (_h = a._Gather = a.asm.Gather).apply(null, arguments);
-      }, Le = a._GatherNd = function() {
-        return (Le = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
-      }, Ah = a._Greater = function() {
-        return (Ah = a._Greater = a.asm.Greater).apply(null, arguments);
-      }, Eh = a._GreaterEqual = function() {
-        return (Eh = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
-      }, Rh = a._LeakyRelu = function() {
-        return (Rh = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments);
-      }, Dh = a._Less = function() {
-        return (Dh = a._Less = a.asm.Less).apply(null, arguments);
-      }, Fh = a._LessEqual = function() {
-        return (Fh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
-      }, Oh = a._Log = function() {
-        return (Oh = a._Log = a.asm.Log).apply(null, arguments);
-      }, yu = a._LogicalAnd = function() {
-        return (yu = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
+      }, Th = a._FlipLeftRight = function() {
+        return (Th = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
+      }, $h = a._Floor = function() {
+        return ($h = a._Floor = a.asm.Floor).apply(null, arguments);
+      }, Br = a._FloorDiv = function() {
+        return (Br = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
+      }, yu = a._FusedBatchNorm = function() {
+        return (yu = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
+      }, _h = a._FusedConv2D = function() {
+        return (_h = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
+      }, Ah = a._FusedDepthwiseConv2D = function() {
+        return (Ah = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
+      }, Eh = a._Gather = function() {
+        return (Eh = a._Gather = a.asm.Gather).apply(null, arguments);
+      }, Be = a._GatherNd = function() {
+        return (Be = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
+      }, Rh = a._Greater = function() {
+        return (Rh = a._Greater = a.asm.Greater).apply(null, arguments);
+      }, Dh = a._GreaterEqual = function() {
+        return (Dh = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
+      }, Fh = a._LeakyRelu = function() {
+        return (Fh = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments);
+      }, Oh = a._Less = function() {
+        return (Oh = a._Less = a.asm.Less).apply(null, arguments);
+      }, Ph = a._LessEqual = function() {
+        return (Ph = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
+      }, zh = a._Log = function() {
+        return (zh = a._Log = a.asm.Log).apply(null, arguments);
+      }, vu = a._LogicalAnd = function() {
+        return (vu = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
       }, Cc = a._Max = function() {
         return (Cc = a._Max = a.asm.Max).apply(null, arguments);
       }, Nc = a._MaxPool = function() {
         return (Nc = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
-      }, Ph = a._Maximum = function() {
-        return (Ph = a._Maximum = a.asm.Maximum).apply(null, arguments);
-      }, zh = a._Mean = function() {
-        return (zh = a._Mean = a.asm.Mean).apply(null, arguments);
-      }, Mh = a._Min = function() {
-        return (Mh = a._Min = a.asm.Min).apply(null, arguments);
-      }, Lh = a._Minimum = function() {
-        return (Lh = a._Minimum = a.asm.Minimum).apply(null, arguments);
-      }, Bh = a._MirrorPad = function() {
-        return (Bh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
-      }, Vh = a._Multiply = function() {
-        return (Vh = a._Multiply = a.asm.Multiply).apply(null, arguments);
+      }, Mh = a._Maximum = function() {
+        return (Mh = a._Maximum = a.asm.Maximum).apply(null, arguments);
+      }, Lh = a._Mean = function() {
+        return (Lh = a._Mean = a.asm.Mean).apply(null, arguments);
+      }, Bh = a._Min = function() {
+        return (Bh = a._Min = a.asm.Min).apply(null, arguments);
+      }, Vh = a._Minimum = function() {
+        return (Vh = a._Minimum = a.asm.Minimum).apply(null, arguments);
+      }, Wh = a._MirrorPad = function() {
+        return (Wh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
+      }, Uh = a._Multiply = function() {
+        return (Uh = a._Multiply = a.asm.Multiply).apply(null, arguments);
       }, gt = a._Neg = function() {
         return (gt = a._Neg = a.asm.Neg).apply(null, arguments);
-      }, Wh = a._NonMaxSuppressionV3 = function() {
-        return (Wh = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
-      }, Uh = a._NonMaxSuppressionV4 = function() {
-        return (Uh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
-      }, Gh = a._NonMaxSuppressionV5 = function() {
-        return (Gh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
-      }, _i = a._NotEqual = function() {
-        return (_i = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
+      }, Gh = a._NonMaxSuppressionV3 = function() {
+        return (Gh = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
+      }, Hh = a._NonMaxSuppressionV4 = function() {
+        return (Hh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
+      }, qh = a._NonMaxSuppressionV5 = function() {
+        return (qh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
+      }, Ei = a._NotEqual = function() {
+        return (Ei = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
       }, Tc = a._OneHot = function() {
         return (Tc = a._OneHot = a.asm.OneHot).apply(null, arguments);
       }, $c = a._PadV2 = function() {
         return ($c = a._PadV2 = a.asm.PadV2).apply(null, arguments);
       }, _c = a._Pow = function() {
         return (_c = a._Pow = a.asm.Pow).apply(null, arguments);
-      }, Hh = a._Prelu = function() {
-        return (Hh = a._Prelu = a.asm.Prelu).apply(null, arguments);
+      }, jh = a._Prelu = function() {
+        return (jh = a._Prelu = a.asm.Prelu).apply(null, arguments);
       }, Ac = a._Prod = function() {
         return (Ac = a._Prod = a.asm.Prod).apply(null, arguments);
-      }, qh = a._RealDiv = function() {
-        return (qh = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
-      }, Uv = a._Relu = function() {
-        return (Uv = a._Relu = a.asm.Relu).apply(null, arguments);
+      }, Kh = a._RealDiv = function() {
+        return (Kh = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
+      }, Hv = a._Relu = function() {
+        return (Hv = a._Relu = a.asm.Relu).apply(null, arguments);
       }, Ec = a._Relu6 = function() {
         return (Ec = a._Relu6 = a.asm.Relu6).apply(null, arguments);
-      }, Gv = a._ResizeBilinear = function() {
-        return (Gv = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
-      }, jh = a._Reverse = function() {
-        return (jh = a._Reverse = a.asm.Reverse).apply(null, arguments);
-      }, Kh = a._RotateWithOffset = function() {
-        return (Kh = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
-      }, Xh = a._Round = function() {
-        return (Xh = a._Round = a.asm.Round).apply(null, arguments);
-      }, Yh = a._Rsqrt = function() {
-        return (Yh = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
-      }, Qh = a._ScatterNd = function() {
-        return (Qh = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
-      }, Zh = a._SelectV2 = function() {
-        return (Zh = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
-      }, Jh = a._Sigmoid = function() {
-        return (Jh = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
-      }, ef = a._Sin = function() {
-        return (ef = a._Sin = a.asm.Sin).apply(null, arguments);
-      }, tf = a._Softmax = function() {
-        return (tf = a._Softmax = a.asm.Softmax).apply(null, arguments);
-      }, nf = a._SparseFillEmptyRows = function() {
-        return (nf = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
-      }, sf = a._SparseReshape = function() {
-        return (sf = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
-      }, rf = a._SparseSegmentReduction = function() {
-        return (rf = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
-      }, af = a._Sqrt = function() {
-        return (af = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
-      }, of = a._Square = function() {
-        return (of = a._Square = a.asm.Square).apply(null, arguments);
-      }, uf = a._SquaredDifference = function() {
-        return (uf = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
-      }, lf = a._Step = function() {
-        return (lf = a._Step = a.asm.Step).apply(null, arguments);
-      }, cf = a._StridedSlice = function() {
-        return (cf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
-      }, df = a._Sub = function() {
-        return (df = a._Sub = a.asm.Sub).apply(null, arguments);
-      }, pf = a._Sum = function() {
-        return (pf = a._Sum = a.asm.Sum).apply(null, arguments);
-      }, hf = a._Tan = function() {
-        return (hf = a._Tan = a.asm.Tan).apply(null, arguments);
-      }, ff = a._Tanh = function() {
-        return (ff = a._Tanh = a.asm.Tanh).apply(null, arguments);
-      }, mf = a._Tile = function() {
-        return (mf = a._Tile = a.asm.Tile).apply(null, arguments);
-      }, gf = a._TopK = function() {
-        return (gf = a._TopK = a.asm.TopK).apply(null, arguments);
-      }, bf = a._Transform = function() {
-        return (bf = a._Transform = a.asm.Transform).apply(null, arguments);
-      }, yf = a._Transpose = function() {
-        return (yf = a._Transpose = a.asm.Transpose).apply(null, arguments);
-      }, vf = a.__FusedMatMul = function() {
-        return (vf = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments);
-      }, xf = a._malloc = function() {
-        return (xf = a._malloc = a.asm.malloc).apply(null, arguments);
-      }, wf = a._free = function() {
-        return (wf = a._free = a.asm.free).apply(null, arguments);
-      }, kf = a.___errno_location = function() {
-        return (kf = a.___errno_location = a.asm.__errno_location).apply(null, arguments);
-      }, If = a._emscripten_main_thread_process_queued_calls = function() {
-        return (If = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
+      }, qv = a._ResizeBilinear = function() {
+        return (qv = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
+      }, Xh = a._Reverse = function() {
+        return (Xh = a._Reverse = a.asm.Reverse).apply(null, arguments);
+      }, Yh = a._RotateWithOffset = function() {
+        return (Yh = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
+      }, Qh = a._Round = function() {
+        return (Qh = a._Round = a.asm.Round).apply(null, arguments);
+      }, Zh = a._Rsqrt = function() {
+        return (Zh = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
+      }, Jh = a._ScatterNd = function() {
+        return (Jh = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
+      }, ef = a._SelectV2 = function() {
+        return (ef = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
+      }, tf = a._Sigmoid = function() {
+        return (tf = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
+      }, nf = a._Sin = function() {
+        return (nf = a._Sin = a.asm.Sin).apply(null, arguments);
+      }, sf = a._Softmax = function() {
+        return (sf = a._Softmax = a.asm.Softmax).apply(null, arguments);
+      }, rf = a._SparseFillEmptyRows = function() {
+        return (rf = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
+      }, af = a._SparseReshape = function() {
+        return (af = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
+      }, of = a._SparseSegmentReduction = function() {
+        return (of = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
+      }, uf = a._Sqrt = function() {
+        return (uf = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
+      }, lf = a._Square = function() {
+        return (lf = a._Square = a.asm.Square).apply(null, arguments);
+      }, cf = a._SquaredDifference = function() {
+        return (cf = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
+      }, df = a._Step = function() {
+        return (df = a._Step = a.asm.Step).apply(null, arguments);
+      }, pf = a._StridedSlice = function() {
+        return (pf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
+      }, hf = a._Sub = function() {
+        return (hf = a._Sub = a.asm.Sub).apply(null, arguments);
+      }, ff = a._Sum = function() {
+        return (ff = a._Sum = a.asm.Sum).apply(null, arguments);
+      }, mf = a._Tan = function() {
+        return (mf = a._Tan = a.asm.Tan).apply(null, arguments);
+      }, gf = a._Tanh = function() {
+        return (gf = a._Tanh = a.asm.Tanh).apply(null, arguments);
+      }, bf = a._Tile = function() {
+        return (bf = a._Tile = a.asm.Tile).apply(null, arguments);
+      }, yf = a._TopK = function() {
+        return (yf = a._TopK = a.asm.TopK).apply(null, arguments);
+      }, vf = a._Transform = function() {
+        return (vf = a._Transform = a.asm.Transform).apply(null, arguments);
+      }, xf = a._Transpose = function() {
+        return (xf = a._Transpose = a.asm.Transpose).apply(null, arguments);
+      }, wf = a.__FusedMatMul = function() {
+        return (wf = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments);
+      }, kf = a._malloc = function() {
+        return (kf = a._malloc = a.asm.malloc).apply(null, arguments);
+      }, If = a._free = function() {
+        return (If = a._free = a.asm.free).apply(null, arguments);
+      }, Sf = a.___errno_location = function() {
+        return (Sf = a.___errno_location = a.asm.__errno_location).apply(null, arguments);
+      }, Cf = a._emscripten_main_thread_process_queued_calls = function() {
+        return (Cf = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
       }, Rc = a.stackSave = function() {
         return (Rc = a.stackSave = a.asm.stackSave).apply(null, arguments);
       }, Dc = a.stackRestore = function() {
         return (Dc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
-      }, vu = a.stackAlloc = function() {
-        return (vu = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
-      }, Sf = a.dynCall_iijjiiii = function() {
-        return (Sf = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
-      }, Cf = a.dynCall_jiji = function() {
-        return (Cf = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments);
+      }, xu = a.stackAlloc = function() {
+        return (xu = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
+      }, Nf = a.dynCall_iijjiiii = function() {
+        return (Nf = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
+      }, Tf = a.dynCall_jiji = function() {
+        return (Tf = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments);
       };
       a.cwrap = Se;
-      var Ai;
-      function xu(H) {
+      var Ri;
+      function wu(H) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + H + ")", this.status = H;
       }
-      er = function H() {
-        Ai || wu(), Ai || (er = H);
+      tr = function H() {
+        Ri || ku(), Ri || (tr = H);
       };
-      function wu(H) {
+      function ku(H) {
         if (H = H || c, Fn > 0 || (uc(), Fn > 0))
           return;
         function J() {
-          Ai || (Ai = true, a.calledRun = true, !oe && (lc(), i(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), cc()));
+          Ri || (Ri = true, a.calledRun = true, !oe && (lc(), i(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), cc()));
         }
         a.setStatus ? (a.setStatus("Running..."), setTimeout(function() {
           setTimeout(function() {
@@ -3245,33 +3245,33 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           }, 1), J();
         }, 1)) : J();
       }
-      a.run = wu;
-      function Hv(H) {
-        re = H, hu() || (a.onExit && a.onExit(H), oe = true), d(H, new xu(H));
+      a.run = ku;
+      function jv(H) {
+        re = H, fu() || (a.onExit && a.onExit(H), oe = true), d(H, new wu(H));
       }
       if (a.preInit)
         for (typeof a.preInit == "function" && (a.preInit = [a.preInit]); a.preInit.length > 0; )
           a.preInit.pop()();
-      wu();
-      var Ei;
-      u && (Ei = { uncaughtException: process.listeners("uncaughtException").filter(function(H) {
+      ku();
+      var Di;
+      u && (Di = { uncaughtException: process.listeners("uncaughtException").filter(function(H) {
         return !u.uncaughtException.indexOf(H) > -1;
       }), unhandledRejection: process.listeners("unhandledRejection").filter(function(H) {
         return !u.unhandledRejection.indexOf(H) > -1;
       }) });
-      var Ri;
+      var Fi;
       if (typeof r != "undefined")
-        Ri = r;
+        Fi = r;
       else if (typeof WasmBackendModuleThreadedSimd != "undefined")
-        Ri = WasmBackendModuleThreadedSimd;
+        Fi = WasmBackendModuleThreadedSimd;
       else
         throw new Error("Could not find wasm module in post.js");
-      if (Ei) {
-        var Nf = Ri._dispose;
-        Ri._dispose = function() {
-          Nf(), Ei.uncaughtException.forEach(function(H) {
+      if (Di) {
+        var $f = Fi._dispose;
+        Fi._dispose = function() {
+          $f(), Di.uncaughtException.forEach(function(H) {
             process.removeListener("uncaughtException", H);
-          }), Ei.unhandledRejection.forEach(function(H) {
+          }), Di.unhandledRejection.forEach(function(H) {
             process.removeListener("unhandledRejection", H);
           });
         };
@@ -3283,9 +3283,9 @@ var i$ = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
     return n;
   }) : typeof e == "object" && (e.WasmBackendModule = n);
 } });
-var o$ = 1e-7;
-var u$ = 1e-4;
-var Ud = class {
+var h$ = 1e-7;
+var f$ = 1e-4;
+var Hd = class {
   constructor(e, t) {
     this.backend = e, this.dataMover = t, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0;
   }
@@ -3305,7 +3305,7 @@ var Ud = class {
     return this.dataIdsCount;
   }
 };
-var nl = class {
+var sl = class {
   refCount(e) {
     return On("refCount");
   }
@@ -3346,7 +3346,7 @@ var nl = class {
     return On("floatPrecision");
   }
   epsilon() {
-    return this.floatPrecision() === 32 ? o$ : u$;
+    return this.floatPrecision() === 32 ? h$ : f$;
   }
   dispose() {
     return On("dispose");
@@ -3355,39 +3355,39 @@ var nl = class {
 function On(e) {
   throw new Error(`'${e}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
 }
-function Uw(e) {
+function jw(e) {
   let t = e.length, n = 0;
   for (; t > 0; )
     n = Math.random() * t | 0, t--, pd(e, t, n);
 }
-function l$(e, t) {
+function m$(e, t) {
   if (e.length !== t.length)
     throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);
   let n = e.length, s = 0;
   for (; n > 0; )
     s = Math.random() * n | 0, n--, pd(e, n, s), pd(t, n, s);
 }
-function Vu(e, t, n) {
+function Wu(e, t, n) {
   return Math.max(e, Math.min(t, n));
 }
-function c$(e) {
+function g$(e) {
   return e % 2 === 0 ? e : e + 1;
 }
 function pd(e, t, n) {
   let s = e[t];
   e[t] = e[n], e[n] = s;
 }
-function d$(e) {
+function b$(e) {
   let t = 0;
   for (let n = 0; n < e.length; n++)
     t += e[n];
   return t;
 }
-function p$(e, t) {
+function y$(e, t) {
   let n = Math.random();
   return t * n + (1 - n) * e;
 }
-function h$(e, t) {
+function v$(e, t) {
   let n = 0;
   for (let s = 0; s < e.length; s++) {
     let r = Number(e[s]) - Number(t[s]);
@@ -3400,15 +3400,15 @@ function O(e, t) {
     throw new Error(typeof t == "string" ? t : t());
 }
 function pn(e, t, n = "") {
-  O(kr(e, t), () => n + ` Shapes ${e} and ${t} must match`);
+  O(Ir(e, t), () => n + ` Shapes ${e} and ${t} must match`);
 }
-function wa(e) {
+function ka(e) {
   O(e != null, () => "The input to the tensor constructor must be a non-null value.");
 }
-function sa(e, t = [], n = false) {
+function ra(e, t = [], n = false) {
   if (t == null && (t = []), Array.isArray(e) || Qt(e) && !n)
     for (let s = 0; s < e.length; ++s)
-      sa(e[s], t, n);
+      ra(e[s], t, n);
   else
     t.push(e);
   return t;
@@ -3421,10 +3421,10 @@ function pt(e) {
     t *= e[n];
   return t;
 }
-function f$(e) {
+function x$(e) {
   return e.length === 0;
 }
-function kr(e, t) {
+function Ir(e, t) {
   if (e === t)
     return true;
   if (e == null || t == null || e.length !== t.length)
@@ -3434,10 +3434,10 @@ function kr(e, t) {
       return false;
   return true;
 }
-function Yi(e) {
+function Zi(e) {
   return e % 1 === 0;
 }
-function m$(e) {
+function w$(e) {
   if (Math.tanh != null)
     return Math.tanh(e);
   if (e === 1 / 0)
@@ -3449,20 +3449,20 @@ function m$(e) {
     return (t - 1) / (t + 1);
   }
 }
-function g$(e) {
+function k$(e) {
   let t = Math.ceil(Math.sqrt(e));
   return [t, Math.ceil(e / t)];
 }
-function b$(e) {
+function I$(e) {
   let t = new Uint32Array(e);
   for (let n = 0; n < e; ++n)
     t[n] = n;
-  return Uw(t), t;
+  return jw(t), t;
 }
-function zu(e, t) {
+function Mu(e, t) {
   return t <= e.length ? e : e + " ".repeat(t - e.length);
 }
-function y$(e, t = (s) => 0, n) {
+function S$(e, t = (s) => 0, n) {
   return new Promise((s, r) => {
     let a = 0, i = () => {
       if (e()) {
@@ -3480,7 +3480,7 @@ function y$(e, t = (s) => 0, n) {
     i();
   });
 }
-function v$(e, t) {
+function C$(e, t) {
   let n = 1, s = -1;
   for (let a = 0; a < e.length; ++a)
     if (e[a] >= 0)
@@ -3505,9 +3505,9 @@ function v$(e, t) {
 }
 function ts(e, t) {
   let n = t.length;
-  return e = e == null ? t.map((s, r) => r) : [].concat(e), O(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), O(e.every((s) => Yi(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s);
+  return e = e == null ? t.map((s, r) => r) : [].concat(e), O(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), O(e.every((s) => Zi(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s);
 }
-function Gw(e, t) {
+function Kw(e, t) {
   let n = [], s = [], r = t != null && Array.isArray(t) && t.length === 0, a = t == null || r ? null : ts(t, e).sort(), i = 0;
   for (let o = 0; o < e.length; ++o) {
     if (a != null) {
@@ -3519,7 +3519,7 @@ function Gw(e, t) {
   }
   return { newShape: n, keptDims: s };
 }
-function Hw(e, t) {
+function Xw(e, t) {
   let n = null;
   if (e == null || e === "float32")
     n = new Float32Array(t);
@@ -3531,7 +3531,7 @@ function Hw(e, t) {
     throw new Error(`Unknown data type ${e}`);
   return n;
 }
-function qw(e, t) {
+function Yw(e, t) {
   let n = null;
   if (e == null || e === "float32")
     n = new Float32Array(t);
@@ -3545,23 +3545,23 @@ function qw(e, t) {
     throw new Error(`Unknown data type ${e}`);
   return n;
 }
-function jw(e, t) {
+function Qw(e, t) {
   for (let n = 0; n < e.length; n++) {
     let s = e[n];
     if (isNaN(s) || !isFinite(s))
       throw Error(`A tensor of type ${t} being uploaded contains ${s}.`);
   }
 }
-function Kw(e) {
+function Zw(e) {
   return e === "bool" || e === "complex64" || e === "float32" || e === "int32" || e === "string";
 }
-function x$(e, t) {
+function N$(e, t) {
   return !(t === "complex64" || t === "float32" && e !== "complex64" || t === "int32" && e !== "float32" && e !== "complex64" || t === "bool" && e === "bool");
 }
 function Qt(e) {
   return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray;
 }
-function Xf(e) {
+function Yf(e) {
   if (e === "float32" || e === "int32")
     return 4;
   if (e === "complex64")
@@ -3570,25 +3570,25 @@ function Xf(e) {
     return 1;
   throw new Error(`Unknown dtype ${e}`);
 }
-function Xw(e) {
+function Jw(e) {
   if (e == null)
     return 0;
   let t = 0;
   return e.forEach((n) => t += n.length), t;
 }
-function ir(e) {
+function or(e) {
   return typeof e == "string" || e instanceof String;
 }
-function Yw(e) {
+function ek(e) {
   return typeof e == "boolean";
 }
-function Qw(e) {
+function tk(e) {
   return typeof e == "number";
 }
-function Gd(e) {
-  return Array.isArray(e) ? Gd(e[0]) : e instanceof Float32Array ? "float32" : e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray ? "int32" : Qw(e) ? "float32" : ir(e) ? "string" : Yw(e) ? "bool" : "float32";
+function qd(e) {
+  return Array.isArray(e) ? qd(e[0]) : e instanceof Float32Array ? "float32" : e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray ? "int32" : tk(e) ? "float32" : or(e) ? "string" : ek(e) ? "bool" : "float32";
 }
-function hr(e) {
+function fr(e) {
   return !!(e && e.constructor && e.call && e.apply);
 }
 function hd(e, t) {
@@ -3597,7 +3597,7 @@ function hd(e, t) {
       return n;
   return e;
 }
-function ao(e) {
+function oo(e) {
   let t = e.length;
   if (t < 2)
     return [];
@@ -3607,7 +3607,7 @@ function ao(e) {
     n[s] = n[s + 1] * e[s + 1];
   return n;
 }
-function Zw(e, t, n, s = false) {
+function nk(e, t, n, s = false) {
   let r = new Array();
   if (t.length === 1) {
     let a = t[0] * (s ? 2 : 1);
@@ -3616,11 +3616,11 @@ function Zw(e, t, n, s = false) {
   } else {
     let a = t[0], i = t.slice(1), o = i.reduce((u, l) => u * l) * (s ? 2 : 1);
     for (let u = 0; u < a; u++)
-      r[u] = Zw(e + u * o, i, n, s);
+      r[u] = nk(e + u * o, i, n, s);
   }
   return r;
 }
-function Hi(e, t, n = false) {
+function ji(e, t, n = false) {
   if (e.length === 0)
     return t[0];
   let s = e.reduce((r, a) => r * a) * (n ? 2 : 1);
@@ -3628,15 +3628,15 @@ function Hi(e, t, n = false) {
     return [];
   if (s !== t.length)
     throw new Error(`[${e}] does not match the input size ${t.length}${n ? " for a complex tensor" : ""}.`);
-  return Zw(0, e, t, n);
+  return nk(0, e, t, n);
 }
-function ng(e, t) {
-  let n = Hd(e, t);
+function rg(e, t) {
+  let n = jd(e, t);
   for (let s = 0; s < n.length; s++)
     n[s] = 1;
   return n;
 }
-function Hd(e, t) {
+function jd(e, t) {
   if (t == null || t === "float32" || t === "complex64")
     return new Float32Array(e);
   if (t === "int32")
@@ -3645,22 +3645,22 @@ function Hd(e, t) {
     return new Uint8Array(e);
   throw new Error(`Unknown data type ${t}`);
 }
-function w$(e, t) {
+function T$(e, t) {
   let n = e.reduce((s, r) => s * r, 1);
   if (t == null || t === "float32")
-    return Hi(e, new Float32Array(n));
+    return ji(e, new Float32Array(n));
   if (t === "int32")
-    return Hi(e, new Int32Array(n));
+    return ji(e, new Int32Array(n));
   if (t === "bool")
-    return Hi(e, new Uint8Array(n));
+    return ji(e, new Uint8Array(n));
   throw new Error(`Unknown data type ${t}`);
 }
-function sg(e) {
+function ag(e) {
   e.forEach((t) => {
     O(Number.isInteger(t) && t >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${e}].`);
   });
 }
-function k$(e, t, n) {
+function $$(e, t, n) {
   if (t === 0)
     return 0;
   if (t === 1)
@@ -3670,7 +3670,7 @@ function k$(e, t, n) {
     s += n[r] * e[r];
   return s;
 }
-function I$(e, t, n) {
+function _$(e, t, n) {
   if (t === 0)
     return [];
   if (t === 1)
@@ -3680,13 +3680,13 @@ function I$(e, t, n) {
     s[r] = Math.floor(e / n[r]), e -= s[r] * n[r];
   return s[s.length - 1] = e, s;
 }
-function rg(e) {
+function ig(e) {
   return e && e.then && typeof e.then == "function";
 }
-var tx = "tfjsflags";
-var S$ = class {
+var sx = "tfjsflags";
+var A$ = class {
   constructor(e) {
-    this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = C$, this.populateURLFlags();
+    this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = E$, this.populateURLFlags();
   }
   setPlatform(e, t) {
     this.platform != null && (X().getBool("IS_TEST") || X().getBool("PROD") || console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${e}.`)), this.platformName = e, this.platform = t;
@@ -3704,7 +3704,7 @@ var S$ = class {
     if (e in this.flags)
       return this.flags[e];
     let t = this.evaluateFlag(e);
-    if (rg(t))
+    if (ig(t))
       throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);
     return this.flags[e] = t, this.flags[e];
   }
@@ -3740,20 +3740,20 @@ var S$ = class {
     if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined")
       return;
     let e = this.getQueryParams(this.global.location.search);
-    tx in e && e[tx].split(",").forEach((n) => {
+    sx in e && e[sx].split(",").forEach((n) => {
       let [s, r] = n.split(":");
-      this.urlFlags[s] = T$(s, r);
+      this.urlFlags[s] = D$(s, r);
     });
   }
 };
-function C$(e) {
+function E$(e) {
   let t = {};
-  return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (n, ...s) => (N$(t, s[0], s[1]), s.join("="))), t;
+  return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (n, ...s) => (R$(t, s[0], s[1]), s.join("="))), t;
 }
-function N$(e, t, n) {
+function R$(e, t, n) {
   e[decodeURIComponent(t)] = decodeURIComponent(n || "");
 }
-function T$(e, t) {
+function D$(e, t) {
   if (t = t.toLowerCase(), t === "true" || t === "false")
     return t === "true";
   if (`${+t}` === t)
@@ -3761,15 +3761,15 @@ function T$(e, t) {
   throw new Error(`Could not parse value flag value ${t} for flag ${e}.`);
 }
 function X() {
-  return Jw;
+  return sk;
 }
-var Jw = null;
-function $$(e) {
-  Jw = e;
+var sk = null;
+function F$(e) {
+  sk = e;
 }
-var Df;
-function ek() {
-  if (Df == null) {
+var Of;
+function rk() {
+  if (Of == null) {
     let e;
     if (typeof window != "undefined")
       e = window;
@@ -3781,16 +3781,16 @@ function ek() {
       e = self;
     else
       throw new Error("Could not find a global object");
-    Df = e;
+    Of = e;
   }
-  return Df;
+  return Of;
 }
-function _$() {
-  let e = ek();
+function O$() {
+  let e = rk();
   return e._tfGlobals == null && (e._tfGlobals = /* @__PURE__ */ new Map()), e._tfGlobals;
 }
-function ag(e, t) {
-  let n = _$();
+function og(e, t) {
+  let n = O$();
   if (n.has(e))
     return n.get(e);
   {
@@ -3798,191 +3798,191 @@ function ag(e, t) {
     return n.set(e, s), n.get(e);
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 }
-var io = "Abs";
-var sl = "Acos";
-var rl = "Acosh";
-var Ir = "Add";
-var ka = "AddN";
-var al = "All";
-var il = "Any";
-var Ia = "ArgMax";
-var ol = "ArgMin";
-var ul = "Asin";
-var ll = "Asinh";
-var cl = "Atan";
-var dl = "Atanh";
-var pl = "Atan2";
-var Sa = "AvgPool";
-var ig = "AvgPoolGrad";
-var qd = "AvgPool3D";
-var og = "AvgPool3DGrad";
-var Ca = "BatchMatMul";
-var oo = "BatchToSpaceND";
-var ug = "Bincount";
-var A$ = "BroadcastTo";
-var lg = "BroadcastArgs";
-var Na = "Cast";
-var Ta = "Ceil";
-var Sr = "ClipByValue";
-var jd = "Complex";
-var Kd = "ComplexAbs";
-var uo = "Concat";
-var $a = "Conv2D";
-var cg = "Conv2DBackpropFilter";
-var _a = "Conv2DBackpropInput";
-var Xd = "Conv3D";
-var dg = "Conv3DBackpropFilterV2";
-var pg = "Conv3DBackpropInputV2";
-var Aa = "Cos";
-var Ea = "Cosh";
-var hl = "Cumprod";
-var lo = "Cumsum";
-var co = "CropAndResize";
-var hg = "DenseBincount";
-var po = "DepthToSpace";
-var Ra = "DepthwiseConv2dNative";
-var fg = "DepthwiseConv2dNativeBackpropFilter";
-var mg = "DepthwiseConv2dNativeBackpropInput";
-var gg = "Diag";
-var Yd = "Dilation2D";
-var Yf = "Dilation2DBackpropInput";
-var Qf = "Dilation2DBackpropFilter";
-var Da = "RealDiv";
-var Qd = "Einsum";
-var Fa = "Elu";
-var bg = "EluGrad";
+var uo = "Abs";
+var rl = "Acos";
+var al = "Acosh";
+var Sr = "Add";
+var Ia = "AddN";
+var il = "All";
+var ol = "Any";
+var Sa = "ArgMax";
+var ul = "ArgMin";
+var ll = "Asin";
+var cl = "Asinh";
+var dl = "Atan";
+var pl = "Atanh";
+var hl = "Atan2";
+var Ca = "AvgPool";
+var ug = "AvgPoolGrad";
+var Kd = "AvgPool3D";
+var lg = "AvgPool3DGrad";
+var Na = "BatchMatMul";
+var lo = "BatchToSpaceND";
+var cg = "Bincount";
+var P$ = "BroadcastTo";
+var dg = "BroadcastArgs";
+var Ta = "Cast";
+var $a = "Ceil";
+var Cr = "ClipByValue";
+var Xd = "Complex";
+var Yd = "ComplexAbs";
+var co = "Concat";
+var _a = "Conv2D";
+var pg = "Conv2DBackpropFilter";
+var Aa = "Conv2DBackpropInput";
+var Qd = "Conv3D";
+var hg = "Conv3DBackpropFilterV2";
+var fg = "Conv3DBackpropInputV2";
+var Ea = "Cos";
+var Ra = "Cosh";
+var po = "Cumprod";
+var Da = "Cumsum";
+var ho = "CropAndResize";
+var mg = "DenseBincount";
+var fo = "DepthToSpace";
+var Fa = "DepthwiseConv2dNative";
+var gg = "DepthwiseConv2dNativeBackpropFilter";
+var bg = "DepthwiseConv2dNativeBackpropInput";
+var yg = "Diag";
+var Zd = "Dilation2D";
+var Qf = "Dilation2DBackpropInput";
+var Zf = "Dilation2DBackpropFilter";
+var Oa = "RealDiv";
+var Jd = "Einsum";
+var Pa = "Elu";
+var vg = "EluGrad";
 var fl = "Erf";
-var ho = "Equal";
-var Oa = "Exp";
-var fo = "ExpandDims";
-var mo = "Expm1";
-var yg = "FFT";
+var mo = "Equal";
+var za = "Exp";
+var go = "ExpandDims";
+var bo = "Expm1";
+var xg = "FFT";
 var ml = "Fill";
-var go = "FlipLeftRight";
-var Pa = "Floor";
-var za = "FloorDiv";
-var Ma = "FusedBatchNorm";
-var bo = "GatherV2";
-var yo = "GatherNd";
-var vo = "Greater";
-var La = "GreaterEqual";
-var Ba = "Identity";
-var vg = "IFFT";
-var Zd = "Imag";
+var yo = "FlipLeftRight";
+var Ma = "Floor";
+var La = "FloorDiv";
+var Ba = "FusedBatchNorm";
+var vo = "GatherV2";
+var xo = "GatherNd";
+var wo = "Greater";
+var Va = "GreaterEqual";
+var Wa = "Identity";
+var wg = "IFFT";
+var ep = "Imag";
 var gl = "IsFinite";
 var bl = "IsInf";
 var yl = "IsNan";
-var Va = "LeakyRelu";
-var xo = "Less";
-var wo = "LessEqual";
-var xg = "LinSpace";
-var Wa = "Log";
+var Ua = "LeakyRelu";
+var ko = "Less";
+var Io = "LessEqual";
+var kg = "LinSpace";
+var Ga = "Log";
 var vl = "Log1p";
-var ko = "LogicalAnd";
+var So = "LogicalAnd";
 var xl = "LogicalNot";
-var Jd = "LogicalOr";
-var E$ = "LogSoftmax";
-var ep = "LRN";
-var wg = "LRNGrad";
-var Ua = "Max";
-var Ga = "Maximum";
-var Ha = "MaxPool";
-var kg = "MaxPoolGrad";
-var tp = "MaxPool3D";
-var Ig = "MaxPool3DGrad";
-var Sg = "MaxPoolWithArgmax";
-var qa = "Mean";
-var ja = "Min";
-var Ka = "Minimum";
-var Xa = "MirrorPad";
+var tp = "LogicalOr";
+var z$ = "LogSoftmax";
+var np = "LRN";
+var Ig = "LRNGrad";
+var Ha = "Max";
+var qa = "Maximum";
+var ja = "MaxPool";
+var Sg = "MaxPoolGrad";
+var sp = "MaxPool3D";
+var Cg = "MaxPool3DGrad";
+var Ng = "MaxPoolWithArgmax";
+var Ka = "Mean";
+var Xa = "Min";
+var Ya = "Minimum";
+var Qa = "MirrorPad";
 var wl = "Mod";
-var Cg = "Multinomial";
-var Ya = "Multiply";
-var Io = "Neg";
-var So = "NotEqual";
-var Co = "NonMaxSuppressionV3";
+var Tg = "Multinomial";
+var Za = "Multiply";
+var Co = "Neg";
+var No = "NotEqual";
+var To = "NonMaxSuppressionV3";
 var kl = "NonMaxSuppressionV4";
-var No = "NonMaxSuppressionV5";
-var To = "OnesLike";
-var $o = "OneHot";
-var _o = "Pack";
-var Qa = "PadV2";
-var $de = "Pool";
-var Za = "Pow";
-var Ja = "Prelu";
-var Ao = "Prod";
+var $o = "NonMaxSuppressionV5";
+var _o = "OnesLike";
+var Ao = "OneHot";
+var Eo = "Pack";
+var Ja = "PadV2";
+var Wde = "Pool";
+var ei = "Pow";
+var ti = "Prelu";
+var ni = "Prod";
 var Il = "Range";
-var np = "Real";
+var rp = "Real";
 var Sl = "Reciprocal";
-var ei = "Relu";
-var Eo = "Reshape";
+var si = "Relu";
+var Ro = "Reshape";
 var Cl = "ResizeNearestNeighbor";
-var Ng = "ResizeNearestNeighborGrad";
-var ti = "ResizeBilinear";
-var Tg = "ResizeBilinearGrad";
-var ni = "Relu6";
-var Ro = "Reverse";
-var Do = "Round";
-var si = "Rsqrt";
-var Fo = "ScatterNd";
-var Oo = "Select";
+var $g = "ResizeNearestNeighborGrad";
+var ri = "ResizeBilinear";
+var _g = "ResizeBilinearGrad";
+var ai = "Relu6";
+var Do = "Reverse";
+var Fo = "Round";
+var ii = "Rsqrt";
+var Oo = "ScatterNd";
+var Po = "Select";
 var Nl = "Selu";
-var Po = "Slice";
-var ri = "Sin";
-var zo = "Sinh";
+var zo = "Slice";
+var oi = "Sin";
+var Mo = "Sinh";
 var Tl = "Sign";
-var ai = "Sigmoid";
+var ui = "Sigmoid";
 var $l = "Softplus";
-var ii = "Sqrt";
-var oi = "Sum";
-var Mo = "SpaceToBatchND";
-var Lo = "SplitV";
-var ui = "Softmax";
-var sp = "SparseFillEmptyRows";
+var li = "Sqrt";
+var ci = "Sum";
+var Lo = "SpaceToBatchND";
+var Bo = "SplitV";
+var di = "Softmax";
+var ap = "SparseFillEmptyRows";
 var _l = "SparseReshape";
-var rp = "SparseSegmentMean";
-var ap = "SparseSegmentSum";
-var ip = "SparseToDense";
-var li = "SquaredDifference";
+var ip = "SparseSegmentMean";
+var op = "SparseSegmentSum";
+var up = "SparseToDense";
+var pi = "SquaredDifference";
 var Al = "Square";
-var Bo = "StridedSlice";
-var op = "StringNGrams";
-var $g = "StringSplit";
-var _g = "StringToHashBucketFast";
-var ci = "Sub";
-var Vo = "Tan";
-var di = "Tanh";
-var Cr = "Tile";
-var Wo = "TopK";
-var Uo = "Transform";
-var pi = "Transpose";
-var Ag = "Unique";
-var Go = "Unpack";
-var up = "UnsortedSegmentSum";
-var Ho = "ZerosLike";
-var hi = "Step";
+var Vo = "StridedSlice";
+var lp = "StringNGrams";
+var Ag = "StringSplit";
+var Eg = "StringToHashBucketFast";
+var hi = "Sub";
+var Wo = "Tan";
+var fi = "Tanh";
+var Nr = "Tile";
+var Uo = "TopK";
+var Go = "Transform";
+var mi = "Transpose";
+var Rg = "Unique";
+var Ho = "Unpack";
+var cp = "UnsortedSegmentSum";
+var qo = "ZerosLike";
+var gi = "Step";
 var fd = "FromPixels";
-var qo = "RotateWithOffset";
-var ra = "_FusedMatMul";
-var aa = "FusedConv2D";
-var ia = "FusedDepthwiseConv2D";
-function ar(...e) {
+var jo = "RotateWithOffset";
+var aa = "_FusedMatMul";
+var ia = "FusedConv2D";
+var oa = "FusedDepthwiseConv2D";
+function ir(...e) {
   X().getBool("IS_TEST") || X().getBool("PROD") || console.warn(...e);
 }
-function R$(...e) {
+function M$(...e) {
   X().getBool("IS_TEST") || X().getBool("PROD") || console.log(...e);
 }
-var Qi = ag("kernelRegistry", () => /* @__PURE__ */ new Map());
-var Wu = ag("gradRegistry", () => /* @__PURE__ */ new Map());
-function Zf(e, t) {
-  let n = Eg(e, t);
-  return Qi.get(n);
+var Ji = og("kernelRegistry", () => /* @__PURE__ */ new Map());
+var Uu = og("gradRegistry", () => /* @__PURE__ */ new Map());
+function Jf(e, t) {
+  let n = Dg(e, t);
+  return Ji.get(n);
 }
-function nx(e) {
-  return Wu.get(e);
+function rx(e) {
+  return Uu.get(e);
 }
-function Jf(e) {
-  let t = Qi.entries(), n = [];
+function em(e) {
+  let t = Ji.entries(), n = [];
   for (; ; ) {
     let { done: s, value: r } = t.next();
     if (s)
@@ -3993,121 +3993,121 @@ function Jf(e) {
   return n;
 }
 function El(e) {
-  let { kernelName: t, backendName: n } = e, s = Eg(t, n);
-  Qi.has(s) && ar(`The kernel '${t}' for backend '${n}' is already registered`), Qi.set(s, e);
+  let { kernelName: t, backendName: n } = e, s = Dg(t, n);
+  Ji.has(s) && ir(`The kernel '${t}' for backend '${n}' is already registered`), Ji.set(s, e);
 }
-function D$(e) {
+function L$(e) {
   let { kernelName: t } = e;
-  Wu.has(t) && X().getBool("DEBUG") && ar(`Overriding the gradient for '${t}'`), Wu.set(t, e);
+  Uu.has(t) && X().getBool("DEBUG") && ir(`Overriding the gradient for '${t}'`), Uu.set(t, e);
 }
-function _de(e, t) {
-  let n = Eg(e, t);
-  if (!Qi.has(n))
+function Ude(e, t) {
+  let n = Dg(e, t);
+  if (!Ji.has(n))
     throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);
-  Qi.delete(n);
+  Ji.delete(n);
 }
-function Ade(e) {
-  if (!Wu.has(e))
+function Gde(e) {
+  if (!Uu.has(e))
     throw new Error(`The gradient '${e}' for backend is not registered`);
-  Wu.delete(e);
+  Uu.delete(e);
 }
-function Ede(e, t) {
-  Jf(e).forEach((s) => {
+function Hde(e, t) {
+  em(e).forEach((s) => {
     let r = Object.assign({}, s, { backendName: t });
     El(r);
   });
 }
-function Eg(e, t) {
+function Dg(e, t) {
   return `${t}_${e}`;
 }
 var w = {};
-Ae(w, { arraysEqual: () => kr, assert: () => O, assertNonNegativeIntegerDimensions: () => sg, assertNonNull: () => wa, assertShapesMatch: () => pn, bytesFromStringArray: () => Xw, bytesPerElement: () => Xf, checkConversionForErrors: () => jw, clamp: () => Vu, computeStrides: () => ao, createScalarValue: () => L$, createShuffledIndices: () => b$, decodeString: () => md, distSquared: () => h$, encodeString: () => Dl, fetch: () => V$, fingerPrint64: () => M$, flatten: () => sa, getArrayFromDType: () => qw, getTypedArrayFromDType: () => Hw, hasEncodingLoss: () => x$, hexToLong: () => Rl, indexToLoc: () => I$, inferDtype: () => Gd, inferFromImplicitShape: () => v$, isBoolean: () => Yw, isFunction: () => hr, isInt: () => Yi, isNumber: () => Qw, isPromise: () => rg, isScalarShape: () => f$, isString: () => ir, isTypedArray: () => Qt, isValidDtype: () => Kw, locToIndex: () => k$, makeOnesTypedArray: () => ng, makeZerosNestedTypedArray: () => w$, makeZerosTypedArray: () => Hd, nearestDivisor: () => hd, nearestLargerEven: () => c$, now: () => Uu, parseAxisParam: () => ts, randUniform: () => p$, repeatedTry: () => y$, rightPad: () => zu, shuffle: () => Uw, shuffleCombo: () => l$, sizeFromShape: () => pt, sizeToSquarishShape: () => g$, squeezeShape: () => Gw, sum: () => d$, swap: () => pd, tanh: () => m$, toNestedArray: () => Hi, toTypedArray: () => lp });
-var sx = xa(HT());
-var qr = sx.default || sx;
+Ae(w, { arraysEqual: () => Ir, assert: () => O, assertNonNegativeIntegerDimensions: () => ag, assertNonNull: () => ka, assertShapesMatch: () => pn, bytesFromStringArray: () => Jw, bytesPerElement: () => Yf, checkConversionForErrors: () => Qw, clamp: () => Wu, computeStrides: () => oo, createScalarValue: () => H$, createShuffledIndices: () => I$, decodeString: () => md, distSquared: () => v$, encodeString: () => Dl, fetch: () => j$, fingerPrint64: () => G$, flatten: () => ra, getArrayFromDType: () => Yw, getTypedArrayFromDType: () => Xw, hasEncodingLoss: () => N$, hexToLong: () => Rl, indexToLoc: () => _$, inferDtype: () => qd, inferFromImplicitShape: () => C$, isBoolean: () => ek, isFunction: () => fr, isInt: () => Zi, isNumber: () => tk, isPromise: () => ig, isScalarShape: () => x$, isString: () => or, isTypedArray: () => Qt, isValidDtype: () => Zw, locToIndex: () => $$, makeOnesTypedArray: () => rg, makeZerosNestedTypedArray: () => T$, makeZerosTypedArray: () => jd, nearestDivisor: () => hd, nearestLargerEven: () => g$, now: () => Gu, parseAxisParam: () => ts, randUniform: () => y$, repeatedTry: () => S$, rightPad: () => Mu, shuffle: () => jw, shuffleCombo: () => m$, sizeFromShape: () => pt, sizeToSquarishShape: () => k$, squeezeShape: () => Kw, sum: () => b$, swap: () => pd, tanh: () => w$, toNestedArray: () => ji, toTypedArray: () => dp });
+var ax = wa(QT());
+var jr = ax.default || ax;
 function Rl(e) {
-  return qr.fromString(e, true, 16);
+  return jr.fromString(e, true, 16);
 }
-var tk = Rl("c3a5c85c97cb3127");
-var Gr = Rl("b492b66fbe98f273");
+var ak = Rl("c3a5c85c97cb3127");
+var Hr = Rl("b492b66fbe98f273");
 var on = Rl("9ae16a3b2f90404f");
-function em(e) {
+function tm(e) {
   return e.xor(e.shru(47));
 }
-function nk(e, t, n) {
+function ik(e, t, n) {
   let s = e.slice(t, t + n);
-  return qr.fromBytes(Array.from(s), true, true);
+  return jr.fromBytes(Array.from(s), true, true);
 }
 function lt(e, t) {
-  return nk(e, t, 8);
+  return ik(e, t, 8);
 }
-function rx(e, t) {
-  return nk(e, t, 4);
+function ix(e, t) {
+  return ik(e, t, 4);
 }
 function Lt(e, t) {
   return t === 0 ? e : e.shru(t).or(e.shl(64 - t));
 }
-function ur(e, t, n = Rl("9ddfea08eb382d69")) {
+function lr(e, t, n = Rl("9ddfea08eb382d69")) {
   let s = e.xor(t).mul(n);
   s = s.xor(s.shru(47));
   let r = t.xor(s).mul(n);
   return r = r.xor(r.shru(47)), r = r.mul(n), r;
 }
-function F$(e, t, n, s, r, a) {
+function B$(e, t, n, s, r, a) {
   r = r.add(e), a = Lt(a.add(r).add(s), 21);
   let i = r;
   return r = r.add(t), r = r.add(n), a = a.add(Lt(r, 44)), [r.add(s), a.add(i)];
 }
 function Vc(e, t, n, s) {
-  return F$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
+  return B$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
 }
-function O$(e, t = e.length) {
+function V$(e, t = e.length) {
   if (t >= 8) {
     let n = on.add(t * 2), s = lt(e, 0).add(on), r = lt(e, t - 8), a = Lt(r, 37).mul(n).add(s), i = Lt(s, 25).add(r).mul(n);
-    return ur(a, i, n);
+    return lr(a, i, n);
   }
   if (t >= 4) {
-    let n = on.add(t * 2), s = rx(e, 0);
-    return ur(s.shl(3).add(t), rx(e, t - 4), n);
+    let n = on.add(t * 2), s = ix(e, 0);
+    return lr(s.shl(3).add(t), ix(e, t - 4), n);
   }
   if (t > 0) {
     let n = e[0], s = e[t >> 1], r = e[t - 1], a = n + (s << 8), i = t + (r << 2);
-    return em(on.mul(a).xor(tk.mul(i))).mul(on);
+    return tm(on.mul(a).xor(ak.mul(i))).mul(on);
   }
   return on;
 }
-function P$(e, t = e.length) {
-  let n = on.add(t * 2), s = lt(e, 0).mul(Gr), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on);
-  return ur(Lt(s.add(r), 43).add(Lt(a, 30)).add(i), s.add(Lt(r.add(on), 18)).add(a), n);
+function W$(e, t = e.length) {
+  let n = on.add(t * 2), s = lt(e, 0).mul(Hr), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on);
+  return lr(Lt(s.add(r), 43).add(Lt(a, 30)).add(i), s.add(Lt(r.add(on), 18)).add(a), n);
 }
-function z$(e, t = e.length) {
-  let n = on.add(t * 2), s = lt(e, 0).mul(on), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on), o = Lt(s.add(r), 43).add(Lt(a, 30)).add(i), u = ur(o, s.add(Lt(r.add(on), 18)).add(a), n), l = lt(e, 16).mul(n), c = lt(e, 24), p = o.add(lt(e, t - 32)).mul(n), d = u.add(lt(e, t - 24)).mul(n);
-  return ur(Lt(l.add(c), 43).add(Lt(p, 30)).add(d), l.add(Lt(c.add(s), 18)).add(p), n);
+function U$(e, t = e.length) {
+  let n = on.add(t * 2), s = lt(e, 0).mul(on), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(on), o = Lt(s.add(r), 43).add(Lt(a, 30)).add(i), u = lr(o, s.add(Lt(r.add(on), 18)).add(a), n), l = lt(e, 16).mul(n), c = lt(e, 24), p = o.add(lt(e, t - 32)).mul(n), d = u.add(lt(e, t - 24)).mul(n);
+  return lr(Lt(l.add(c), 43).add(Lt(p, 30)).add(d), l.add(Lt(c.add(s), 18)).add(p), n);
 }
-function M$(e, t = e.length) {
-  let n = qr.fromNumber(81, true);
+function G$(e, t = e.length) {
+  let n = jr.fromNumber(81, true);
   if (t <= 32)
-    return t <= 16 ? O$(e, t) : P$(e, t);
+    return t <= 16 ? V$(e, t) : W$(e, t);
   if (t <= 64)
-    return z$(e, t);
-  let s = n, r = n.mul(Gr).add(113), a = em(r.mul(on).add(113)).mul(on), i = [qr.UZERO, qr.UZERO], o = [qr.UZERO, qr.UZERO];
+    return U$(e, t);
+  let s = n, r = n.mul(Hr).add(113), a = tm(r.mul(on).add(113)).mul(on), i = [jr.UZERO, jr.UZERO], o = [jr.UZERO, jr.UZERO];
   s = s.mul(on).add(lt(e, 0));
   let u = 0, l = (t - 1 >> 6) * 64, c = l + (t - 1 & 63) - 63;
   do
-    s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(Gr), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(Gr), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Lt(a.add(o[0]), 33).mul(Gr), i = Vc(e, u, i[1].mul(Gr), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
+    s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(Hr), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(Hr), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Lt(a.add(o[0]), 33).mul(Hr), i = Vc(e, u, i[1].mul(Hr), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
   while (u !== l);
-  let p = Gr.add(a.and(255).shl(1));
-  return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Lt(a.add(o[0]), 33).mul(p), i = Vc(e, u, i[1].mul(p), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], ur(ur(i[0], o[0], p).add(em(r).mul(tk)).add(a), ur(i[1], o[1], p).add(s), p);
+  let p = Hr.add(a.and(255).shl(1));
+  return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Lt(a.add(o[0]), 33).mul(p), i = Vc(e, u, i[1].mul(p), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], lr(lr(i[0], o[0], p).add(tm(r).mul(ak)).add(a), lr(i[1], o[1], p).add(s), p);
 }
-function L$(e, t) {
-  return t === "string" ? Dl(e) : lp([e], t);
+function H$(e, t) {
+  return t === "string" ? Dl(e) : dp([e], t);
 }
-function B$(e, t) {
+function q$(e, t) {
   return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool";
 }
-function lp(e, t) {
+function dp(e, t) {
   if (t === "string")
     throw new Error("Cannot convert a string[] to a TypedArray");
-  if (Array.isArray(e) && (e = sa(e)), X().getBool("DEBUG") && jw(e, t), B$(e, t))
+  if (Array.isArray(e) && (e = ra(e)), X().getBool("DEBUG") && Qw(e, t), q$(e, t))
     return e;
   if (t == null || t === "float32" || t === "complex64")
     return new Float32Array(e);
@@ -4121,10 +4121,10 @@ function lp(e, t) {
   } else
     throw new Error(`Unknown data type ${t}`);
 }
-function Uu() {
+function Gu() {
   return X().platform.now();
 }
-function V$(e, t) {
+function j$(e, t) {
   return X().platform.fetch(e, t);
 }
 function Dl(e, t = "utf-8") {
@@ -4133,27 +4133,27 @@ function Dl(e, t = "utf-8") {
 function md(e, t = "utf-8") {
   return t = t || "utf-8", X().platform.decode(e, t);
 }
-var W$ = class {
+var K$ = class {
   constructor(e, t) {
-    this.backendTimer = e, this.logger = t, t == null && (this.logger = new G$());
+    this.backendTimer = e, this.logger = t, t == null && (this.logger = new Y$());
   }
   profileKernel(e, t, n) {
     let s, r = () => {
       s = n();
-    }, a, i = Uu();
+    }, a, i = Gu();
     if (this.backendTimer.timerAvailable())
       a = this.backendTimer.time(r);
     else {
       r();
       for (let u of s)
         u.dataSync();
-      a = Promise.resolve({ kernelMs: Uu() - i });
+      a = Promise.resolve({ kernelMs: Gu() - i });
     }
     if (X().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
       for (let u = 0; u < s.length; u++) {
         let l = s[u];
         l.data().then((c) => {
-          U$(c, l.dtype, e);
+          X$(c, l.dtype, e);
         });
       }
     return { kernelName: e, outputs: s, inputs: t, timeMs: a.then((u) => u.kernelMs), extraInfo: a.then((u) => u.getExtraProfileInfo != null ? u.getExtraProfileInfo() : "") };
@@ -4167,7 +4167,7 @@ var W$ = class {
     });
   }
 };
-function U$(e, t, n) {
+function X$(e, t, n) {
   if (t !== "float32")
     return false;
   for (let s = 0; s < e.length; s++) {
@@ -4177,9 +4177,9 @@ function U$(e, t, n) {
   }
   return false;
 }
-var G$ = class {
+var Y$ = class {
   logKernelProfile(e, t, n, s, r, a) {
-    let i = typeof s == "number" ? zu(`${s}ms`, 9) : s.error, o = zu(e, 25), u = t.rank, l = t.size, c = zu(t.shape.toString(), 14), p = "";
+    let i = typeof s == "number" ? Mu(`${s}ms`, 9) : s.error, o = Mu(e, 25), u = t.rank, l = t.size, c = Mu(t.shape.toString(), 14), p = "";
     for (let d in r) {
       let h = r[d];
       if (h != null) {
@@ -4190,7 +4190,7 @@ var G$ = class {
     console.log(`%c${o}	%c${i}	%c${u}D ${c}	%c${l}	%c${p}	%c${a}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
   }
 };
-function H$(e, t, n) {
+function Q$(e, t, n) {
   let s = {}, r = {};
   for (let u = 0; u < t.length; u++)
     s[t[u].id] = true;
@@ -4234,7 +4234,7 @@ function H$(e, t, n) {
   }
   return o;
 }
-function q$(e, t, n, s) {
+function Z$(e, t, n, s) {
   for (let r = t.length - 1; r >= 0; r--) {
     let a = t[r], i = [];
     if (a.outputs.forEach((u) => {
@@ -4250,7 +4250,7 @@ function q$(e, t, n, s) {
       if (l.dtype !== "float32")
         throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input ${u} must have 'float32' dtype, but has '${l.dtype}'`);
       let c = a.inputs[u];
-      if (!kr(l.shape, c.shape))
+      if (!Ir(l.shape, c.shape))
         throw new Error(`Error in gradient for op ${a.kernelName}. The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`);
       if (e[c.id] == null)
         e[c.id] = l;
@@ -4261,57 +4261,57 @@ function q$(e, t, n, s) {
     }
   }
 }
-var ax = 20;
-var Su = 3;
-var Ff = 7;
-function j$(e, t, n, s) {
-  let r = ao(t), a = K$(e, t, n, r), i = t.length, o = ed(e, t, n, r, a), u = ["Tensor"];
+var ox = 20;
+var Cu = 3;
+var Pf = 7;
+function J$(e, t, n, s) {
+  let r = oo(t), a = e_(e, t, n, r), i = t.length, o = ed(e, t, n, r, a), u = ["Tensor"];
   return s && (u.push(`  dtype: ${n}`), u.push(`  rank: ${i}`), u.push(`  shape: [${t}]`), u.push("  values:")), u.push(o.map((l) => "    " + l).join(`
 `)), u.join(`
 `);
 }
-function K$(e, t, n, s) {
-  let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? _u(e) : e;
+function e_(e, t, n, s) {
+  let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? Au(e) : e;
   if (o > 1)
     for (let l = 0; l < r / a; l++) {
       let c = l * a;
       for (let p = 0; p < a; p++)
-        i[p] = Math.max(i[p], $u(u[c + p], 0, n).length);
+        i[p] = Math.max(i[p], _u(u[c + p], 0, n).length);
     }
   return i;
 }
-function $u(e, t, n) {
+function _u(e, t, n) {
   let s;
-  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Ff))} + ${parseFloat(e[1].toFixed(Ff))}j` : ir(e) ? s = `'${e}'` : n === "bool" ? s = sk(e) : s = parseFloat(e.toFixed(Ff)).toString(), zu(s, t);
+  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Pf))} + ${parseFloat(e[1].toFixed(Pf))}j` : or(e) ? s = `'${e}'` : n === "bool" ? s = ok(e) : s = parseFloat(e.toFixed(Pf)).toString(), Mu(s, t);
 }
-function sk(e) {
+function ok(e) {
   return e === 0 ? "false" : "true";
 }
 function ed(e, t, n, s, r, a = true) {
   let i = n === "complex64" ? 2 : 1, o = t[0], u = t.length;
   if (u === 0) {
     if (n === "complex64") {
-      let m = _u(e);
-      return [$u(m[0], 0, n)];
+      let m = Au(e);
+      return [_u(m[0], 0, n)];
     }
-    return n === "bool" ? [sk(e[0])] : [e[0].toString()];
+    return n === "bool" ? [ok(e[0])] : [e[0].toString()];
   }
   if (u === 1) {
-    if (o > ax) {
-      let g = Su * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Su) * i, o * i));
-      return n === "complex64" && (b = _u(b), y = _u(y)), ["[" + b.map((v, x) => $u(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => $u(v, r[o - Su + x], n)).join(", ") + "]"];
+    if (o > ox) {
+      let g = Cu * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Cu) * i, o * i));
+      return n === "complex64" && (b = Au(b), y = Au(y)), ["[" + b.map((v, x) => _u(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => _u(v, r[o - Cu + x], n)).join(", ") + "]"];
     }
-    let m = n === "complex64" ? _u(e) : Array.from(e);
-    return ["[" + m.map((g, b) => $u(g, r[b], n)).join(", ") + "]"];
+    let m = n === "complex64" ? Au(e) : Array.from(e);
+    return ["[" + m.map((g, b) => _u(g, r[b], n)).join(", ") + "]"];
   }
   let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = [];
-  if (o > ax) {
-    for (let m = 0; m < Su; m++) {
+  if (o > ox) {
+    for (let m = 0; m < Cu; m++) {
       let g = m * p, b = g + p;
       d.push(...ed(e.slice(g, b), l, n, c, r, false));
     }
     d.push("...");
-    for (let m = o - Su; m < o; m++) {
+    for (let m = o - Cu; m < o; m++) {
       let g = m * p, b = g + p;
       d.push(...ed(e.slice(g, b), l, n, c, r, m === o - 1));
     }
@@ -4331,7 +4331,7 @@ function ed(e, t, n, s, r, a = true) {
 `;
   return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : f), d;
 }
-function _u(e) {
+function Au(e) {
   let t = [];
   for (let n = 0; n < e.length; n += 2)
     t.push([e[n], e[n + 1]]);
@@ -4345,7 +4345,7 @@ var Vt = class {
     }
     if (t === "complex64")
       throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
-    this.values = n || qw(t, this.size), this.strides = ao(e);
+    this.values = n || Yw(t, this.size), this.strides = oo(e);
   }
   set(e, ...t) {
     t.length === 0 && (t = [0]), O(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
@@ -4395,37 +4395,37 @@ var Vt = class {
   }
 };
 var cs = null;
-var Wi = null;
-var X$ = null;
-function Y$(e) {
+var Gi = null;
+var t_ = null;
+function n_(e) {
   cs = e;
 }
-function Q$(e) {
-  Wi = e;
+function s_(e) {
+  Gi = e;
 }
-function Z$(e) {
-  X$ = e;
+function r_(e) {
+  t_ = e;
 }
 var et = class {
   constructor(e, t, n, s) {
-    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = pt(e), this.strides = ao(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
+    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = pt(e), this.strides = oo(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
   }
   get rank() {
     return this.shape.length;
   }
   async buffer() {
     let e = await this.data();
-    return Wi.buffer(this.shape, this.dtype, e);
+    return Gi.buffer(this.shape, this.dtype, e);
   }
   bufferSync() {
-    return Wi.buffer(this.shape, this.dtype, this.dataSync());
+    return Gi.buffer(this.shape, this.dtype, this.dataSync());
   }
   async array() {
     let e = await this.data();
-    return Hi(this.shape, e, this.dtype === "complex64");
+    return ji(this.shape, e, this.dtype === "complex64");
   }
   arraySync() {
-    return Hi(this.shape, this.dataSync(), this.dtype === "complex64");
+    return ji(this.shape, this.dataSync(), this.dtype === "complex64");
   }
   async data() {
     this.throwIfDisposed();
@@ -4470,27 +4470,27 @@ var et = class {
       throw new Error("Tensor is disposed.");
   }
   print(e = false) {
-    return Wi.print(this, e);
+    return Gi.print(this, e);
   }
   clone() {
-    return this.throwIfDisposed(), Wi.clone(this);
+    return this.throwIfDisposed(), Gi.clone(this);
   }
   toString(e = false) {
     let t = this.dataSync();
-    return j$(t, this.shape, this.dtype, e);
+    return J$(t, this.shape, this.dtype, e);
   }
   cast(e) {
-    return this.throwIfDisposed(), Wi.cast(this, e);
+    return this.throwIfDisposed(), Gi.cast(this, e);
   }
   variable(e = true, t, n) {
     return this.throwIfDisposed(), cs().makeVariable(this, e, t, n);
   }
 };
 Object.defineProperty(et, Symbol.hasInstance, { value: (e) => !!e && e.data != null && e.dataSync != null && e.throwIfDisposed != null });
-function J$() {
-  return ag("Tensor", () => et);
+function a_() {
+  return og("Tensor", () => et);
 }
-J$();
+a_();
 var gd = class extends et {
   constructor(e, t, n, s) {
     super(e.shape, e.dtype, e.dataId, s), this.trainable = t, this.name = n;
@@ -4498,7 +4498,7 @@ var gd = class extends et {
   assign(e) {
     if (e.dtype !== this.dtype)
       throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);
-    if (!kr(e.shape, this.shape))
+    if (!Ir(e.shape, this.shape))
       throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);
     cs().disposeTensor(this), this.dataId = e.dataId, cs().incRef(this, null);
   }
@@ -4508,22 +4508,22 @@ var gd = class extends et {
 };
 Object.defineProperty(gd, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
 var _s = {};
-Ae(_s, { assertTypesMatch: () => uk, getTensorsInContainer: () => Rg, isTensorInList: () => n_, makeTypesMatch: () => vt });
-var e_ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(e_ || {});
-var rk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(rk || {});
-var ak = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(ak || {});
-var ik = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(ik || {});
-var ok = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(ok || {});
-var t_ = { float32: ik, int32: rk, bool: ak, complex64: ok };
+Ae(_s, { assertTypesMatch: () => pk, getTensorsInContainer: () => Fg, isTensorInList: () => u_, makeTypesMatch: () => vt });
+var i_ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(i_ || {});
+var uk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(uk || {});
+var lk = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(lk || {});
+var ck = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(ck || {});
+var dk = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(dk || {});
+var o_ = { float32: ck, int32: uk, bool: lk, complex64: dk };
 function cn(e, t) {
   if (e === "string" || t === "string") {
     if (e === "string" && t === "string")
       return "string";
     throw new Error(`Can not upcast ${e} with ${t}`);
   }
-  return t_[e][t];
+  return o_[e][t];
 }
-function cp(e) {
+function pp(e) {
   return cn(e, "int32");
 }
 function vt(e, t) {
@@ -4532,38 +4532,38 @@ function vt(e, t) {
   let n = cn(e.dtype, t.dtype);
   return [e.cast(n), t.cast(n)];
 }
-function uk(e, t) {
+function pk(e, t) {
   O(e.dtype === t.dtype, () => `The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`);
 }
-function n_(e, t) {
+function u_(e, t) {
   return t.some((n) => n.id === e.id);
 }
-function Rg(e) {
+function Fg(e) {
   let t = [];
-  return lk(e, t, /* @__PURE__ */ new Set()), t;
+  return hk(e, t, /* @__PURE__ */ new Set()), t;
 }
-function lk(e, t, n) {
+function hk(e, t, n) {
   if (e == null)
     return;
   if (e instanceof et) {
     t.push(e);
     return;
   }
-  if (!s_(e))
+  if (!l_(e))
     return;
   let s = e;
   for (let r in s) {
     let a = s[r];
-    n.has(a) || (n.add(a), lk(a, t, n));
+    n.has(a) || (n.add(a), hk(a, t, n));
   }
 }
-function s_(e) {
+function l_(e) {
   return Array.isArray(e) || typeof e == "object";
 }
-function Of(e) {
+function zf(e) {
   return e.kernelName != null;
 }
-var ix = class {
+var ux = class {
   constructor() {
     this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
       return Array.from(new Set(this.kernels.map((e) => e.name)));
@@ -4574,9 +4574,9 @@ var ix = class {
       this.registeredVariables[e].dispose();
   }
 };
-var tm = class {
+var nm = class {
   constructor(e) {
-    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new ix();
+    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new ux();
   }
   async ready() {
     if (this.pendingBackendInit != null)
@@ -4622,7 +4622,7 @@ var tm = class {
     return e in this.registryFactory ? this.registryFactory[e].factory : null;
   }
   registerBackend(e, t, n = 1) {
-    return e in this.registryFactory ? (ar(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t, priority: n }, true);
+    return e in this.registryFactory ? (ir(`${e} backend was already registered. Reusing existing backend factory.`), false) : (this.registryFactory[e] = { factory: t, priority: n }, true);
   }
   async setBackend(e) {
     if (this.registryFactory[e] == null)
@@ -4633,15 +4633,15 @@ var tm = class {
       if (!(n ? await t : t))
         return false;
     }
-    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new W$(this.backendInstance), true;
+    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new K$(this.backendInstance), true;
   }
   setupRegisteredKernels() {
-    Jf(this.backendName).forEach((t) => {
+    em(this.backendName).forEach((t) => {
       t.setupFunc != null && t.setupFunc(this.backendInstance);
     });
   }
   disposeRegisteredKernels(e) {
-    Jf(e).forEach((n) => {
+    em(e).forEach((n) => {
       n.disposeFunc != null && n.disposeFunc(this.registry[e]);
     });
   }
@@ -4651,13 +4651,13 @@ var tm = class {
       throw new Error(`Cannot initialize backend ${e}, no registration found.`);
     try {
       let n = t.factory();
-      if (n && !(n instanceof nl) && typeof n.then == "function") {
-        let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, ar(`Initialization of backend ${e} failed`), ar(a.stack || a.message)), false));
+      if (n && !(n instanceof sl) && typeof n.then == "function") {
+        let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, ir(`Initialization of backend ${e} failed`), ir(a.stack || a.message)), false));
         return this.pendingBackendInit = r, { success: r, asyncInit: true };
       } else
         return this.registry[e] = n, { success: true, asyncInit: false };
     } catch (n) {
-      return ar(`Initialization of backend ${e} failed`), ar(n.stack || n.message), { success: false, asyncInit: false };
+      return ir(`Initialization of backend ${e} failed`), ir(n.stack || n.message), { success: false, asyncInit: false };
     }
   }
   removeBackend(e) {
@@ -4709,20 +4709,20 @@ var tm = class {
     }
   }
   nextTensorId() {
-    return tm.nextTensorId++;
+    return nm.nextTensorId++;
   }
   nextVariableId() {
-    return tm.nextVariableId++;
+    return nm.nextVariableId++;
   }
   clone(e) {
-    let t = M.runKernel(Ba, { x: e }), n = { x: e }, s = (a) => ({ x: () => {
+    let t = M.runKernel(Wa, { x: e }), n = { x: e }, s = (a) => ({ x: () => {
       let i = "float32", o = { x: a }, u = { dtype: i };
-      return M.runKernel(Na, o, u);
+      return M.runKernel(Ta, o, u);
     } }), r = [];
     return this.addTapeNode(this.state.activeScope.name, n, [t], s, r, {}), t;
   }
   runKernel(e, t, n) {
-    if (this.backendName == null && this.backend, !(Zf(e, this.backendName) != null))
+    if (this.backendName == null && this.backend, !(Jf(e, this.backendName) != null))
       throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);
     return this.runKernelFunc({ kernelName: e, inputs: t, attrs: n });
   }
@@ -4743,11 +4743,11 @@ var tm = class {
     this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0);
     let i;
     this.backendName == null && this.backend;
-    let o, u = Of(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
-    if (Of(e)) {
+    let o, u = zf(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
+    if (zf(e)) {
       let { kernelName: h, inputs: f, attrs: m } = e;
       this.backendName == null && this.backend;
-      let g = Zf(h, this.backendName);
+      let g = Jf(h, this.backendName);
       O(g != null, () => `Cannot find registered kernel '${h}' for backend '${this.backendName}'`), i = () => {
         let b = this.backend.numDataIds();
         o = g.kernelFunc({ inputs: f, attrs: m, backend: this.backend });
@@ -4776,7 +4776,7 @@ var tm = class {
         return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, m, g), g;
       };
     }
-    let { inputs: l, attrs: c } = e, p = Of(e) ? null : e.backwardsFunc, d;
+    let { inputs: l, attrs: c } = e, p = zf(e) ? null : e.backwardsFunc, d;
     return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
       !this.ENV.getBool("DEBUG") && !this.state.profiling ? t = i() : (d = this.profiler.profileKernel(u, l, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t = d.outputs);
     }), s && this.addTapeNode(u, l, t, p, n, c), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - r, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(l).map((h) => l[h] != null ? l[h].shape : null), outputShapes: t.map((h) => h.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(o) ? t : t[0];
@@ -4785,7 +4785,7 @@ var tm = class {
     return e.map((n) => this.keep(this.clone(n)));
   }
   getTensorsForGradient(e, t, n) {
-    let s = nx(e);
+    let s = rx(e);
     if (s != null) {
       let r = s.inputsToSave || [], a = s.outputsToSave || [], i;
       s.saveAllInputs ? (O(Array.isArray(t), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t).map((u) => t[u])) : i = r.map((u) => t[u]);
@@ -4799,10 +4799,10 @@ var tm = class {
       throw new Error("Values passed to engine.makeTensor() are null");
     n = n || "float32", s = s || this.backend;
     let r = e;
-    n === "string" && ir(e[0]) && (r = e.map((o) => Dl(o)));
+    n === "string" && or(e[0]) && (r = e.map((o) => Dl(o)));
     let a = s.write(r, t, n), i = new et(t, n, a, this.nextTensorId());
     if (this.trackTensor(i, s), n === "string") {
-      let o = this.state.tensorInfo.get(a), u = Xw(r);
+      let o = this.state.tensorInfo.get(a), u = Jw(r);
       this.state.numBytes += u - o.bytes, o.bytes = u;
     }
     return i;
@@ -4822,7 +4822,7 @@ var tm = class {
   trackTensor(e, t) {
     this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
     let n = 0;
-    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * Xf(e.dtype)), this.state.numBytes += n, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t || this.backend, dtype: e.dtype, shape: e.shape, bytes: n })), e instanceof gd || this.track(e);
+    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * Yf(e.dtype)), this.state.numBytes += n, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t || this.backend, dtype: e.dtype, shape: e.shape, bytes: n })), e instanceof gd || this.track(e);
   }
   incRef(e, t) {
     this.trackTensor(e, t), this.backend.incRef(e.dataId);
@@ -4835,7 +4835,7 @@ var tm = class {
       return;
     let t = this.state.tensorInfo.get(e.dataId);
     if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
-      let n = e.size * Xf(e.dtype);
+      let n = e.size * Yf(e.dtype);
       this.state.numBytes -= n;
     }
     t.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t.backend);
@@ -4865,10 +4865,10 @@ var tm = class {
     return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
   }
   addTapeNode(e, t, n, s, r, a) {
-    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = nx(e);
+    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = rx(e);
     o != null && (s = o.gradFunc), s != null && (i.gradient = (u) => (u = u.map((l, c) => {
       if (l == null) {
-        let p = n[c], d = Hd(p.size, p.dtype);
+        let p = n[c], d = jd(p.size, p.dtype);
         return this.makeTensor(d, p.shape, p.dtype);
       }
       return l;
@@ -4888,7 +4888,7 @@ var tm = class {
     e && (t.name = e), this.state.scopeStack.push(t), this.state.activeScope = t;
   }
   endScope(e) {
-    let t = Rg(e), n = new Set(t.map((r) => r.id));
+    let t = Fg(e), n = new Set(t.map((r) => r.id));
     for (let r = 0; r < this.state.activeScope.track.length; r++) {
       let a = this.state.activeScope.track[r];
       !a.kept && !n.has(a.id) && a.dispose();
@@ -4903,12 +4903,12 @@ var tm = class {
       throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);
     let r = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e));
     O(r instanceof et, () => "The result y returned by f() must be a tensor.");
-    let a = H$(this.state.activeTape, t, r);
+    let a = Q$(this.state.activeTape, t, r);
     if (!s && a.length === 0 && t.length > 0)
       throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
     return this.tidy("backward", () => {
       let i = {};
-      i[r.id] = n == null ? r_(r.shape) : n, q$(i, a, (u) => this.tidy(u), a_);
+      i[r.id] = n == null ? c_(r.shape) : n, Z$(i, a, (u) => this.tidy(u), d_);
       let o = t.map((u) => i[u.id]);
       return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => {
         for (let l of u.saved)
@@ -4917,13 +4917,13 @@ var tm = class {
     });
   }
   customGrad(e) {
-    return O(hr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => {
+    return O(fr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => {
       O(t.every((i) => i instanceof et), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
       let n, s = {};
       t.forEach((i, o) => {
         s[o] = i;
       });
-      let r = (i, o) => (n = e(...t, o), O(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), O(hr(n.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), n.value), a = (i, o) => {
+      let r = (i, o) => (n = e(...t, o), O(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), O(fr(n.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), n.value), a = (i, o) => {
         let u = n.gradFunc(i, o), l = Array.isArray(u) ? u : [u];
         O(l.length === t.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), O(l.every((p) => p instanceof et), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
         let c = {};
@@ -4944,8 +4944,8 @@ var tm = class {
     return this.state.tensorInfo.get(e).backend.readToGPU(e, t);
   }
   async time(e) {
-    let t = Uu(), n = await this.backend.time(e);
-    return n.wallMs = Uu() - t, n;
+    let t = Gu(), n = await this.backend.time(e);
+    return n.wallMs = Gu() - t, n;
   }
   track(e) {
     return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e;
@@ -4954,45 +4954,45 @@ var tm = class {
     return this.state.registeredVariables;
   }
   reset() {
-    this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new ix();
+    this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new ux();
     for (let e in this.registry)
       this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e];
     this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null;
   }
 };
-var Dg = tm;
-Dg.nextTensorId = 0;
-Dg.nextVariableId = 0;
-function r_(e) {
-  let t = ng(pt(e), "float32");
+var Og = nm;
+Og.nextTensorId = 0;
+Og.nextVariableId = 0;
+function c_(e) {
+  let t = rg(pt(e), "float32");
   return M.makeTensor(t, e, "float32");
 }
-function ck() {
-  let e = ek();
+function fk() {
+  let e = rk();
   if (e._tfengine == null) {
-    let t = new S$(e);
-    e._tfengine = new Dg(t);
+    let t = new A$(e);
+    e._tfengine = new Og(t);
   }
-  return $$(e._tfengine.ENV), Y$(() => e._tfengine), e._tfengine;
+  return F$(e._tfengine.ENV), n_(() => e._tfengine), e._tfengine;
 }
-var M = ck();
-function a_(e, t) {
+var M = fk();
+function d_(e, t) {
   let n = { a: e, b: t };
-  return M.runKernel(Ir, n);
+  return M.runKernel(Sr, n);
 }
-var dp = {};
-Ae(dp, { isBrowser: () => dk, isMobile: () => u_, mockIsMobile: () => o_ });
-function i_() {
+var hp = {};
+Ae(hp, { isBrowser: () => mk, isMobile: () => f_, mockIsMobile: () => h_ });
+function p_() {
   return typeof navigator != "undefined" && navigator != null;
 }
-var nm;
-function o_(e) {
-  nm = e;
+var sm;
+function h_(e) {
+  sm = e;
 }
-function u_(e) {
-  if (nm !== void 0)
-    return nm;
-  if (e || i_()) {
+function f_(e) {
+  if (sm !== void 0)
+    return sm;
+  if (e || p_()) {
     if (e || (e = navigator), e.product === "ReactNative")
       return true;
     let t = e.userAgent || e.vendor || (typeof window != "undefined" ? window.opera : "");
@@ -5004,23 +5004,23 @@ function u_(e) {
   }
   return false;
 }
-function dk() {
+function mk() {
   return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
 }
-var jn = X();
-jn.registerFlag("DEBUG", () => false, (e) => {
+var Kn = X();
+Kn.registerFlag("DEBUG", () => false, (e) => {
   e && console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
 });
-jn.registerFlag("IS_BROWSER", () => dk());
-jn.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
-jn.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
-jn.registerFlag("PROD", () => false);
-jn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => jn.getBool("DEBUG"));
-jn.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
-jn.registerFlag("IS_TEST", () => false);
-jn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
-jn.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
-jn.registerFlag("ENGINE_COMPILE_ONLY", () => false);
+Kn.registerFlag("IS_BROWSER", () => mk());
+Kn.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
+Kn.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
+Kn.registerFlag("PROD", () => false);
+Kn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => Kn.getBool("DEBUG"));
+Kn.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
+Kn.registerFlag("IS_TEST", () => false);
+Kn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
+Kn.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
+Kn.registerFlag("ENGINE_COMPILE_ONLY", () => false);
 function Rs(e, t) {
   let n = e;
   if (Qt(e))
@@ -5030,9 +5030,9 @@ function Rs(e, t) {
   let s = [];
   for (; Array.isArray(n) || Qt(n) && t !== "string"; )
     s.push(n.length), n = n[0];
-  return Array.isArray(e) && X().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && pk(e, s, []), s;
+  return Array.isArray(e) && X().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && gk(e, s, []), s;
 }
-function pk(e, t, n) {
+function gk(e, t, n) {
   if (n = n || [], !Array.isArray(e) && !Qt(e)) {
     O(t.length === 0, () => `Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);
     return;
@@ -5040,9 +5040,9 @@ function pk(e, t, n) {
   O(t.length > 0, () => `Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`), O(e.length === t[0], () => `Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);
   let s = t.slice(1);
   for (let r = 0; r < e.length; ++r)
-    pk(e[r], s, n.concat(r));
+    gk(e[r], s, n.concat(r));
 }
-function ox(e, t, n, s) {
+function lx(e, t, n, s) {
   if (e !== "string_or_numeric") {
     if (e == null)
       throw new Error("Expected dtype cannot be null.");
@@ -5052,54 +5052,54 @@ function ox(e, t, n, s) {
 }
 function _(e, t, n, s = "numeric") {
   if (e instanceof et)
-    return ox(s, e.dtype, t, n), e;
-  let r = Gd(e);
-  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), ox(s, r, t, n), e == null || !Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
+    return lx(s, e.dtype, t, n), e;
+  let r = qd(e);
+  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), lx(s, r, t, n), e == null || !Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
     let u = e == null ? "null" : e.constructor.name;
     throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`);
   }
   let a = Rs(e, r);
   !Qt(e) && !Array.isArray(e) && (e = [e]);
-  let o = r !== "string" ? lp(e, r) : sa(e, [], true);
+  let o = r !== "string" ? dp(e, r) : ra(e, [], true);
   return M.makeTensor(o, a, r);
 }
-function Gu(e, t, n, s = "numeric") {
+function Hu(e, t, n, s = "numeric") {
   if (!Array.isArray(e))
     throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);
   return e.map((a, i) => _(a, `${t}[${i}]`, n, s));
 }
-var l_ = "__op";
+var m_ = "__op";
 function L(e) {
   let t = Object.keys(e);
   if (t.length !== 1)
     throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${t.length} keys.`);
   let n = t[0], s = e[n];
-  n.endsWith("_") && (n = n.substring(0, n.length - 1)), n = n + l_;
+  n.endsWith("_") && (n = n.substring(0, n.length - 1)), n = n + m_;
   let r = (...a) => {
     M.startScope(n);
     try {
       let i = s(...a);
-      return rg(i) && console.error("Cannot return a Promise inside of tidy."), M.endScope(i), i;
+      return ig(i) && console.error("Cannot return a Promise inside of tidy."), M.endScope(i), i;
     } catch (i) {
       throw M.endScope(null), i;
     }
   };
   return Object.defineProperty(r, "name", { value: n, configurable: true }), r;
 }
-function c_(e, t) {
+function g_(e, t) {
   let n = _(e, "real", "complex"), s = _(t, "imag", "complex");
   pn(n.shape, s.shape, `real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);
   let r = { real: n, imag: s };
-  return M.runKernel(jd, r);
+  return M.runKernel(Xd, r);
 }
-var oa = L({ complex_: c_ });
-function Nr(e, t, n, s) {
-  if (s == null && (s = Gd(e)), s === "complex64")
+var ua = L({ complex_: g_ });
+function Tr(e, t, n, s) {
+  if (s == null && (s = qd(e)), s === "complex64")
     throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
   if (!Qt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string")
     throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
   if (t != null) {
-    sg(t);
+    ag(t);
     let r = pt(t), a = pt(n);
     O(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);
     for (let i = 0; i < n.length; ++i) {
@@ -5107,15 +5107,15 @@ function Nr(e, t, n, s) {
       O(n[i] === t[i] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `);
     }
   }
-  return !Qt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? lp(e, s) : sa(e, [], true), M.makeTensor(e, t, s);
+  return !Qt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? dp(e, s) : ra(e, [], true), M.makeTensor(e, t, s);
 }
 function fs(e, t, n) {
   let s = Rs(e, n);
-  return Nr(e, t, s, n);
+  return Tr(e, t, s, n);
 }
-var sm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
+var rm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
 var bd = 4;
-async function d_(e, t) {
+async function b_(e, t) {
   let n = [], s = [], r = Array.isArray(e) ? e.map((i) => i.name) : Object.keys(e);
   for (let i = 0; i < r.length; ++i) {
     let o = r[i], u = Array.isArray(e) ? e[i].tensor : e[o];
@@ -5137,9 +5137,9 @@ async function d_(e, t) {
     t != null && (l.group = t), n.push(l);
   }
   let a = await Promise.all(s);
-  return { data: p_(a), specs: n };
+  return { data: y_(a), specs: n };
 }
-function hk(e, t) {
+function bk(e, t) {
   let n = {}, s, r = 0;
   for (let a of t) {
     let i = a.name, o = a.dtype, u = a.shape, l = pt(u), c;
@@ -5153,7 +5153,7 @@ function hk(e, t) {
           throw new Error(`Weight ${a.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${o}.`);
       } else
         throw new Error(`Weight ${a.name} has unknown quantization dtype ${p.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
-      let d = sm[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h);
+      let d = rm[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h);
       if (o === "float32")
         if (p.dtype === "uint8" || p.dtype === "uint16") {
           c = new Float32Array(f.length);
@@ -5162,7 +5162,7 @@ function hk(e, t) {
             c[m] = g * p.scale + p.min;
           }
         } else if (p.dtype === "float16")
-          s === void 0 && (s = y_()), c = s(f);
+          s === void 0 && (s = S_()), c = s(f);
         else
           throw new Error(`Unsupported quantization type ${p.dtype} for weight type float32.`);
       else if (o === "int32") {
@@ -5186,7 +5186,7 @@ function hk(e, t) {
         c.push(f), r += h;
       }
     } else {
-      let p = sm[o], d = e.slice(r, r + l * p);
+      let p = rm[o], d = e.slice(r, r + l * p);
       if (o === "float32")
         c = new Float32Array(d);
       else if (o === "int32")
@@ -5199,7 +5199,7 @@ function hk(e, t) {
         for (let b = 0; b < h.length; b++)
           h[b] = c[b * 2], f[b] = c[b * 2 + 1];
         let m = fs(h, u, "float32"), g = fs(f, u, "float32");
-        n[i] = oa(m, g), m.dispose(), g.dispose();
+        n[i] = ua(m, g), m.dispose(), g.dispose();
       } else
         throw new Error(`Unsupported dtype in weight '${i}': ${o}`);
       r += l * p;
@@ -5208,7 +5208,7 @@ function hk(e, t) {
   }
   return n;
 }
-function p_(e) {
+function y_(e) {
   if (e === null)
     throw new Error(`Invalid input value: ${JSON.stringify(e)}`);
   let t = 0, n = [];
@@ -5221,20 +5221,20 @@ function p_(e) {
     s.set(new Uint8Array(a.buffer), r), r += a.byteLength;
   }), s.buffer;
 }
-var Fg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
-function ux(e) {
-  return Fg ? Buffer.byteLength(e) : new Blob([e]).size;
+var Pg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
+function cx(e) {
+  return Pg ? Buffer.byteLength(e) : new Blob([e]).size;
 }
-function h_(e) {
-  if (Fg)
+function v_(e) {
+  if (Pg)
     return Buffer.from(e).toString("base64");
   let t = new Uint8Array(e), n = "";
   for (let s = 0, r = t.length; s < r; s++)
     n += String.fromCharCode(t[s]);
   return btoa(n);
 }
-function f_(e) {
-  if (Fg) {
+function x_(e) {
+  if (Pg) {
     let s = Buffer.from(e, "base64");
     return s.buffer.slice(s.byteOffset, s.byteOffset + s.byteLength);
   }
@@ -5243,7 +5243,7 @@ function f_(e) {
     n.set([t.charCodeAt(s)], s);
   return n.buffer;
 }
-function Og(e) {
+function zg(e) {
   if (e.length === 1)
     return e[0];
   let t = 0;
@@ -5255,18 +5255,18 @@ function Og(e) {
     n.set(new Uint8Array(r), s), s += r.byteLength;
   }), n.buffer;
 }
-function lx(e) {
+function dx(e) {
   let t = "/";
   for (e = e.trim(); e.endsWith(t); )
     e = e.slice(0, e.length - 1);
   let n = e.split(t);
   return n[n.length - 1];
 }
-function fk(e, t) {
+function yk(e, t) {
   let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, weightsManifest: t };
   return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), n;
 }
-async function Pg(e, t) {
+async function Mg(e, t) {
   let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy };
   if (e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), e.weightsManifest != null) {
     let [s, r] = await t(e.weightsManifest);
@@ -5277,9 +5277,9 @@ async function Pg(e, t) {
 function Fl(e) {
   if (e.modelTopology instanceof ArrayBuffer)
     throw new Error("Expected JSON model topology, received ArrayBuffer.");
-  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : ux(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : ux(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
+  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : cx(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : cx(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
 }
-function m_() {
+function w_() {
   let e = (n) => {
     let s = n << 13, r = 0;
     for (; (s & 8388608) === 0; )
@@ -5293,7 +5293,7 @@ function m_() {
     t[n] = 939524096 + (n - 1024 << 13);
   return t;
 }
-function g_() {
+function k_() {
   let e = new Uint32Array(64);
   e[0] = 0, e[31] = 1199570944, e[32] = 2147483648, e[63] = 3347054592;
   for (let t = 1; t < 31; t++)
@@ -5302,14 +5302,14 @@ function g_() {
     e[t] = 2147483648 + (t - 32 << 23);
   return e;
 }
-function b_() {
+function I_() {
   let e = new Uint32Array(64);
   for (let t = 0; t < 64; t++)
     e[t] = 1024;
   return e[0] = e[32] = 0, e;
 }
-function y_() {
-  let e = m_(), t = g_(), n = b_();
+function S_() {
+  let e = w_(), t = k_(), n = I_();
   return (s) => {
     let r = new ArrayBuffer(4 * s.length), a = new Uint32Array(r);
     for (let i = 0; i < s.length; i++) {
@@ -5346,15 +5346,15 @@ var xt = class {
     }), s;
   }
 };
-var v_ = (e) => xt.registerSaveRouter(e);
-var x_ = (e) => xt.registerLoadRouter(e);
-var w_ = (e) => xt.getSaveHandlers(e);
-var k_ = (e, t) => xt.getLoadHandlers(e, t);
-var rm = "tensorflowjs";
-var am = 1;
-var Yr = "models_store";
-var or = "model_info_store";
-function mk() {
+var C_ = (e) => xt.registerSaveRouter(e);
+var N_ = (e) => xt.registerLoadRouter(e);
+var T_ = (e) => xt.getSaveHandlers(e);
+var $_ = (e, t) => xt.getLoadHandlers(e, t);
+var am = "tensorflowjs";
+var im = 1;
+var Qr = "models_store";
+var ur = "model_info_store";
+function vk() {
   if (!X().getBool("IS_BROWSER"))
     throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
   let e = typeof window == "undefined" ? self : window, t = e.indexedDB || e.mozIndexedDB || e.webkitIndexedDB || e.msIndexedDB || e.shimIndexedDB;
@@ -5362,13 +5362,13 @@ function mk() {
     throw new Error("The current browser does not appear to support IndexedDB.");
   return t;
 }
-function im(e) {
+function om(e) {
   let t = e.result;
-  t.createObjectStore(Yr, { keyPath: "modelPath" }), t.createObjectStore(or, { keyPath: "modelPath" });
+  t.createObjectStore(Qr, { keyPath: "modelPath" }), t.createObjectStore(ur, { keyPath: "modelPath" });
 }
-var ua = class {
+var la = class {
   constructor(e) {
-    if (this.indexedDB = mk(), e == null || !e)
+    if (this.indexedDB = vk(), e == null || !e)
       throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
     this.modelPath = e;
   }
@@ -5382,23 +5382,23 @@ var ua = class {
   }
   databaseAction(e, t) {
     return new Promise((n, s) => {
-      let r = this.indexedDB.open(rm, am);
-      r.onupgradeneeded = () => im(r), r.onsuccess = () => {
+      let r = this.indexedDB.open(am, im);
+      r.onupgradeneeded = () => om(r), r.onsuccess = () => {
         let a = r.result;
         if (t == null) {
-          let i = a.transaction(Yr, "readonly"), u = i.objectStore(Yr).get(this.modelPath);
+          let i = a.transaction(Qr, "readonly"), u = i.objectStore(Qr).get(this.modelPath);
           u.onsuccess = () => {
             if (u.result == null)
               return a.close(), s(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
             n(u.result.modelArtifacts);
           }, u.onerror = (l) => (a.close(), s(u.error)), i.oncomplete = () => a.close();
         } else {
-          let i = Fl(t), o = a.transaction(or, "readwrite"), u = o.objectStore(or), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
+          let i = Fl(t), o = a.transaction(ur, "readwrite"), u = o.objectStore(ur), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
           l.onsuccess = () => {
-            c = a.transaction(Yr, "readwrite");
-            let d = c.objectStore(Yr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i });
+            c = a.transaction(Qr, "readwrite");
+            let d = c.objectStore(Qr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i });
             d.onsuccess = () => n({ modelArtifactsInfo: i }), d.onerror = (h) => {
-              u = o.objectStore(or);
+              u = o.objectStore(ur);
               let f = u.delete(this.modelPath);
               f.onsuccess = () => (a.close(), s(d.error)), f.onerror = (m) => (a.close(), s(d.error));
             };
@@ -5410,25 +5410,25 @@ var ua = class {
     });
   }
 };
-ua.URL_SCHEME = "indexeddb://";
-var gk = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ua.URL_SCHEME) ? I_(e.slice(ua.URL_SCHEME.length)) : null;
-xt.registerSaveRouter(gk);
-xt.registerLoadRouter(gk);
-function I_(e) {
-  return new ua(e);
+la.URL_SCHEME = "indexeddb://";
+var xk = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(la.URL_SCHEME) ? __(e.slice(la.URL_SCHEME.length)) : null;
+xt.registerSaveRouter(xk);
+xt.registerLoadRouter(xk);
+function __(e) {
+  return new la(e);
 }
-function S_(e) {
-  return e.startsWith(ua.URL_SCHEME) ? e.slice(ua.URL_SCHEME.length) : e;
+function A_(e) {
+  return e.startsWith(la.URL_SCHEME) ? e.slice(la.URL_SCHEME.length) : e;
 }
-var C_ = class {
+var E_ = class {
   constructor() {
-    this.indexedDB = mk();
+    this.indexedDB = vk();
   }
   async listModels() {
     return new Promise((e, t) => {
-      let n = this.indexedDB.open(rm, am);
-      n.onupgradeneeded = () => im(n), n.onsuccess = () => {
-        let s = n.result, r = s.transaction(or, "readonly"), i = r.objectStore(or).getAll();
+      let n = this.indexedDB.open(am, im);
+      n.onupgradeneeded = () => om(n), n.onsuccess = () => {
+        let s = n.result, r = s.transaction(ur, "readonly"), i = r.objectStore(ur).getAll();
         i.onsuccess = () => {
           let o = {};
           for (let u of i.result)
@@ -5439,17 +5439,17 @@ var C_ = class {
     });
   }
   async removeModel(e) {
-    return e = S_(e), new Promise((t, n) => {
-      let s = this.indexedDB.open(rm, am);
-      s.onupgradeneeded = () => im(s), s.onsuccess = () => {
-        let r = s.result, a = r.transaction(or, "readwrite"), i = a.objectStore(or), o = i.get(e), u;
+    return e = A_(e), new Promise((t, n) => {
+      let s = this.indexedDB.open(am, im);
+      s.onupgradeneeded = () => om(s), s.onsuccess = () => {
+        let r = s.result, a = r.transaction(ur, "readwrite"), i = a.objectStore(ur), o = i.get(e), u;
         o.onsuccess = () => {
           if (o.result == null)
             return r.close(), n(new Error(`Cannot find model with path '${e}' in IndexedDB.`));
           {
             let l = i.delete(e), c = () => {
-              u = r.transaction(Yr, "readwrite");
-              let d = u.objectStore(Yr).delete(e);
+              u = r.transaction(Qr, "readwrite");
+              let d = u.objectStore(Qr).delete(e);
               d.onsuccess = () => t(o.result.modelArtifactsInfo), d.onerror = (h) => n(o.error);
             };
             l.onsuccess = c, l.onerror = (p) => (c(), r.close(), n(o.error));
@@ -5462,35 +5462,35 @@ var C_ = class {
   }
 };
 var Gs = "/";
-var Ui = "tensorflowjs_models";
-var bk = "info";
-var N_ = "model_topology";
-var T_ = "weight_specs";
-var $_ = "weight_data";
-var __ = "model_metadata";
-function yk(e) {
-  return { info: [Ui, e, bk].join(Gs), topology: [Ui, e, N_].join(Gs), weightSpecs: [Ui, e, T_].join(Gs), weightData: [Ui, e, $_].join(Gs), modelMetadata: [Ui, e, __].join(Gs) };
+var Hi = "tensorflowjs_models";
+var wk = "info";
+var R_ = "model_topology";
+var D_ = "weight_specs";
+var F_ = "weight_data";
+var O_ = "model_metadata";
+function kk(e) {
+  return { info: [Hi, e, wk].join(Gs), topology: [Hi, e, R_].join(Gs), weightSpecs: [Hi, e, D_].join(Gs), weightData: [Hi, e, F_].join(Gs), modelMetadata: [Hi, e, O_].join(Gs) };
 }
-function vk(e) {
+function Ik(e) {
   for (let t of Object.values(e))
     window.localStorage.removeItem(t);
 }
-function A_(e) {
+function P_(e) {
   let t = e.split(Gs);
   if (t.length < 3)
     throw new Error(`Invalid key format: ${e}`);
   return t.slice(1, t.length - 1).join(Gs);
 }
-function E_(e) {
-  return e.startsWith(la.URL_SCHEME) ? e.slice(la.URL_SCHEME.length) : e;
+function z_(e) {
+  return e.startsWith(ca.URL_SCHEME) ? e.slice(ca.URL_SCHEME.length) : e;
 }
-var la = class {
+var ca = class {
   constructor(e) {
     if (!X().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined")
       throw new Error("The current environment does not support local storage.");
     if (this.LS = window.localStorage, e == null || !e)
       throw new Error("For local storage, modelPath must not be null, undefined or empty.");
-    this.modelPath = e, this.keys = yk(this.modelPath);
+    this.modelPath = e, this.keys = kk(this.modelPath);
   }
   async save(e) {
     if (e.modelTopology instanceof ArrayBuffer)
@@ -5498,11 +5498,11 @@ var la = class {
     {
       let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Fl(e);
       try {
-        this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, h_(e.weightData));
+        this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, v_(e.weightData));
         let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
         return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(r)), { modelArtifactsInfo: s };
       } catch (r) {
-        throw vk(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`);
+        throw Ik(this.keys), new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${s.modelTopologyBytes}, weightSpecsBytes=${s.weightSpecsBytes}, weightDataBytes=${s.weightDataBytes}.`);
       }
     }
   }
@@ -5528,41 +5528,41 @@ var la = class {
     let a = this.LS.getItem(this.keys.weightData);
     if (a == null)
       throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
-    return t.weightData = f_(a), t;
+    return t.weightData = x_(a), t;
   }
 };
-la.URL_SCHEME = "localstorage://";
-var xk = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(la.URL_SCHEME) ? R_(e.slice(la.URL_SCHEME.length)) : null;
-xt.registerSaveRouter(xk);
-xt.registerLoadRouter(xk);
-function R_(e) {
-  return new la(e);
+ca.URL_SCHEME = "localstorage://";
+var Sk = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ca.URL_SCHEME) ? M_(e.slice(ca.URL_SCHEME.length)) : null;
+xt.registerSaveRouter(Sk);
+xt.registerLoadRouter(Sk);
+function M_(e) {
+  return new ca(e);
 }
-var D_ = class {
+var L_ = class {
   constructor() {
     O(X().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), O(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
   }
   async listModels() {
-    let e = {}, t = Ui + Gs, n = Gs + bk;
+    let e = {}, t = Hi + Gs, n = Gs + wk;
     for (let s = 0; s < this.LS.length; ++s) {
       let r = this.LS.key(s);
       if (r.startsWith(t) && r.endsWith(n)) {
-        let a = A_(r);
+        let a = P_(r);
         e[a] = JSON.parse(this.LS.getItem(r));
       }
     }
     return e;
   }
   async removeModel(e) {
-    e = E_(e);
-    let t = yk(e);
+    e = z_(e);
+    let t = kk(e);
     if (this.LS.getItem(t.info) == null)
       throw new Error(`Cannot find model at path '${e}'`);
     let n = JSON.parse(this.LS.getItem(t.info));
-    return vk(t), n;
+    return Ik(t), n;
   }
 };
-var qi = "://";
+var Ki = "://";
 var zn = class {
   constructor() {
     this.managers = {};
@@ -5571,7 +5571,7 @@ var zn = class {
     return zn.instance == null && (zn.instance = new zn()), zn.instance;
   }
   static registerManager(e, t) {
-    O(e != null, () => "scheme must not be undefined or null."), e.endsWith(qi) && (e = e.slice(0, e.indexOf(qi))), O(e.length > 0, () => "scheme must not be an empty string.");
+    O(e != null, () => "scheme must not be undefined or null."), e.endsWith(Ki) && (e = e.slice(0, e.indexOf(Ki))), O(e.length > 0, () => "scheme must not be an empty string.");
     let n = zn.getInstance();
     O(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t;
   }
@@ -5586,11 +5586,11 @@ var zn = class {
   }
 };
 function td(e) {
-  if (e.indexOf(qi) === -1)
+  if (e.indexOf(Ki) === -1)
     throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${zn.getSchemes().join(",")}`);
-  return { scheme: e.split(qi)[0], path: e.split(qi)[1] };
+  return { scheme: e.split(Ki)[0], path: e.split(Ki)[1] };
 }
-async function wk(e, t, n = false) {
+async function Ck(e, t, n = false) {
   O(e !== t, () => `Old path and new path are the same: '${e}'`);
   let s = xt.getLoadHandlers(e);
   O(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), O(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);
@@ -5601,28 +5601,28 @@ async function wk(e, t, n = false) {
   let p = await i.save(c);
   return n && !l && await zn.getManager(o).removeModel(u), p.modelArtifactsInfo;
 }
-async function F_() {
+async function B_() {
   let e = zn.getSchemes(), t = {};
   for (let n of e) {
     let s = await zn.getManager(n).listModels();
     for (let r in s) {
-      let a = n + qi + r;
+      let a = n + Ki + r;
       t[a] = s[r];
     }
   }
   return t;
 }
-async function O_(e) {
+async function V_(e) {
   let t = td(e);
   return zn.getManager(t.scheme).removeModel(t.path);
 }
-async function P_(e, t) {
-  return wk(e, t, false);
+async function W_(e, t) {
+  return Ck(e, t, false);
 }
-async function z_(e, t) {
-  return wk(e, t, true);
+async function U_(e, t) {
+  return Ck(e, t, true);
 }
-var M_ = class {
+var G_ = class {
   fetch(e, t) {
     return fetch(e, t);
   }
@@ -5639,24 +5639,24 @@ var M_ = class {
   }
 };
 if (X().get("IS_BROWSER")) {
-  X().setPlatform("browser", new M_());
+  X().setPlatform("browser", new G_());
   try {
-    zn.registerManager(la.URL_SCHEME, new D_());
+    zn.registerManager(ca.URL_SCHEME, new L_());
   } catch (e) {
   }
   try {
-    zn.registerManager(ua.URL_SCHEME, new C_());
+    zn.registerManager(la.URL_SCHEME, new E_());
   } catch (e) {
   }
 }
-var L_ = { importFetch: () => qT() };
-var Pf;
-var B_ = class {
+var H_ = { importFetch: () => ZT() };
+var Mf;
+var q_ = class {
   constructor() {
-    this.util = jT(), this.textEncoder = new this.util.TextEncoder();
+    this.util = JT(), this.textEncoder = new this.util.TextEncoder();
   }
   fetch(e, t) {
-    return X().global.fetch != null ? X().global.fetch(e, t) : (Pf == null && (Pf = L_.importFetch()), Pf(e, t));
+    return X().global.fetch != null ? X().global.fetch(e, t) : (Mf == null && (Mf = H_.importFetch()), Mf(e, t));
   }
   now() {
     let e = process.hrtime();
@@ -5671,44 +5671,44 @@ var B_ = class {
     return e.length === 0 ? "" : new this.util.TextDecoder(t).decode(e);
   }
 };
-X().get("IS_NODE") && !X().get("IS_BROWSER") && X().setPlatform("node", new B_());
+X().get("IS_NODE") && !X().get("IS_BROWSER") && X().setPlatform("node", new q_());
 function De(e, t = "float32", n) {
-  return t = t || "float32", sg(e), new Vt(e, t, n);
+  return t = t || "float32", ag(e), new Vt(e, t, n);
 }
-function V_(e, t) {
+function j_(e, t) {
   let n = _(e, "x", "cast");
-  if (!Kw(t))
+  if (!Zw(t))
     throw new Error(`Failed to cast to unknown dtype ${t}`);
   if (t === "string" && n.dtype !== "string" || t !== "string" && n.dtype === "string")
     throw new Error("Only strings can be casted to strings");
   let s = { x: n }, r = { dtype: t };
-  return M.runKernel(Na, s, r);
+  return M.runKernel(Ta, s, r);
 }
-var ce = L({ cast_: V_ });
-function W_(e) {
+var ce = L({ cast_: j_ });
+function K_(e) {
   let n = { x: _(e, "x", "clone", "string_or_numeric") };
-  return M.runKernel(Ba, n);
+  return M.runKernel(Wa, n);
 }
-var lr = L({ clone_: W_ });
-function U_(e, t = false) {
+var cr = L({ clone_: K_ });
+function X_(e, t = false) {
   console.log(e.toString(t));
 }
-ck();
-var G_ = { buffer: De, cast: ce, clone: lr, print: U_ };
-Q$(G_);
+fk();
+var Y_ = { buffer: De, cast: ce, clone: cr, print: X_ };
+s_(Y_);
 var En = {};
-Ae(En, { browserFiles: () => Q_, browserHTTPRequest: () => nA, concatenateArrayBuffers: () => Og, copyModel: () => P_, decodeWeights: () => hk, encodeWeights: () => d_, fromMemory: () => rA, getLoadHandlers: () => k_, getModelArtifactsForJSON: () => Pg, getModelArtifactsInfoForJSON: () => Fl, getSaveHandlers: () => w_, http: () => Mg, isHTTPScheme: () => um, listModels: () => F_, loadWeights: () => Z_, moveModel: () => z_, registerLoadRouter: () => x_, registerSaveRouter: () => v_, removeModel: () => O_, weightsLoaderFactory: () => Ik, withSaveHandler: () => aA });
-var H_ = "model";
-var q_ = ".json";
-var j_ = ".weights.bin";
-function cx(e) {
+Ae(En, { browserFiles: () => sA, browserHTTPRequest: () => uA, concatenateArrayBuffers: () => zg, copyModel: () => W_, decodeWeights: () => bk, encodeWeights: () => b_, fromMemory: () => cA, getLoadHandlers: () => $_, getModelArtifactsForJSON: () => Mg, getModelArtifactsInfoForJSON: () => Fl, getSaveHandlers: () => T_, http: () => Bg, isHTTPScheme: () => lm, listModels: () => B_, loadWeights: () => rA, moveModel: () => U_, registerLoadRouter: () => N_, registerSaveRouter: () => C_, removeModel: () => V_, weightsLoaderFactory: () => Tk, withSaveHandler: () => dA });
+var Q_ = "model";
+var Z_ = ".json";
+var J_ = ".weights.bin";
+function px(e) {
   return new Promise((t) => setTimeout(t)).then(e);
 }
-var om = class {
+var um = class {
   constructor(e) {
     if (!X().getBool("IS_BROWSER"))
       throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
-    e.startsWith(om.URL_SCHEME) && (e = e.slice(om.URL_SCHEME.length)), (e == null || e.length === 0) && (e = H_), this.modelJsonFileName = e + q_, this.weightDataFileName = e + j_;
+    e.startsWith(um.URL_SCHEME) && (e = e.slice(um.URL_SCHEME.length)), (e == null || e.length === 0) && (e = Q_), this.modelJsonFileName = e + Z_, this.weightDataFileName = e + J_;
   }
   async save(e) {
     if (typeof document == "undefined")
@@ -5717,18 +5717,18 @@ var om = class {
     if (e.modelTopology instanceof ArrayBuffer)
       throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
     {
-      let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = fk(e, n), r = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
-      if (a.download = this.modelJsonFileName, a.href = r, await cx(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
+      let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = yk(e, n), r = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
+      if (a.download = this.modelJsonFileName, a.href = r, await px(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
         let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
-        i.download = this.weightDataFileName, i.href = t, await cx(() => i.dispatchEvent(new MouseEvent("click")));
+        i.download = this.weightDataFileName, i.href = t, await px(() => i.dispatchEvent(new MouseEvent("click")));
       }
       return { modelArtifactsInfo: Fl(e) };
     }
   }
 };
-var yd = om;
+var yd = um;
 yd.URL_SCHEME = "downloads://";
-var K_ = class {
+var eA = class {
   constructor(e) {
     if (e == null || e.length < 1)
       throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);
@@ -5751,7 +5751,7 @@ var K_ = class {
           e({ modelTopology: a });
           return;
         }
-        let o = Pg(r, (u) => this.loadWeights(u));
+        let o = Mg(r, (u) => this.loadWeights(u));
         e(o);
       }, n.onerror = (s) => t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), n.readAsText(this.jsonFile);
     });
@@ -5761,7 +5761,7 @@ var K_ = class {
     for (let a of e)
       t.push(...a.weights), n.push(...a.paths);
     let s = this.checkManifestAndWeightFiles(e), r = n.map((a) => this.loadWeightsFile(a, s[a]));
-    return Promise.all(r).then((a) => [t, Og(a)]);
+    return Promise.all(r).then((a) => [t, zg(a)]);
   }
   loadWeightsFile(e, t) {
     return new Promise((n, s) => {
@@ -5773,10 +5773,10 @@ var K_ = class {
     });
   }
   checkManifestAndWeightFiles(e) {
-    let t = [], n = this.weightsFiles.map((r) => lx(r.name)), s = {};
+    let t = [], n = this.weightsFiles.map((r) => dx(r.name)), s = {};
     for (let r of e)
       r.paths.forEach((a) => {
-        let i = lx(a);
+        let i = dx(a);
         if (t.indexOf(i) !== -1)
           throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);
         if (t.push(i), n.indexOf(i) === -1)
@@ -5788,15 +5788,15 @@ var K_ = class {
     return s;
   }
 };
-var X_ = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(yd.URL_SCHEME) ? Y_(e.slice(yd.URL_SCHEME.length)) : null;
-xt.registerSaveRouter(X_);
-function Y_(e = "model") {
+var tA = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(yd.URL_SCHEME) ? nA(e.slice(yd.URL_SCHEME.length)) : null;
+xt.registerSaveRouter(tA);
+function nA(e = "model") {
   return new yd(e);
 }
-function Q_(e) {
-  return new K_(e);
+function sA(e) {
+  return new eA(e);
 }
-function dx(e, t, n, s) {
+function hx(e, t, n, s) {
   i(e), n = n == null ? 0 : n, s = s == null ? 1 : s, o(n, s);
   let r = 0, a = (u) => (u.then((l) => {
     let c = n + ++r / e.length * (s - n);
@@ -5810,21 +5810,21 @@ function dx(e, t, n, s) {
   }
   return Promise.all(e.map(a));
 }
-async function kk(e, t) {
+async function Nk(e, t) {
   t == null && (t = {});
-  let n = t.fetchFunc == null ? X().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, o = (t.onProgress == null ? await Promise.all(s) : await dx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
-  return t.onProgress == null ? await Promise.all(o) : await dx(o, t.onProgress, u, l);
+  let n = t.fetchFunc == null ? X().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, o = (t.onProgress == null ? await Promise.all(s) : await hx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
+  return t.onProgress == null ? await Promise.all(o) : await hx(o, t.onProgress, u, l);
 }
-async function Z_(e, t = "", n, s) {
-  return Ik((i) => kk(i, { requestInit: s }))(e, t, n);
+async function rA(e, t = "", n, s) {
+  return Tk((i) => Nk(i, { requestInit: s }))(e, t, n);
 }
-function Ik(e) {
+function Tk(e) {
   return async (t, n = "", s) => {
     let r = t.map(() => false), a = {}, i = s != null ? s.map(() => false) : [], o = [];
     if (t.forEach((h, f) => {
       let m = 0;
       h.weights.forEach((g) => {
-        let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = sm[b] * pt(g.shape), v = () => {
+        let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = rm[b] * pt(g.shape), v = () => {
           r[f] = true, a[f] == null && (a[f] = []), a[f].push({ manifestEntry: g, groupOffset: m, sizeBytes: y });
         };
         s != null ? s.forEach((x, k) => {
@@ -5854,16 +5854,16 @@ Manifest JSON has weights with names: ${o.join(", ")}.`);
         b.set(k, y), y += k.byteLength;
       }
       a[h].forEach((x) => {
-        let k = g.slice(x.groupOffset, x.groupOffset + x.sizeBytes), T = hk(k, [x.manifestEntry]);
+        let k = g.slice(x.groupOffset, x.groupOffset + x.sizeBytes), T = bk(k, [x.manifestEntry]);
         for (let N in T)
           p[N] = T[N];
       }), d += f;
     }), p;
   };
 }
-var J_ = "application/octet-stream";
-var eA = "application/json";
-var zg = class {
+var aA = "application/octet-stream";
+var iA = "application/json";
+var Lg = class {
   constructor(e, t) {
     if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (O(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = X().platform.fetch, O(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && O(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null)
       throw new Error("requestInit is expected to have no pre-existing body, but has one.");
@@ -5874,8 +5874,8 @@ var zg = class {
       throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
     let t = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
     t.body = new FormData();
-    let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = fk(e, n);
-    t.body.append("model.json", new Blob([JSON.stringify(s)], { type: eA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: J_ }), "model.weights.bin");
+    let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = yk(e, n);
+    t.body.append("model.json", new Blob([JSON.stringify(s)], { type: iA }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: aA }), "model.weights.bin");
     let r = await this.fetch(this.path, t);
     if (r.ok)
       return { modelArtifactsInfo: Fl(e), responses: [r] };
@@ -5895,10 +5895,10 @@ var zg = class {
     let n = t.modelTopology, s = t.weightsManifest;
     if (n == null && s == null)
       throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
-    return Pg(t, (r) => this.loadWeights(r));
+    return Mg(t, (r) => this.loadWeights(r));
   }
   async loadWeights(e) {
-    let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = tA(t), r = this.weightPathPrefix || n, a = [];
+    let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = oA(t), r = this.weightPathPrefix || n, a = [];
     for (let l of e)
       a.push(...l.weights);
     let i = [], o = [];
@@ -5906,37 +5906,37 @@ var zg = class {
       for (let c of l.paths)
         this.weightUrlConverter != null ? o.push(this.weightUrlConverter(c)) : i.push(r + c + s);
     this.weightUrlConverter && i.push(...await Promise.all(o));
-    let u = await kk(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
-    return [a, Og(u)];
+    let u = await Nk(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
+    return [a, zg(u)];
   }
 };
-zg.URL_SCHEME_REGEX = /^https?:\/\//;
-function tA(e) {
+Lg.URL_SCHEME_REGEX = /^https?:\/\//;
+function oA(e) {
   let t = e.lastIndexOf("/"), n = e.lastIndexOf("?"), s = e.substring(0, t), r = n > t ? e.substring(n) : "";
   return [s + "/", r];
 }
-function um(e) {
-  return e.match(zg.URL_SCHEME_REGEX) != null;
+function lm(e) {
+  return e.match(Lg.URL_SCHEME_REGEX) != null;
 }
-var Sk = (e, t) => {
+var $k = (e, t) => {
   if (typeof fetch == "undefined" && (t == null || t.fetchFunc == null))
     return null;
   {
     let n = true;
-    if (Array.isArray(e) ? n = e.every((s) => um(s)) : n = um(e), n)
-      return Mg(e, t);
+    if (Array.isArray(e) ? n = e.every((s) => lm(s)) : n = lm(e), n)
+      return Bg(e, t);
   }
   return null;
 };
-xt.registerSaveRouter(Sk);
-xt.registerLoadRouter(Sk);
-function Mg(e, t) {
-  return new zg(e, t);
+xt.registerSaveRouter($k);
+xt.registerLoadRouter($k);
+function Bg(e, t) {
+  return new Lg(e, t);
 }
-function nA(e, t) {
-  return Mg(e, t);
+function uA(e, t) {
+  return Bg(e, t);
 }
-var zf = class {
+var Lf = class {
   constructor(e) {
     this.modelArtifacts = e;
   }
@@ -5944,7 +5944,7 @@ var zf = class {
     return this.modelArtifacts;
   }
 };
-var sA = class {
+var lA = class {
   constructor(e) {
     this.saveHandler = e;
   }
@@ -5952,48 +5952,48 @@ var sA = class {
     return this.saveHandler(e);
   }
 };
-function rA(e, t, n, s) {
-  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new zf(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new zf({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new zf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
+function cA(e, t, n, s) {
+  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Lf(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Lf({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Lf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
 }
-function aA(e) {
-  return new sA(e);
+function dA(e) {
+  return new lA(e);
 }
-var iA = {};
-Ae(iA, { confusionMatrix: () => dA });
-function oA(e, t, n = false, s = false) {
+var pA = {};
+Ae(pA, { confusionMatrix: () => bA });
+function hA(e, t, n = false, s = false) {
   let r = _(e, "a", "matMul"), a = _(t, "b", "matMul");
   [r, a] = vt(r, a);
   let i = { a: r, b: a }, o = { transposeA: n, transposeB: s };
-  return M.runKernel(Ca, i, o);
+  return M.runKernel(Na, i, o);
 }
-var We = L({ matMul_: oA });
-function uA(e, t, n = 1, s = 0) {
+var We = L({ matMul_: hA });
+function fA(e, t, n = 1, s = 0) {
   if (t < 2)
     throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);
   let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s };
-  return M.runKernel($o, a, i);
+  return M.runKernel(Ao, a, i);
 }
-var vd = L({ oneHot_: uA });
-function lA(e, t) {
+var vd = L({ oneHot_: fA });
+function mA(e, t) {
   let n = _(e, "x", "transpose");
   if (t == null && (t = n.shape.map((a, i) => i).reverse()), O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`), t.forEach((a) => {
     O(a >= 0 && a < n.rank, () => `All entries in 'perm' must be between 0 and ${n.rank - 1} but got ${t}`);
   }), n.rank <= 1)
     return n.clone();
   let s = { x: n }, r = { perm: t };
-  return M.runKernel(pi, s, r);
+  return M.runKernel(mi, s, r);
 }
-var qe = L({ transpose_: lA });
-function cA(e, t, n) {
+var Ge = L({ transpose_: mA });
+function gA(e, t, n) {
   let s = _(e, "labels", "confusionMatrix"), r = _(t, "predictions", "confusionMatrix");
   O(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), O(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), O(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), O(s.shape[0] === r.shape[0], () => `Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`), O(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`);
-  let a = vd(ce(s, "int32"), n), i = vd(ce(r, "int32"), n), o = qe(a), u = We(o, i);
+  let a = vd(ce(s, "int32"), n), i = vd(ce(r, "int32"), n), o = Ge(a), u = We(o, i);
   return ce(u, "int32");
 }
-var dA = L({ confusionMatrix_: cA });
-var jo = {};
-Ae(jo, { assertAndGetBroadcastShape: () => ot, getBroadcastDims: () => Ck, getReductionAxes: () => _t });
-function Ck(e, t) {
+var bA = L({ confusionMatrix_: gA });
+var Ko = {};
+Ae(Ko, { assertAndGetBroadcastShape: () => ot, getBroadcastDims: () => _k, getReductionAxes: () => _t });
+function _k(e, t) {
   let n = e.length, s = [];
   for (let r = 0; r < n; r++) {
     let a = n - 1 - r, i = e[a] || 1;
@@ -6027,20 +6027,20 @@ function ot(e, t) {
   }
   return n;
 }
-var Nk = {};
-Ae(Nk, { fromPixels: () => vA, fromPixelsAsync: () => bA, toPixels: () => yA });
-function pA(e, t, n) {
-  if (wa(e), t != null && t.length !== 3)
+var Ak = {};
+Ae(Ak, { fromPixels: () => CA, fromPixelsAsync: () => IA, toPixels: () => SA });
+function yA(e, t, n) {
+  if (ka(e), t != null && t.length !== 3)
     throw new Error("tensor3d() requires shape to have three numbers");
   let s = Rs(e, n);
   if (s.length !== 3 && s.length !== 1)
     throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
-  return Nr(e, t, s, n);
+  return Tr(e, t, s, n);
 }
-var Vr;
-function Tk(e, t = 3) {
+var Wr;
+function Ek(e, t = 3) {
   if (t > 4)
     throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
   if (e == null)
@@ -6062,7 +6062,7 @@ function Tk(e, t = 3) {
     throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);
   if (r && r && e.readyState < 2)
     throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
-  if (Zf(fd, M.backendName) != null) {
+  if (Jf(fd, M.backendName) != null) {
     let f = { pixels: e }, m = { numChannels: t };
     return M.runKernel(fd, f, m);
   }
@@ -6072,15 +6072,15 @@ function Tk(e, t = 3) {
   else if (s || n)
     p = e.data;
   else if (a || r || o) {
-    if (Vr == null)
+    if (Wr == null)
       if (typeof document == "undefined")
         if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
-          Vr = new OffscreenCanvas(1, 1).getContext("2d");
+          Wr = new OffscreenCanvas(1, 1).getContext("2d");
         else
           throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
       else
-        Vr = document.createElement("canvas").getContext("2d");
-    Vr.canvas.width = l, Vr.canvas.height = c, Vr.drawImage(e, 0, 0, l, c), p = Vr.getImageData(0, 0, l, c).data;
+        Wr = document.createElement("canvas").getContext("2d");
+    Wr.canvas.width = l, Wr.canvas.height = c, Wr.drawImage(e, 0, 0, l, c), p = Wr.getImageData(0, 0, l, c).data;
   }
   let d;
   if (t === 4)
@@ -6092,23 +6092,23 @@ function Tk(e, t = 3) {
       for (let g = 0; g < t; ++g)
         d[m * t + g] = p[m * 4 + g];
   }
-  return pA(d, [c, l, t], "int32");
+  return yA(d, [c, l, t], "int32");
 }
-function hA(e) {
+function vA(e) {
   return e != null && e.data instanceof Uint8Array;
 }
-function fA() {
+function xA() {
   return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
 }
-function mA(e) {
+function wA(e) {
   return e != null && e.width !== 0 && e.height !== 0;
 }
-function gA(e) {
-  return fA() && !(e instanceof ImageBitmap) && mA(e) && !hA(e);
+function kA(e) {
+  return xA() && !(e instanceof ImageBitmap) && wA(e) && !vA(e);
 }
-async function bA(e, t = 3) {
+async function IA(e, t = 3) {
   let n = null;
-  if (X().getBool("WRAP_TO_IMAGEBITMAP") && gA(e)) {
+  if (X().getBool("WRAP_TO_IMAGEBITMAP") && kA(e)) {
     let s;
     try {
       s = await createImageBitmap(e, { premultiplyAlpha: "none" });
@@ -6118,9 +6118,9 @@ async function bA(e, t = 3) {
     s != null && s.width === e.width && s.height === e.height ? n = s : n = e;
   } else
     n = e;
-  return Tk(n, t);
+  return Ek(n, t);
 }
-async function yA(e, t) {
+async function SA(e, t) {
   let n = _(e, "img", "toPixels");
   if (!(e instanceof et)) {
     let l = n;
@@ -6155,10 +6155,10 @@ async function yA(e, t) {
   }
   return n !== e && n.dispose(), u;
 }
-var vA = L({ fromPixels_: Tk });
-var $k = {};
-Ae($k, { prepareAndValidate: () => _k });
-function _k(e, t) {
+var CA = L({ fromPixels_: Ek });
+var Rk = {};
+Ae(Rk, { prepareAndValidate: () => Dk });
+function Dk(e, t) {
   let n = e.shape.length, s = t.shape.length;
   if (n < 1)
     throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${n}.`);
@@ -6178,12 +6178,12 @@ function _k(e, t) {
   let l = 1;
   for (let p = a; p < n; ++p)
     l *= o[p], u.push(o[p]);
-  let c = [...ao(e.shape).map((p) => p / l), 1].slice(0, a);
+  let c = [...oo(e.shape).map((p) => p / l), 1].slice(0, a);
   return [u, i, l, c];
 }
-var Ak = {};
-Ae(Ak, { calculateShapes: () => Ek, validateInput: () => Bg, validateUpdateShape: () => Lg });
-function Lg(e, t, n) {
+var Fk = {};
+Ae(Fk, { calculateShapes: () => Ok, validateInput: () => Wg, validateUpdateShape: () => Vg });
+function Vg(e, t, n) {
   let s = t.rank > 1 ? t.shape[t.rank - 1] : 1, r = t.rank > 1 ? t.rank - 1 : 1, a = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${r}.`;
   if (n.rank < r)
     throw new Error(a + ` update.rank < ${r}. `);
@@ -6198,7 +6198,7 @@ function Lg(e, t, n) {
     if (n.shape[i + r] !== e[i + s])
       throw new Error(a + ` updates.shape[${i + r}] (${n.shape[i + r]}) != shape[${i + r}] (${e[i + r]})`);
 }
-function Bg(e, t, n) {
+function Wg(e, t, n) {
   if (t.rank < 1)
     throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);
   if (e.rank < 1)
@@ -6213,38 +6213,38 @@ function Bg(e, t, n) {
     if (e.size === 0)
       throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`);
   }
-  Lg(n, t, e);
+  Vg(n, t, e);
 }
-function Ek(e, t, n) {
+function Ok(e, t, n) {
   let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, i = 1;
   for (let p = r; p < a; ++p)
     i *= n[p];
-  let o = r < 1 ? 1 : r, u = pt(t.shape) / o, l = [...ao(n.slice(0, r)), 1], c = pt(n);
+  let o = r < 1 ? 1 : r, u = pt(t.shape) / o, l = [...oo(n.slice(0, r)), 1], c = pt(n);
   return { sliceRank: r, numUpdates: u, sliceSize: i, strides: l, outputSize: c };
 }
 var wt = {};
-Ae(wt, { assertParamsValid: () => wA, computeFlatOffset: () => NA, computeOutShape: () => IA, getNormalizedAxes: () => SA, isSliceContinous: () => CA, maskToAxes: () => kA, parseSliceParams: () => Bk, sliceInfo: () => TA, startForAxis: () => Mk, startIndicesWithElidedDims: () => Ok, stopForAxis: () => Lk, stopIndicesWithElidedDims: () => Pk, stridesForAxis: () => zk, stridesWithElidedDims: () => Rk });
-var lm = -2;
-var xA = -1;
-function wA(e, t, n) {
+Ae(wt, { assertParamsValid: () => TA, computeFlatOffset: () => RA, computeOutShape: () => _A, getNormalizedAxes: () => AA, isSliceContinous: () => EA, maskToAxes: () => $A, parseSliceParams: () => Gk, sliceInfo: () => DA, startForAxis: () => Wk, startIndicesWithElidedDims: () => Lk, stopForAxis: () => Uk, stopIndicesWithElidedDims: () => Bk, stridesForAxis: () => Vk, stridesWithElidedDims: () => Pk });
+var cm = -2;
+var NA = -1;
+function TA(e, t, n) {
   let s = e.shape.length;
   O(s === t.length, () => `Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`), O(s === n.length, () => `Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);
   for (let r = 0; r < s; ++r)
     O(t[r] + n[r] <= e.shape[r], () => `Error in slice${s}D: begin[${r}] + size[${r}] (${t[r] + n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`);
 }
-function kA(e) {
+function $A(e) {
   let t = [], n = 0;
   for (; e > 0; )
     e & 1 && t.push(n), e /= 2, n++;
   return t;
 }
-function IA(e, t, n) {
+function _A(e, t, n) {
   let s = [];
   for (let r = 0; r < e.length; r++)
     s[r] = Math.ceil((t[r] - e[r]) / n[r]);
   return s;
 }
-function Rk(e, t, n, s) {
+function Pk(e, t, n, s) {
   let r = [...e];
   for (let a = r.length; a < s.length; a++)
     r.push(1);
@@ -6252,68 +6252,68 @@ function Rk(e, t, n, s) {
     a === 0 ? r[t] = 1 : (r.splice(t, 0, 1), r.pop());
   return r;
 }
-function Dk(e, t, n) {
+function zk(e, t, n) {
   return n <= e ? n : n - (t - 1);
 }
-function Fk(e, t) {
+function Mk(e, t) {
   let n = [];
   for (let s = 0; s < e; s++)
     n.push(t + s);
   return n;
 }
-function SA(e, t, n, s, r, a, i, o, u) {
+function AA(e, t, n, s, r, a, i, o, u) {
   let l = e.length, c = new Array(l), p = new Array(l), d = new Array(l);
   if (t.length && n > 0) {
     let h = t[0], f = n + 1;
-    c = Ok(i, h, f, s, e), p = Pk(o, h, f, r, e), d = Rk(a, h, f, e);
+    c = Lk(i, h, f, s, e), p = Bk(o, h, f, r, e), d = Pk(a, h, f, e);
   } else
     for (let h = 0; h < l; h++)
-      c[h] = Mk(i, s, a, e, h, u), p[h] = Lk(o, r, a, e, h, u), d[h] = zk(a, h, u);
+      c[h] = Wk(i, s, a, e, h, u), p[h] = Uk(o, r, a, e, h, u), d[h] = Vk(a, h, u);
   return { begin: c, end: p, strides: d };
 }
-function Ok(e, t, n, s, r) {
-  let a = [...r], i = Fk(n, t);
+function Lk(e, t, n, s, r) {
+  let a = [...r], i = Mk(n, t);
   for (let o = 0; o < a.length; o++)
     if (i.indexOf(o) > -1)
       a[o] = 0;
     else {
-      let u = Dk(t, n, o), l = s[u];
+      let u = zk(t, n, o), l = s[u];
       e & 1 << u && (l = 0), a[o] = l;
     }
   return a;
 }
-function Pk(e, t, n, s, r) {
-  let a = [...r], i = Fk(n, t);
+function Bk(e, t, n, s, r) {
+  let a = [...r], i = Mk(n, t);
   for (let o = 0; o < a.length; o++)
     if (i.indexOf(o) > -1)
       a[o] = Number.MAX_SAFE_INTEGER;
     else {
-      let u = Dk(t, n, o), l = s[u];
+      let u = zk(t, n, o), l = s[u];
       e & 1 << u && (l = Number.MAX_SAFE_INTEGER), a[o] = l;
     }
   for (let o = 0; o < a.length; o++) {
     let u = r[o];
-    a[o] < 0 && (a[o] += u), a[o] = Vu(0, a[o], r[o]);
+    a[o] < 0 && (a[o] += u), a[o] = Wu(0, a[o], r[o]);
   }
   return a;
 }
-function zk(e, t, n) {
+function Vk(e, t, n) {
   let s = e[t];
   return (n & 1 << t || s == null) && (s = 1), s;
 }
-function Mk(e, t, n, s, r, a) {
+function Wk(e, t, n, s, r, a) {
   let i = t[r], o = n[r] || 1;
   (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MIN_SAFE_INTEGER : i = Number.MAX_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), i = Vu(0, i, u - 1), i;
+  return i < 0 && (i += u), i = Wu(0, i, u - 1), i;
 }
-function Lk(e, t, n, s, r, a) {
+function Uk(e, t, n, s, r, a) {
   let i = t[r], o = n[r] || 1;
   (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MAX_SAFE_INTEGER : i = Number.MIN_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), o > 0 ? i = Vu(0, i, u) : i = Vu(-1, i, u - 1), i;
+  return i < 0 && (i += u), o > 0 ? i = Wu(0, i, u) : i = Wu(-1, i, u - 1), i;
 }
-function CA(e, t, n) {
+function EA(e, t, n) {
   let s = n.length;
   for (let r = 0; r < n.length; r++)
     if (n[r] > 1) {
@@ -6325,13 +6325,13 @@ function CA(e, t, n) {
       return false;
   return true;
 }
-function NA(e, t) {
+function RA(e, t) {
   let n = e.length > 0 ? e[e.length - 1] : 1;
   for (let s = 0; s < e.length - 1; s++)
     n += e[s] * t[s];
   return n;
 }
-function Bk(e, t, n) {
+function Gk(e, t, n) {
   let s, r = e.shape.length;
   typeof t == "number" ? s = [t, ...new Array(r - 1).fill(0)] : t.length < r ? s = t.concat(new Array(r - t.length).fill(0)) : s = t.slice(), s.forEach((i) => {
     O(i !== -1, () => "slice() does not support negative begin indexing.");
@@ -6339,7 +6339,7 @@ function Bk(e, t, n) {
   let a;
   return n == null ? a = new Array(r).fill(-1) : typeof n == "number" ? a = [n, ...new Array(r - 1).fill(-1)] : n.length < r ? a = n.concat(new Array(r - n.length).fill(-1)) : a = n, a = a.map((i, o) => i >= 0 ? i : (O(i === -1, () => `Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`), e.shape[o] - s[o])), [s, a];
 }
-function TA(e, t, n, s, r, a, i, o, u) {
+function DA(e, t, n, s, r, a, i, o, u) {
   let l;
   if (s == null ? (l = new Array(t.length), l.fill(1)) : l = s, i != null && (i & i - 1) !== 0)
     throw new Error("Multiple ellipses in slice is not allowed.");
@@ -6348,7 +6348,7 @@ function TA(e, t, n, s, r, a, i, o, u) {
     c && (1 << v & o) !== 0 && p.numAddAxisAfterEllipsis++, 1 << v & i && (c = true);
   c || (p.ellipsisMask |= 1 << p.dims, p.dims++);
   let d = { dims: e.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
-  $A(p, d);
+  FA(p, d);
   let h = true, f = true, m = true, g = [], b = [];
   for (let v = 0; v < e.length; ++v) {
     if (d.strides[v] === 0)
@@ -6369,7 +6369,7 @@ function TA(e, t, n, s, r, a, i, o, u) {
         if (d.begin[v] = F, d.end[v] = d.begin[v] + 1, F < 0 || F >= k)
           throw Error(`slice index ${d.begin[v]} of dimension ${v} out of bounds.`);
       } else
-        d.begin[v] = px(d.begin[v], 0, d.strides[v], k, T, N), d.end[v] = px(d.end[v], 1, d.strides[v], k, T, N);
+        d.begin[v] = fx(d.begin[v], 0, d.strides[v], k, T, N), d.end[v] = fx(d.end[v], 1, d.strides[v], k, T, N);
       let R = d.strides[v] === 1 && d.begin[v] === 0 && d.end[v] === k;
       h = h && R, f = f && (v === 0 && d.strides[v] === 1 || R);
     } else
@@ -6383,11 +6383,11 @@ function TA(e, t, n, s, r, a, i, o, u) {
   }
   for (let v = 0; v < d.finalShapeGatherIndices.length; ++v) {
     let x = d.finalShapeGatherIndices[v];
-    x >= 0 ? b.push(g[x]) : x === lm && b.push(1);
+    x >= 0 ? b.push(g[x]) : x === cm && b.push(1);
   }
-  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== lm), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
+  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== cm), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
 }
-function $A(e, t) {
+function FA(e, t) {
   t.beginMask = 0, t.endMask = 0, t.shrinkAxisMask = 0;
   let n = 0;
   t.beginValid = e.begin != null, t.endValid = e.end != null, t.begin = new Array(t.dims), t.end = new Array(t.dims), t.strides = new Array(t.dims), t.finalShapeGatherIndices = [], t.finalShapeGatherIndicesSparse = [], t.inputShapeGatherIndicesSparse = new Array(t.dims);
@@ -6397,14 +6397,14 @@ function $A(e, t) {
       for (; n < r; n++)
         t.begin[n] = 0, t.end[n] = 0, t.strides[n] = 1, t.beginMask |= 1 << n, t.endMask |= 1 << n, t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(-1), t.inputShapeGatherIndicesSparse[n] = s;
     } else if (1 << s & e.newAxisMask)
-      t.finalShapeGatherIndices.push(lm), t.finalShapeGatherIndicesSparse.push(-1);
+      t.finalShapeGatherIndices.push(cm), t.finalShapeGatherIndicesSparse.push(-1);
     else {
       if (n === t.begin.length)
         throw Error(`Index out of range using input dim ${n}; input has only ${t.dims} dims, ${t.begin.length}.`);
-      e.begin != null && (t.begin[n] = e.begin[s]), e.end != null && (t.end[n] = e.end[s]), t.strides[n] = e.strides[s], e.beginMask & 1 << s && (t.beginMask |= 1 << n), e.endMask & 1 << s && (t.endMask |= 1 << n), e.shrinkAxisMask & 1 << s ? (t.finalShapeGatherIndices.push(xA), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
+      e.begin != null && (t.begin[n] = e.begin[s]), e.end != null && (t.end[n] = e.end[s]), t.strides[n] = e.strides[s], e.beginMask & 1 << s && (t.beginMask |= 1 << n), e.endMask & 1 << s && (t.endMask |= 1 << n), e.shrinkAxisMask & 1 << s ? (t.finalShapeGatherIndices.push(NA), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
     }
 }
-function px(e, t, n, s, r, a) {
+function fx(e, t, n, s, r, a) {
   if (r[t])
     return n > 0 ? a[t] : a[t + 1 & 1];
   {
@@ -6413,8 +6413,8 @@ function px(e, t, n, s, r, a) {
   }
 }
 var ae = {};
-Ae(ae, { Serializable: () => Vk, SerializationMap: () => jr, registerClass: () => Tr });
-var Vk = class {
+Ae(ae, { Serializable: () => Hk, SerializationMap: () => Kr, registerClass: () => $r });
+var Hk = class {
   getClassName() {
     return this.constructor.className;
   }
@@ -6422,31 +6422,31 @@ var Vk = class {
     return new e(t);
   }
 };
-var jr = class {
+var Kr = class {
   constructor() {
     this.classNameMap = {};
   }
   static getMap() {
-    return jr.instance == null && (jr.instance = new jr()), jr.instance;
+    return Kr.instance == null && (Kr.instance = new Kr()), Kr.instance;
   }
   static register(e) {
-    jr.getMap().classNameMap[e.className] = [e, e.fromConfig];
+    Kr.getMap().classNameMap[e.className] = [e, e.fromConfig];
   }
 };
-function Tr(e) {
-  O(e.className != null, () => "Class being registered does not have the static className property defined."), O(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), O(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), jr.register(e);
+function $r(e) {
+  O(e.className != null, () => "Class being registered does not have the static className property defined."), O(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), O(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Kr.register(e);
 }
-var _A = {};
-Ae(_A, { TEST_EPSILON_FLOAT16: () => Wk, encodeStrings: () => Uk, expectArrayBuffersEqual: () => PA, expectArraysClose: () => EA, expectArraysEqual: () => DA, expectNumbersClose: () => FA, expectPromiseToFail: () => RA, expectValuesInRange: () => OA, testEpsilon: () => Vg });
-var AA = 1e-3;
-var Wk = 0.1;
-function EA(e, t, n) {
-  return n == null && (n = Vg()), cm(e, t, (s, r) => Wg(s, r, n));
+var OA = {};
+Ae(OA, { TEST_EPSILON_FLOAT16: () => qk, encodeStrings: () => jk, expectArrayBuffersEqual: () => WA, expectArraysClose: () => zA, expectArraysEqual: () => LA, expectNumbersClose: () => BA, expectPromiseToFail: () => MA, expectValuesInRange: () => VA, testEpsilon: () => Ug });
+var PA = 1e-3;
+var qk = 0.1;
+function zA(e, t, n) {
+  return n == null && (n = Ug()), dm(e, t, (s, r) => Gg(s, r, n));
 }
-function Vg() {
-  return M.backend.floatPrecision() === 32 ? AA : Wk;
+function Ug() {
+  return M.backend.floatPrecision() === 32 ? PA : qk;
 }
-function cm(e, t, n) {
+function dm(e, t, n) {
   let s = true;
   if ((Qt(e) || Qt(t)) && (s = false), Qt(e) && Qt(t) && (s = true), s) {
     let i = e.constructor.name, o = t.constructor.name;
@@ -6455,10 +6455,10 @@ function cm(e, t, n) {
   }
   if (Array.isArray(e) && Array.isArray(t)) {
     let i = Rs(e), o = Rs(t);
-    if (!kr(i, o))
+    if (!Ir(i, o))
       throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`);
   }
-  let r = Qt(e) ? e : sa(e), a = Qt(t) ? t : sa(t);
+  let r = Qt(e) ? e : ra(e), a = Qt(t) ? t : ra(t);
   if (r.length !== a.length)
     throw new Error(`Arrays have different lengths actual: ${r.length} vs expected: ${a.length}.
 Actual:   ${r}.
@@ -6471,26 +6471,26 @@ Actual:   ${r}.
 Expected: ${a}.`);
   }
 }
-function RA(e, t) {
+function MA(e, t) {
   e().then(() => t.fail(), () => t());
 }
-function DA(e, t) {
+function LA(e, t) {
   let n = typeof t == "string" || typeof t == "number" || typeof t == "boolean" ? [t] : t;
-  return ir(e) || ir(e[0]) || ir(t) || ir(t[0]) ? cm(e, n, (s, r) => s == r) : cm(e, t, (s, r) => Wg(s, r, 0));
+  return or(e) || or(e[0]) || or(t) || or(t[0]) ? dm(e, n, (s, r) => s == r) : dm(e, t, (s, r) => Gg(s, r, 0));
 }
-function FA(e, t, n) {
-  if (n == null && (n = Vg()), !Wg(e, t, n))
+function BA(e, t, n) {
+  if (n == null && (n = Ug()), !Gg(e, t, n))
     throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`);
 }
-function Wg(e, t, n) {
+function Gg(e, t, n) {
   return !isFinite(e) && !isFinite(t) ? true : !(isNaN(e) || isNaN(t) || Math.abs(e - t) > n);
 }
-function OA(e, t, n) {
+function VA(e, t, n) {
   for (let s = 0; s < e.length; s++)
     if (e[s] < t || e[s] > n)
       throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`);
 }
-function PA(e, t) {
+function WA(e, t) {
   let n = new Float32Array(e), s = new Float32Array(t);
   if (n.length !== s.length)
     throw new Error(`Expected ArrayBuffer to be of length ${s.length}, but it was ${n.length}`);
@@ -6498,194 +6498,194 @@ function PA(e, t) {
     if (n[r] !== s[r])
       throw new Error(`Expected ArrayBuffer value at ${r} to be ${s[r]} but got ${n[r]} instead`);
 }
-function Uk(e) {
+function jk(e) {
   for (let t = 0; t < e.length; t++) {
     let n = e[t];
-    Array.isArray(n) ? Uk(n) : e[t] = Dl(n);
+    Array.isArray(n) ? jk(n) : e[t] = Dl(n);
   }
   return e;
 }
-var Rde = "0.0.0";
-function Dde() {
+var qde = "0.0.0";
+function jde() {
   X().set("PROD", true);
 }
-function Fde() {
+function Kde() {
   X().set("DEBUG", true);
 }
-function Ode() {
+function Xde() {
   X().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
 }
-function Gk(e) {
+function Kk(e) {
   X().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(e + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
 }
-Z$(Gk);
-function Pde() {
+r_(Kk);
+function Yde() {
   M.disposeVariables();
 }
 function Ss() {
   return M;
 }
-function dm() {
+function pm() {
   return M.memory();
 }
-function zde(e) {
+function Qde(e) {
   return M.profile(e);
 }
 function j(e, t) {
   return M.tidy(e, t);
 }
 function Re(e) {
-  Rg(e).forEach((n) => n.dispose());
+  Fg(e).forEach((n) => n.dispose());
 }
 function Ht(e) {
   return M.keep(e);
 }
-function Mde(e) {
+function Zde(e) {
   return M.time(e);
 }
-function Lde(e) {
+function Jde(e) {
   return M.setBackend(e);
 }
-function Bde() {
+function epe() {
   return M.ready();
 }
-function Vde() {
+function tpe() {
   return M.backendName;
 }
-function Wde(e) {
+function npe(e) {
   M.removeBackend(e);
 }
-function Ude(e) {
+function spe(e) {
   return M.findBackend(e);
 }
-function Gde(e) {
+function rpe(e) {
   return M.findBackendFactory(e);
 }
-function pp(e, t, n = 1) {
+function fp(e, t, n = 1) {
   return M.registerBackend(e, t, n);
 }
-function zA() {
+function UA() {
   return M.backend;
 }
-function Hde(e, t) {
+function ape(e, t) {
   X().setPlatform(e, t);
 }
-function MA(e, t) {
+function GA(e, t) {
   let n = _(e, "a", "add"), s = _(t, "b", "add");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(Ir, r);
+  return M.runKernel(Sr, r);
 }
-var ie = L({ add_: MA });
-function LA(e, t) {
+var ie = L({ add_: GA });
+function HA(e, t) {
   let n = _(e, "a", "floorDiv"), s = _(t, "b", "floorDiv");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(za, r);
+  return M.runKernel(La, r);
 }
-var Hk = L({ floorDiv_: LA });
-function BA(e, t) {
+var Xk = L({ floorDiv_: HA });
+function qA(e, t) {
   let n = _(e, "a", "div"), s = _(t, "b", "div");
   if ([n, s] = vt(n, s), n.dtype === "int32" && s.dtype === "int32")
-    return Hk(n, s);
+    return Xk(n, s);
   let r = { a: n, b: s }, a = {};
-  return M.runKernel(Da, r, a);
+  return M.runKernel(Oa, r, a);
 }
-var xe = L({ div_: BA });
-function VA(e, t) {
+var xe = L({ div_: qA });
+function jA(e, t) {
   let n = _(e, "a", "mul"), s = _(t, "b", "mul");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(Ya, r);
+  return M.runKernel(Za, r);
 }
-var V = L({ mul_: VA });
-function WA(e) {
+var V = L({ mul_: jA });
+function KA(e) {
   let t = _(e, "x", "abs");
   if (t.dtype === "complex64") {
     let n = { x: t };
-    return M.runKernel(Kd, n);
+    return M.runKernel(Yd, n);
   } else {
     let n = { x: t };
-    return M.runKernel(io, n);
+    return M.runKernel(uo, n);
   }
 }
-var Mt = L({ abs_: WA });
-function UA(e) {
+var Mt = L({ abs_: KA });
+function XA(e) {
   let n = { x: _(e, "x", "acos") };
-  return M.runKernel(sl, n);
-}
-var GA = L({ acos_: UA });
-function HA(e) {
-  let n = { x: _(e, "x", "acosh") };
   return M.runKernel(rl, n);
 }
-var qA = L({ acosh_: HA });
-function jA(e) {
+var YA = L({ acos_: XA });
+function QA(e) {
+  let n = { x: _(e, "x", "acosh") };
+  return M.runKernel(al, n);
+}
+var ZA = L({ acosh_: QA });
+function JA(e) {
   O(Array.isArray(e), () => "The argument passed to tf.addN() must be a list of tensors"), O(e.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${e.length}`);
   let t = e.map((r, a) => _(r, `tensors${a}`, "addN")), n = t[0];
   t.forEach((r) => {
     if (r.dtype !== n.dtype)
       throw new Error("All tensors passed to tf.addN() must have the same dtype");
   }), t.forEach((r) => {
-    if (!kr(r.shape, n.shape))
+    if (!Ir(r.shape, n.shape))
       throw new Error("All tensors passed to tf.addN() must have the same shape");
   });
   let s = t;
-  return M.runKernel(ka, s);
+  return M.runKernel(Ia, s);
 }
-var KA = L({ addN_: jA });
-function XA(e, t = null, n = false) {
+var eE = L({ addN_: JA });
+function tE(e, t = null, n = false) {
   let r = { x: _(e, "x", "all", "bool") }, a = { axis: t, keepDims: n };
-  return M.runKernel(al, r, a);
-}
-var qk = L({ all_: XA });
-function YA(e, t = null, n = false) {
-  let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
   return M.runKernel(il, r, a);
 }
-var pm = L({ any_: YA });
-function QA(e, t = 0) {
+var Yk = L({ all_: tE });
+function nE(e, t = null, n = false) {
+  let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
+  return M.runKernel(ol, r, a);
+}
+var hm = L({ any_: nE });
+function sE(e, t = 0) {
   let s = { x: _(e, "x", "argMax") }, r = { axis: t };
-  return M.runKernel(Ia, s, r);
+  return M.runKernel(Sa, s, r);
 }
-var Hu = L({ argMax_: QA });
-function ZA(e, t = 0) {
+var qu = L({ argMax_: sE });
+function rE(e, t = 0) {
   let s = { x: _(e, "x", "argMin") }, r = { axis: t };
-  return M.runKernel(ol, s, r);
+  return M.runKernel(ul, s, r);
 }
-var JA = L({ argMin_: ZA });
-function eE(e) {
+var aE = L({ argMin_: rE });
+function iE(e) {
   let n = { x: _(e, "x", "asin") };
-  return M.runKernel(ul, n);
-}
-var tE = L({ asin_: eE });
-function nE(e) {
-  let n = { x: _(e, "x", "asinh") };
   return M.runKernel(ll, n);
 }
-var sE = L({ asinh_: nE });
-function rE(e) {
-  let n = { x: _(e, "x", "atan") };
+var oE = L({ asin_: iE });
+function uE(e) {
+  let n = { x: _(e, "x", "asinh") };
   return M.runKernel(cl, n);
 }
-var aE = L({ atan_: rE });
-function iE(e, t) {
+var lE = L({ asinh_: uE });
+function cE(e) {
+  let n = { x: _(e, "x", "atan") };
+  return M.runKernel(dl, n);
+}
+var dE = L({ atan_: cE });
+function pE(e, t) {
   let n = _(e, "a", "atan2"), s = _(t, "b", "atan2");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(pl, r);
+  return M.runKernel(hl, r);
 }
-var oE = L({ atan2_: iE });
-function uE(e) {
+var hE = L({ atan2_: pE });
+function fE(e) {
   let n = { x: _(e, "x", "atanh") };
-  return M.runKernel(dl, n);
+  return M.runKernel(pl, n);
 }
-var lE = L({ atanh_: uE });
-function cE(e, t, n, s, r = "NHWC", a) {
-  let i = e[3], o = [...t, i], u = Xk(r);
+var mE = L({ atanh_: fE });
+function gE(e, t, n, s, r = "NHWC", a) {
+  let i = e[3], o = [...t, i], u = Jk(r);
   return Ol(e, o, n, a, s, null, null, u);
 }
-function jk(e, t, n, s, r, a, i = "channelsLast") {
+function Qk(e, t, n, s, r, a, i = "channelsLast") {
   let [o, u] = xd(t), l;
   if (i === "channelsLast")
     l = [o, u, e[3], e[3]];
@@ -6695,15 +6695,15 @@ function jk(e, t, n, s, r, a, i = "channelsLast") {
     throw new Error(`Unknown dataFormat ${i}`);
   return Ol(e, l, n, s, r, a, false, i);
 }
-function dE(e, t, n, s, r, a, i = "NDHWC") {
-  let [o, u, l] = hm(t), c, p;
+function bE(e, t, n, s, r, a, i = "NDHWC") {
+  let [o, u, l] = fm(t), c, p;
   if (i === "NDHWC")
     p = "channelsLast", c = [o, u, l, e[4], e[4]];
   else if (i === "NCDHW")
     p = "channelsFirst", c = [o, u, l, e[1], e[1]];
   else
     throw new Error(`Unknown dataFormat ${i}`);
-  return Kk(e, c, n, s, r, false, p, a);
+  return Zk(e, c, n, s, r, false, p, a);
 }
 function Ol(e, t, n, s, r, a, i = false, o = "channelsLast") {
   let [u, l, c, p] = [-1, -1, -1, -1];
@@ -6713,10 +6713,10 @@ function Ol(e, t, n, s, r, a, i = false, o = "channelsLast") {
     [u, p, l, c] = e;
   else
     throw new Error(`Unknown dataFormat ${o}`);
-  let [d, h, , f] = t, [m, g] = xd(n), [b, y] = xd(s), v = ji(d, b), x = ji(h, y), { padInfo: k, outHeight: T, outWidth: N } = fE(r, l, c, m, g, v, x, a, o), E = i ? f * p : f, A;
+  let [d, h, , f] = t, [m, g] = xd(n), [b, y] = xd(s), v = Xi(d, b), x = Xi(h, y), { padInfo: k, outHeight: T, outWidth: N } = xE(r, l, c, m, g, v, x, a, o), E = i ? f * p : f, A;
   return o === "channelsFirst" ? A = [u, E, T, N] : o === "channelsLast" && (A = [u, T, N, E]), { batchSize: u, dataFormat: o, inHeight: l, inWidth: c, inChannels: p, outHeight: T, outWidth: N, outChannels: E, padInfo: k, strideHeight: m, strideWidth: g, filterHeight: d, filterWidth: h, effectiveFilterHeight: v, effectiveFilterWidth: x, dilationHeight: b, dilationWidth: y, inShape: e, outShape: A, filterShape: t };
 }
-function Kk(e, t, n, s, r, a = false, i = "channelsLast", o) {
+function Zk(e, t, n, s, r, a = false, i = "channelsLast", o) {
   let [u, l, c, p, d] = [-1, -1, -1, -1, -1];
   if (i === "channelsLast")
     [u, l, c, p, d] = e;
@@ -6724,37 +6724,37 @@ function Kk(e, t, n, s, r, a = false, i = "channelsLast", o) {
     [u, d, l, c, p] = e;
   else
     throw new Error(`Unknown dataFormat ${i}`);
-  let [h, f, m, , g] = t, [b, y, v] = hm(n), [x, k, T] = hm(s), N = ji(h, x), E = ji(f, k), A = ji(m, T), { padInfo: P, outDepth: R, outHeight: F, outWidth: $ } = mE(r, l, c, p, b, y, v, N, E, A, o), z = a ? g * d : g, W;
+  let [h, f, m, , g] = t, [b, y, v] = fm(n), [x, k, T] = fm(s), N = Xi(h, x), E = Xi(f, k), A = Xi(m, T), { padInfo: P, outDepth: R, outHeight: F, outWidth: $ } = wE(r, l, c, p, b, y, v, N, E, A, o), z = a ? g * d : g, W;
   return i === "channelsFirst" ? W = [u, z, R, F, $] : i === "channelsLast" && (W = [u, R, F, $, z]), { batchSize: u, dataFormat: i, inDepth: l, inHeight: c, inWidth: p, inChannels: d, outDepth: R, outHeight: F, outWidth: $, outChannels: z, padInfo: P, strideDepth: b, strideHeight: y, strideWidth: v, filterDepth: h, filterHeight: f, filterWidth: m, effectiveFilterDepth: N, effectiveFilterHeight: E, effectiveFilterWidth: A, dilationDepth: x, dilationHeight: k, dilationWidth: T, inShape: e, outShape: W, filterShape: t };
 }
-function pE(e, t, n, s, r) {
-  s == null && (s = Ug(e, t, n));
-  let a = e[0], i = e[1], o = Jr((a - t + 2 * s) / n + 1, r), u = Jr((i - t + 2 * s) / n + 1, r);
+function yE(e, t, n, s, r) {
+  s == null && (s = Hg(e, t, n));
+  let a = e[0], i = e[1], o = ea((a - t + 2 * s) / n + 1, r), u = ea((i - t + 2 * s) / n + 1, r);
   return [o, u];
 }
-function hE(e, t, n, s, r, a) {
-  r == null && (r = Ug(e, t, s));
-  let i = e[0], o = e[1], u = e[2], l = Jr((i - t + 2 * r) / s + 1, a), c = Jr((o - t + 2 * r) / s + 1, a), p = Jr((u - t + 2 * r) / s + 1, a);
+function vE(e, t, n, s, r, a) {
+  r == null && (r = Hg(e, t, s));
+  let i = e[0], o = e[1], u = e[2], l = ea((i - t + 2 * r) / s + 1, a), c = ea((o - t + 2 * r) / s + 1, a), p = ea((u - t + 2 * r) / s + 1, a);
   return [l, c, p, n];
 }
-function Ug(e, t, n, s = 1) {
-  let r = ji(t, s);
+function Hg(e, t, n, s = 1) {
+  let r = Xi(t, s);
   return Math.floor((e[0] * (n - 1) - n + r) / 2);
 }
 function xd(e) {
   return typeof e == "number" ? [e, e, e] : e.length === 2 ? [e[0], e[1], 1] : e;
 }
-function hm(e) {
+function fm(e) {
   return typeof e == "number" ? [e, e, e] : e;
 }
-function ji(e, t) {
+function Xi(e, t) {
   return t <= 1 ? e : e + (e - 1) * (t - 1);
 }
-function fE(e, t, n, s, r, a, i, o, u) {
+function xE(e, t, n, s, r, a, i, o, u) {
   let l, c, p;
   if (typeof e == "number") {
     l = { top: e, bottom: e, left: e, right: e, type: e === 0 ? "VALID" : "NUMBER" };
-    let h = pE([t, n], a, s, e, o);
+    let h = yE([t, n], a, s, e, o);
     c = h[0], p = h[1];
   } else if (e === "same") {
     c = Math.ceil(t / s), p = Math.ceil(n / r);
@@ -6764,16 +6764,16 @@ function fE(e, t, n, s, r, a, i, o, u) {
     l = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" }, c = Math.ceil((t - a + 1) / s), p = Math.ceil((n - i + 1) / r);
   else if (typeof e == "object") {
     let d = u === "channelsLast" ? e[1][0] : e[2][0], h = u === "channelsLast" ? e[1][1] : e[2][1], f = u === "channelsLast" ? e[2][0] : e[3][0], m = u === "channelsLast" ? e[2][1] : e[3][1];
-    l = { top: d, bottom: h, left: f, right: m, type: d === 0 && h === 0 && f === 0 && m === 0 ? "VALID" : "EXPLICIT" }, c = Jr((t - a + d + h) / s + 1, o), p = Jr((n - i + f + m) / r + 1, o);
+    l = { top: d, bottom: h, left: f, right: m, type: d === 0 && h === 0 && f === 0 && m === 0 ? "VALID" : "EXPLICIT" }, c = ea((t - a + d + h) / s + 1, o), p = ea((n - i + f + m) / r + 1, o);
   } else
     throw Error(`Unknown padding parameter: ${e}`);
   return { padInfo: l, outHeight: c, outWidth: p };
 }
-function mE(e, t, n, s, r, a, i, o, u, l, c) {
+function wE(e, t, n, s, r, a, i, o, u, l, c) {
   let p, d, h, f;
   if (typeof e == "number") {
     p = { top: e, bottom: e, left: e, right: e, front: e, back: e, type: e === 0 ? "VALID" : "NUMBER" };
-    let g = hE([t, n, s, 1], o, 1, r, e, c);
+    let g = vE([t, n, s, 1], o, 1, r, e, c);
     d = g[0], h = g[1], f = g[2];
   } else if (e === "same") {
     d = Math.ceil(t / r), h = Math.ceil(n / a), f = Math.ceil(s / i);
@@ -6785,7 +6785,7 @@ function mE(e, t, n, s, r, a, i, o, u, l, c) {
     throw Error(`Unknown padding parameter: ${e}`);
   return { padInfo: p, outDepth: d, outHeight: h, outWidth: f };
 }
-function Jr(e, t) {
+function ea(e, t) {
   if (!t)
     return Math.trunc(e);
   switch (t) {
@@ -6799,14 +6799,14 @@ function Jr(e, t) {
       throw new Error(`Unknown roundingMode ${t}`);
   }
 }
-function fr(e) {
+function mr(e) {
   let [t, n, s] = xd(e);
   return t === 1 && n === 1 && s === 1;
 }
 function Ps(e, t) {
-  return fr(e) || fr(t);
+  return mr(e) || mr(t);
 }
-function Xk(e) {
+function Jk(e) {
   if (e === "NHWC")
     return "channelsLast";
   if (e === "NCHW")
@@ -6818,134 +6818,134 @@ function hn(e, t, n) {
     if (typeof t == "string")
       throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
     if (typeof t == "number")
-      O(Yi(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
+      O(Zi(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
     else if (typeof t == "object")
       t.forEach((s) => {
         s.forEach((r) => {
-          O(Yi(r), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`);
+          O(Zi(r), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`);
         });
       });
     else
       throw Error(`Error in ${e}: Unknown padding parameter: ${t}`);
   }
 }
-function gE(e, t) {
+function kE(e, t) {
   let s = { x: _(e, "x", "reshape", "string_or_numeric") }, r = { shape: t };
-  return M.runKernel(Eo, s, r);
+  return M.runKernel(Ro, s, r);
 }
-var G = L({ reshape_: gE });
-function bE(e, t, n, s, r) {
+var G = L({ reshape_: kE });
+function IE(e, t, n, s, r) {
   let a = _(e, "x", "avgPool", "float32"), i = 1;
   O(Ps(n, i), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);
   let o = a, u = false;
   a.rank === 3 && (u = true, o = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(o.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${o.rank}.`), hn("avgPool", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(Sa, l, c);
+  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(Ca, l, c);
   return p = ce(p, a.dtype), u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
-var Gg = L({ avgPool_: bE });
-function yE(e, t, n, s, r, a = "NDHWC") {
+var qg = L({ avgPool_: IE });
+function SE(e, t, n, s, r, a = "NDHWC") {
   let i = _(e, "x", "avgPool3d", "float32"), o = i, u = false;
   i.rank === 4 && (u = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), hn("avgPool3d", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(qd, l, c);
+  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(Kd, l, c);
   return p = ce(p, o.dtype), u ? G(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
 }
-var Yk = L({ avgPool3d_: yE });
-function vE(e, t = 0) {
+var eI = L({ avgPool3d_: SE });
+function CE(e, t = 0) {
   O(e.length >= 1, () => "Pass at least one tensor to concat");
-  let n = Gu(e, "tensors", "concat", "string_or_numeric");
+  let n = Hu(e, "tensors", "concat", "string_or_numeric");
   if (n[0].dtype === "complex64" && n.forEach((a) => {
     if (a.dtype !== "complex64")
       throw new Error(`Cannot concatenate complex64 tensors with a tensor
           with dtype ${a.dtype}. `);
   }), n.length === 1)
-    return lr(n[0]);
+    return cr(n[0]);
   let s = n, r = { axis: t };
-  return M.runKernel(uo, s, r);
+  return M.runKernel(co, s, r);
 }
-var Ft = L({ concat_: vE });
-function xE(e) {
+var Ft = L({ concat_: CE });
+function NE(e) {
   let n = { x: _(e, "x", "sigmoid", "float32") };
-  return M.runKernel(ai, n);
+  return M.runKernel(ui, n);
 }
-var qs = L({ sigmoid_: xE });
-function wE(e, t, n) {
+var qs = L({ sigmoid_: NE });
+function TE(e, t, n) {
   let s = _(e, "x", "slice", "string_or_numeric");
   if (s.rank === 0)
     throw new Error("Slicing scalar is not possible");
   let r = { x: s }, a = { begin: t, size: n };
-  return M.runKernel(Po, r, a);
+  return M.runKernel(zo, r, a);
 }
-var He = L({ slice_: wE });
-function kE(e) {
+var qe = L({ slice_: TE });
+function $E(e) {
   let n = { x: _(e, "x", "tanh", "float32") };
-  return M.runKernel(di, n);
+  return M.runKernel(fi, n);
 }
-var qu = L({ tanh_: kE });
-function IE(e, t, n, s, r, a) {
-  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = He(f, [0, 0], b), v = He(f, [0, g], b), x = He(f, [0, g * 2], b), k = He(f, [0, g * 3], b), T = ie(V(qs(y), qu(v)), V(c, qs(ie(i, x)))), N = V(qu(T), qs(k));
+var ju = L({ tanh_: $E });
+function _E(e, t, n, s, r, a) {
+  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = qe(f, [0, 0], b), v = qe(f, [0, g], b), x = qe(f, [0, g * 2], b), k = qe(f, [0, g * 3], b), T = ie(V(qs(y), ju(v)), V(c, qs(ie(i, x)))), N = V(ju(T), qs(k));
   return [T, N];
 }
-var qde = L({ basicLSTMCell_: IE });
-function SE(e, t, n) {
+var ipe = L({ basicLSTMCell_: _E });
+function AE(e, t, n) {
   let s = _(e, "x", "batchToSpaceND"), r = t.reduce((o, u) => o * u);
   O(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), O(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), O(s.shape[0] % r === 0, () => `input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);
   let a = { x: s }, i = { blockShape: t, crops: n };
-  return M.runKernel(oo, a, i);
+  return M.runKernel(lo, a, i);
 }
-var Hg = L({ batchToSpaceND_: SE });
-function CE(e) {
+var jg = L({ batchToSpaceND_: AE });
+function EE(e) {
   let t;
   return e.rank === 0 || e.rank === 1 ? t = G(e, [1, 1, 1, e.size]) : e.rank === 2 ? t = G(e, [1, 1, e.shape[0], e.shape[1]]) : e.rank === 3 ? t = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]) : t = e, t;
 }
-function NE(e, t, n, s, r, a) {
+function RE(e, t, n, s, r, a) {
   a == null && (a = 1e-3);
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
   s != null && (c = _(s, "offset", "batchNorm")), O(o.rank === u.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), O(c == null || o.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), O(l == null || o.rank === l.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
-  let d = { x: CE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = M.runKernel(Ma, d, h);
+  let d = { x: EE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = M.runKernel(Ba, d, h);
   return G(f, i.shape);
 }
-var ju = L({ batchNorm_: NE });
-function TE(e, t, n, s, r, a) {
+var Ku = L({ batchNorm_: RE });
+function DE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), Ku(i, o, u, c, l, a);
 }
-var $E = L({ batchNorm2d_: TE });
-function _E(e, t, n, s, r, a) {
+var FE = L({ batchNorm2d_: DE });
+function OE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), Ku(i, o, u, c, l, a);
 }
-var AE = L({ batchNorm3d_: _E });
-function EE(e, t, n, s, r, a) {
+var PE = L({ batchNorm3d_: OE });
+function zE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), Ku(i, o, u, c, l, a);
 }
-var RE = L({ batchNorm4d_: EE });
-function DE(e, t, n) {
+var ME = L({ batchNorm4d_: zE });
+function LE(e, t, n) {
   let s = _(e, "x", "bincount"), r = _(t, "weights", "bincount");
   O(s.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${s.dtype}`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(r.size === s.size || r.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);
   let a = { x: s, weights: r }, i = { size: n };
-  return M.runKernel(ug, a, i);
+  return M.runKernel(cg, a, i);
 }
-var Qk = L({ bincount_: DE });
-function FE(e, t) {
+var tI = L({ bincount_: LE });
+function BE(e, t) {
   let n = _(e, "s0", "broadcastArgs", "int32"), s = _(t, "s1", "broadcastArgs", "int32");
   if (n.rank !== 1)
     throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);
   if (s.rank !== 1)
     throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);
   let r = { s0: n, s1: s };
-  return M.runKernel(lg, r);
+  return M.runKernel(dg, r);
 }
-var OE = L({ broadcastArgs_: FE });
-function PE(e, t) {
+var VE = L({ broadcastArgs_: BE });
+function WE(e, t) {
   let n = _(e, "broadcastTo", "x"), s = n.shape;
   if (t.some((l) => !(l > 0) || l % 1 !== 0))
     throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);
@@ -6964,120 +6964,120 @@ function PE(e, t) {
     else if (n.shape[l] !== 1)
       throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);
   if (a.map((l, c) => l > 1 ? c : -1).filter((l) => l >= 0).length === 0)
-    return lr(n);
+    return cr(n);
   let o = { x: n }, u = { reps: a };
-  return M.runKernel(Cr, o, u);
+  return M.runKernel(Nr, o, u);
 }
-var nd = L({ broadcastTo_: PE });
-function zE(e) {
+var nd = L({ broadcastTo_: WE });
+function UE(e) {
   let n = { x: _(e, "x", "ceil", "float32") };
-  return M.runKernel(Ta, n);
+  return M.runKernel($a, n);
 }
-var ME = L({ ceil_: zE });
-function LE(e, t, n) {
+var GE = L({ ceil_: UE });
+function HE(e, t, n) {
   let s = _(e, "x", "clipByValue");
   O(t <= n, () => `Error in clip: min (${t}) must be less than or equal to max (${n}).`);
   let r = { x: s }, a = { clipValueMin: t, clipValueMax: n };
-  return M.runKernel(Sr, r, a);
+  return M.runKernel(Cr, r, a);
 }
-var Vn = L({ clipByValue_: LE });
-function BE(e) {
+var Vn = L({ clipByValue_: HE });
+function qE(e) {
   return Ft(e, 0);
 }
-var VE = L({ concat1d_: BE });
-function WE(e, t) {
+var jE = L({ concat1d_: qE });
+function KE(e, t) {
   return Ft(e, t);
 }
-var UE = L({ concat2d_: WE });
-function GE(e, t) {
+var XE = L({ concat2d_: KE });
+function YE(e, t) {
   return Ft(e, t);
 }
-var HE = L({ concat3d_: GE });
-function qE(e, t) {
+var QE = L({ concat3d_: YE });
+function ZE(e, t) {
   return Ft(e, t);
 }
-var jE = L({ concat4d_: qE });
-function KE(e, t, n, s, r = "NHWC", a = [1, 1], i) {
+var JE = L({ concat4d_: ZE });
+function eR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
   let o = _(e, "x", "conv2d", "float32"), u = _(t, "filter", "conv2d", "float32"), l = o, c = false;
   o.rank === 3 && (c = true, l = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(l.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`), hn("conv2d", s, i);
   let p = r === "NHWC" ? l.shape[3] : l.shape[1];
   O(p === u.shape[2], () => `Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
-  let d = { x: l, filter: u }, h = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, f = M.runKernel($a, d, h);
+  let d = { x: l, filter: u }, h = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, f = M.runKernel(_a, d, h);
   return c ? G(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
 }
-var ca = L({ conv2d_: KE });
-function XE(e, t, n, s, r = "NWC", a = 1, i) {
+var da = L({ conv2d_: eR });
+function tR(e, t, n, s, r = "NWC", a = 1, i) {
   let o = _(e, "x", "conv1d"), u = _(t, "filter", "conv1d"), l = o, c = false;
   o.rank === 2 && (c = true, l = G(o, [1, o.shape[0], o.shape[1]])), O(l.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${l.rank}.`), O(u.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`), hn("conv1d", s, i), O(l.shape[2] === u.shape[1], () => `Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`), O(Ps(n, a), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`), O(r === "NWC", () => `Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);
-  let p = G(u, [1, u.shape[0], u.shape[1], u.shape[2]]), d = G(l, [l.shape[0], 1, l.shape[1], l.shape[2]]), g = ca(d, p, [1, n], s, "NHWC", [1, a], i);
+  let p = G(u, [1, u.shape[0], u.shape[1], u.shape[2]]), d = G(l, [l.shape[0], 1, l.shape[1], l.shape[2]]), g = da(d, p, [1, n], s, "NHWC", [1, a], i);
   return c ? G(g, [g.shape[2], g.shape[3]]) : G(g, [g.shape[0], g.shape[2], g.shape[3]]);
 }
-var Zk = L({ conv1d_: XE });
-function YE(e, t, n, s, r, a = "NHWC", i) {
+var nI = L({ conv1d_: tR });
+function nR(e, t, n, s, r, a = "NHWC", i) {
   O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
   let o = e, u = t, l = false;
   t.rank === 3 && (l = true, u = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]), o = [1, e[0], e[1], e[2]]), O(o.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`), O(u.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`), O(n.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);
   let c = a === "NHWC" ? o[3] : o[1], p = a === "NHWC" ? u.shape[3] : u.shape[1];
   O(c === n.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`), O(p === n.shape[3], () => `Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`), hn("conv2dDerInput", r, i);
-  let d = { dy: u, filter: n }, h = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, inputShape: o }, f = M.runKernel(_a, d, h);
+  let d = { dy: u, filter: n }, h = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, inputShape: o }, f = M.runKernel(Aa, d, h);
   return l ? G(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
 }
-var qg = L({ conv2DBackpropInput_: YE });
-function QE(e, t, n, s, r, a) {
+var Kg = L({ conv2DBackpropInput_: nR });
+function sR(e, t, n, s, r, a) {
   let i = _(e, "x", "conv2dTranspose"), o = _(t, "filter", "conv2dTranspose");
-  return qg(n, i, o, s, r, "NHWC", a);
+  return Kg(n, i, o, s, r, "NHWC", a);
 }
-var Jk = L({ conv2dTranspose_: QE });
-function ZE(e, t, n, s, r = "NDHWC", a = [1, 1, 1]) {
+var sI = L({ conv2dTranspose_: sR });
+function rR(e, t, n, s, r = "NDHWC", a = [1, 1, 1]) {
   let i = _(e, "x", "conv3d"), o = _(t, "filter", "conv3d"), u = i, l = false;
   i.rank === 4 && (l = true, u = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(u.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${u.rank}.`), O(o.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`), O(u.shape[4] === o.shape[3], () => `Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${o.shape[3]}.`), O(Ps(n, a), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NDHWC", () => `Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);
-  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = M.runKernel(Xd, c, p);
+  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = M.runKernel(Qd, c, p);
   return l ? G(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
 }
-var eI = L({ conv3d_: ZE });
-function JE(e, t, n, s, r) {
+var rI = L({ conv3d_: rR });
+function aR(e, t, n, s, r) {
   O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
   let a = e, i = t, o = false;
   t.rank === 4 && (o = true, i = G(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]]), a = [1, e[0], e[1], e[2], e[3]]);
   let u = a[4], l = i.shape[4];
   O(a.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`), O(i.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`), O(n.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`), O(u === n.shape[3], () => `Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`), O(l === n.shape[4], () => `Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);
-  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = M.runKernel(pg, c, p);
+  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = M.runKernel(fg, c, p);
   return o ? G(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
 }
-var tI = L({ conv3DBackpropInput_: JE });
-function eR(e, t, n, s, r) {
+var aI = L({ conv3DBackpropInput_: aR });
+function iR(e, t, n, s, r) {
   let a = _(e, "x", "conv3dTranspose"), i = _(t, "filter", "conv3dTranspose");
-  return tI(n, a, i, s, r);
+  return aI(n, a, i, s, r);
 }
-var tR = L({ conv3dTranspose_: eR });
-function nR(e) {
+var oR = L({ conv3dTranspose_: iR });
+function uR(e) {
   let n = { x: _(e, "x", "cos", "float32") };
-  return M.runKernel(Aa, n);
-}
-var jg = L({ cos_: nR });
-function sR(e) {
-  let n = { x: _(e, "x", "cosh", "float32") };
   return M.runKernel(Ea, n);
 }
-var nI = L({ cosh_: sR });
-function rR(e, t = 0, n = false, s = false) {
+var Xg = L({ cos_: uR });
+function lR(e) {
+  let n = { x: _(e, "x", "cosh", "float32") };
+  return M.runKernel(Ra, n);
+}
+var iI = L({ cosh_: lR });
+function cR(e, t = 0, n = false, s = false) {
   let a = { x: _(e, "x", "cumprod") }, i = { axis: t, exclusive: n, reverse: s };
-  return M.runKernel(hl, a, i);
+  return M.runKernel(po, a, i);
 }
-var aR = L({ cumprod_: rR });
-function iR(e, t = 0, n = false, s = false) {
+var mm = L({ cumprod_: cR });
+function dR(e, t = 0, n = false, s = false) {
   let a = { x: _(e, "x", "cumsum") }, i = { axis: t, exclusive: n, reverse: s };
-  return M.runKernel(lo, a, i);
+  return M.runKernel(Da, a, i);
 }
-var sI = L({ cumsum_: iR });
-function oR(e, t, n, s = false) {
+var oI = L({ cumsum_: dR });
+function pR(e, t, n, s = false) {
   let r = _(e, "x", "denseBincount"), a = _(t, "weights", "denseBincount");
   O(r.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${r.dtype}`), O(r.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(a.size === r.size || a.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);
   let i = { x: r, weights: a }, o = { size: n, binaryOutput: s };
-  return M.runKernel(hg, i, o);
+  return M.runKernel(mg, i, o);
 }
-var uR = L({ denseBincount_: oR });
-function lR(e, t, n = "NHWC") {
+var hR = L({ denseBincount_: pR });
+function fR(e, t, n = "NHWC") {
   let s = _(e, "x", "depthToSpace", "float32"), r = n === "NHWC" ? s.shape[1] : s.shape[2], a = n === "NHWC" ? s.shape[2] : s.shape[3], i = n === "NHWC" ? s.shape[3] : s.shape[1];
   O(t > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${t}`), O(r * t >= 0, () => `Negative dimension size caused by overflow when multiplying
     ${r} and ${t}  for depthToSpace with input shape
@@ -7085,55 +7085,55 @@ function lR(e, t, n = "NHWC") {
     ${a} and ${t} for depthToSpace with input shape
         ${s.shape}`), O(i % (t * t) === 0, () => `Dimension size must be evenly divisible by ${t * t} but is ${i} for depthToSpace with input shape ${s.shape}`);
   let o = { x: s }, u = { blockSize: t, dataFormat: n };
-  return M.runKernel(po, o, u);
+  return M.runKernel(fo, o, u);
 }
-var cR = L({ depthToSpace_: lR });
-function dR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
+var mR = L({ depthToSpace_: fR });
+function gR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
   let o = _(e, "x", "depthwiseConv2d", "float32"), u = _(t, "filter", "depthwiseConv2d", "float32"), l = o, c = false;
   o.rank === 3 && (c = true, l = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(l.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[3] === u.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`), hn("depthwiseConv2d", s, i);
-  let p = { x: l, filter: u }, d = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, h = M.runKernel(Ra, p, d);
+  let p = { x: l, filter: u }, d = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, h = M.runKernel(Fa, p, d);
   return c ? G(h, [h.shape[1], h.shape[2], h.shape[3]]) : h;
 }
-var hp = L({ depthwiseConv2d_: dR });
-function pR(e) {
+var mp = L({ depthwiseConv2d_: gR });
+function bR(e) {
   let n = { x: _(e, "x", "diag") };
-  return M.runKernel(gg, n);
+  return M.runKernel(yg, n);
 }
-var jde = L({ diag_: pR });
-function hR(e, t, n, s, r = [1, 1], a = "NHWC") {
+var ope = L({ diag_: bR });
+function yR(e, t, n, s, r = [1, 1], a = "NHWC") {
   let i = _(e, "x", "dilation2d"), o = _(t, "filter", "dilation2d");
   O(i.rank === 3 || i.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`), O(o.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`), O(a === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);
   let u = i, l = false;
   i.rank === 3 && (u = G(i, [1, i.shape[0], i.shape[1], i.shape[2]]), l = true);
-  let c = { x: u, filter: o }, p = { strides: n, pad: s, dilations: r }, d = M.runKernel(Yd, c, p);
+  let c = { x: u, filter: o }, p = { strides: n, pad: s, dilations: r }, d = M.runKernel(Zd, c, p);
   return l ? G(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
 }
-var fR = L({ dilation2d_: hR });
-function mR(e, t) {
+var vR = L({ dilation2d_: yR });
+function xR(e, t) {
   let n = _(e, "a", "equal", "string_or_numeric"), s = _(t, "b", "equal", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(ho, r);
+  return M.runKernel(mo, r);
 }
-var Kn = L({ equal_: mR });
-function gR(e, t, n) {
+var Xn = L({ equal_: xR });
+function wR(e, t, n) {
   let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = ot(ot(a.shape, s.shape), r.shape), o = nd(a, i), u = nd(s, i), l = nd(r, i), c = { condition: o, t: u, e: l };
-  return M.runKernel(Oo, c);
+  return M.runKernel(Po, c);
 }
-var vn = L({ where_: gR });
-function bR(e) {
+var vn = L({ where_: wR });
+function kR(e) {
   let n = { x: _(e, "x", "zerosLike") };
-  return M.runKernel(Ho, n);
+  return M.runKernel(qo, n);
 }
-var je = L({ zerosLike_: bR });
-function yR(e, t) {
+var je = L({ zerosLike_: kR });
+function IR(e, t) {
   let n = _(e, "a", "div"), s = _(t, "b", "div");
   [n, s] = vt(n, s);
-  let r = xe(n, s), a = je(r), i = Kn(s, a);
+  let r = xe(n, s), a = je(r), i = Xn(s, a);
   return vn(i, a, r);
 }
-var vR = L({ divNoNan_: yR });
-function xR(e, t) {
+var SR = L({ divNoNan_: IR });
+function CR(e, t) {
   let n = _(e, "t1", "dot"), s = _(t, "t2", "dot");
   O((n.rank === 1 || n.rank === 2) && (s.rank === 1 || s.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);
   let r = n.rank === 1 ? n.size : n.shape[1], a = s.rank === 1 ? s.size : s.shape[0];
@@ -7151,49 +7151,49 @@ function xR(e, t) {
     return We(n, i);
   }
 }
-var Kde = L({ dot_: xR });
-function wR(e, ...t) {
+var upe = L({ dot_: CR });
+function NR(e, ...t) {
   let n = t.map((r, a) => _(r, `tensors${a}`, "einsum")), s = { equation: e };
-  return M.runKernel(Qd, n, s);
+  return M.runKernel(Jd, n, s);
 }
-var kR = L({ einsum_: wR });
-function IR(e) {
+var TR = L({ einsum_: NR });
+function $R(e) {
   let n = { x: _(e, "x", "elu", "float32") };
-  return M.runKernel(Fa, n);
+  return M.runKernel(Pa, n);
 }
-var fp = L({ elu_: IR });
-function SR(e) {
+var gp = L({ elu_: $R });
+function _R(e) {
   let t = _(e, "x", "erf");
   O(t.dtype === "int32" || t.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t.dtype === "int32" && (t = ce(t, "float32"));
   let n = { x: t };
   return M.runKernel(fl, n);
 }
-var CR = L({ erf_: SR });
-function NR(e) {
+var AR = L({ erf_: _R });
+function ER(e) {
   let n = { x: _(e, "x", "exp") };
-  return M.runKernel(Oa, n);
+  return M.runKernel(za, n);
 }
-var Xn = L({ exp_: NR });
-function TR(e, t = 0) {
+var Yn = L({ exp_: ER });
+function RR(e, t = 0) {
   let n = _(e, "x", "expandDims", "string_or_numeric");
   O(t <= n.rank, () => "Axis must be <= rank of the tensor");
   let s = { input: n }, r = { dim: t };
-  return M.runKernel(fo, s, r);
+  return M.runKernel(go, s, r);
 }
-var Pn = L({ expandDims_: TR });
-function $R(e) {
+var Pn = L({ expandDims_: RR });
+function DR(e) {
   let n = { x: _(e, "x", "expm1") };
-  return M.runKernel(mo, n);
+  return M.runKernel(bo, n);
 }
-var _R = L({ expm1_: $R });
-function AR(e, t) {
+var FR = L({ expm1_: DR });
+function OR(e, t) {
   let n = _(e, "x", "tile", "string_or_numeric");
   O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);
   let s = { x: n }, r = { reps: t };
-  return M.runKernel(Cr, s, r);
+  return M.runKernel(Nr, s, r);
 }
-var ps = L({ tile_: AR });
-function ER(e, t, n, s = "float32") {
+var ps = L({ tile_: OR });
+function PR(e, t, n, s = "float32") {
   t == null && (t = e);
   let r = De([e, t], s), a = e <= t ? e : t;
   for (let o = 0; o < a; ++o)
@@ -7209,135 +7209,135 @@ function ER(e, t, n, s = "float32") {
     return ps(Pn(Pn(Pn(i, 0), 0), 0), [n[0], n[1], n[2], 1, 1]);
   throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`);
 }
-var rI = L({ eye_: ER });
+var uI = L({ eye_: PR });
 function Pl(e, t, n) {
   let s = { shape: e, value: t, dtype: n };
   return M.runKernel(ml, {}, s);
 }
-function RR(e) {
-  let n = { x: _(e, "x", "floor", "float32") };
-  return M.runKernel(Pa, n);
-}
-var mp = L({ floor_: RR });
-function DR(e, t, n = 0, s = 0) {
-  let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), i = { x: r, indices: a }, o = { axis: n, batchDims: s };
-  return M.runKernel(bo, i, o);
-}
-var Ku = L({ gather_: DR });
-function FR(e, t) {
-  let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
-  [n, s] = vt(n, s), ot(n.shape, s.shape);
-  let r = { a: n, b: s };
-  return M.runKernel(vo, r);
-}
-var Wn = L({ greater_: FR });
-function OR(e, t) {
-  let n = _(e, "a", "greaterEqual", "string_or_numeric"), s = _(t, "b", "greaterEqual", "string_or_numeric");
-  [n, s] = vt(n, s), ot(n.shape, s.shape);
-  let r = { a: n, b: s };
-  return M.runKernel(La, r);
-}
-var Ko = L({ greaterEqual_: OR });
-function PR(e) {
-  let n = { input: _(e, "input", "imag") };
-  return M.runKernel(Zd, n);
-}
-var Kg = L({ imag_: PR });
 function zR(e) {
-  let n = { x: _(e, "x", "isFinite") };
-  return M.runKernel(gl, n);
+  let n = { x: _(e, "x", "floor", "float32") };
+  return M.runKernel(Ma, n);
 }
-var Xde = L({ isFinite_: zR });
-function MR(e) {
-  let n = { x: _(e, "x", "isInf") };
-  return M.runKernel(bl, n);
+var bp = L({ floor_: zR });
+function MR(e, t, n = 0, s = 0) {
+  let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), i = { x: r, indices: a }, o = { axis: n, batchDims: s };
+  return M.runKernel(vo, i, o);
 }
-var Yde = L({ isInf_: MR });
-function LR(e) {
-  let n = { x: _(e, "x", "isNaN") };
-  return M.runKernel(yl, n);
-}
-var BR = L({ isNaN_: LR });
-function VR(e, t = 0.2) {
-  let s = { x: _(e, "x", "leakyRelu") }, r = { alpha: t };
-  return M.runKernel(Va, s, r);
-}
-var Xg = L({ leakyRelu_: VR });
-function WR(e, t) {
-  let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
-  [n, s] = vt(n, s), ot(n.shape, s.shape);
-  let r = { a: n, b: s };
-  return M.runKernel(xo, r);
-}
-var aI = L({ less_: WR });
-function UR(e, t) {
-  let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
+var Xu = L({ gather_: MR });
+function LR(e, t) {
+  let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
   return M.runKernel(wo, r);
 }
-var Xo = L({ lessEqual_: UR });
-function GR(e, t, n) {
+var Un = L({ greater_: LR });
+function BR(e, t) {
+  let n = _(e, "a", "greaterEqual", "string_or_numeric"), s = _(t, "b", "greaterEqual", "string_or_numeric");
+  [n, s] = vt(n, s), ot(n.shape, s.shape);
+  let r = { a: n, b: s };
+  return M.runKernel(Va, r);
+}
+var Xo = L({ greaterEqual_: BR });
+function VR(e) {
+  let n = { input: _(e, "input", "imag") };
+  return M.runKernel(ep, n);
+}
+var Yg = L({ imag_: VR });
+function WR(e) {
+  let n = { x: _(e, "x", "isFinite") };
+  return M.runKernel(gl, n);
+}
+var lpe = L({ isFinite_: WR });
+function UR(e) {
+  let n = { x: _(e, "x", "isInf") };
+  return M.runKernel(bl, n);
+}
+var cpe = L({ isInf_: UR });
+function GR(e) {
+  let n = { x: _(e, "x", "isNaN") };
+  return M.runKernel(yl, n);
+}
+var HR = L({ isNaN_: GR });
+function qR(e, t = 0.2) {
+  let s = { x: _(e, "x", "leakyRelu") }, r = { alpha: t };
+  return M.runKernel(Ua, s, r);
+}
+var Qg = L({ leakyRelu_: qR });
+function jR(e, t) {
+  let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
+  [n, s] = vt(n, s), ot(n.shape, s.shape);
+  let r = { a: n, b: s };
+  return M.runKernel(ko, r);
+}
+var lI = L({ less_: jR });
+function KR(e, t) {
+  let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
+  [n, s] = vt(n, s), ot(n.shape, s.shape);
+  let r = { a: n, b: s };
+  return M.runKernel(Io, r);
+}
+var Yo = L({ lessEqual_: KR });
+function XR(e, t, n) {
   if (n <= 0)
     throw new Error("The number of values should be positive.");
   let s = { start: e, stop: t, num: n };
-  return M.runKernel(xg, {}, s);
+  return M.runKernel(kg, {}, s);
 }
-function HR(e, t = 5, n = 1, s = 1, r = 0.5) {
+function YR(e, t = 5, n = 1, s = 1, r = 0.5) {
   let a = _(e, "x", "localResponseNormalization");
   O(a.rank === 4 || a.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${a.rank}.`), O(Yi(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
+               rank ${a.rank}.`), O(Zi(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
   let i = a, o = false;
   a.rank === 3 && (o = true, i = G(a, [1, a.shape[0], a.shape[1], a.shape[2]]));
-  let u = { x: i }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = M.runKernel(ep, u, l);
+  let u = { x: i }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = M.runKernel(np, u, l);
   return o ? G(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
 }
-var qR = L({ localResponseNormalization_: HR });
-function jR(e) {
+var QR = L({ localResponseNormalization_: YR });
+function ZR(e) {
   let n = { x: _(e, "x", "log", "float32") };
-  return M.runKernel(Wa, n);
+  return M.runKernel(Ga, n);
 }
-var Yn = L({ log_: jR });
-function KR(e) {
+var Qn = L({ log_: ZR });
+function JR(e) {
   let n = { x: _(e, "x", "log1p") };
   return M.runKernel(vl, n);
 }
-var Yg = L({ log1p_: KR });
-function Qde(e) {
-  return O(hr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
+var Zg = L({ log1p_: JR });
+function dpe(e) {
+  return O(fr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
     let s = _(t, "x", "tf.grad", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grad") : null;
     return M.tidy(() => {
       let { value: a, grads: i } = M.gradients(() => e(s), [s], r);
-      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), gp(i), i[0];
+      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), yp(i), i[0];
     });
   };
 }
-function Zde(e) {
-  return O(hr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
+function ppe(e) {
+  return O(fr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
     O(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-    let s = Gu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
+    let s = Hu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
     return M.tidy(() => {
       let { value: a, grads: i } = M.gradients(() => e(...s), s, r);
-      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), gp(i), i;
+      return r != null && pn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), yp(i), i;
     });
   };
 }
-function Jde(e) {
-  return O(hr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
+function hpe(e) {
+  return O(fr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
     O(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
     let { grads: s, value: r } = M.gradients(() => e(t), [t], n);
-    return gp(s), { grad: s[0], value: r };
+    return yp(s), { grad: s[0], value: r };
   };
 }
-function epe(e) {
-  return O(hr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
+function fpe(e) {
+  return O(fr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
     O(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
     let s = M.gradients(() => e(...t), t, n);
-    return n != null && pn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), gp(s.grads), s;
+    return n != null && pn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), yp(s.grads), s;
   };
 }
-function XR(e, t) {
-  O(hr(e), () => "The f passed in variableGrads(f) must be a function"), O(t == null || Array.isArray(t) && t.every((l) => l instanceof gd), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
+function eD(e, t) {
+  O(fr(e), () => "The f passed in variableGrads(f) must be a function"), O(t == null || Array.isArray(t) && t.every((l) => l instanceof gd), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
   let n = t != null;
   if (!n) {
     t = [];
@@ -7356,182 +7356,182 @@ function XR(e, t) {
 function js(e) {
   return M.customGrad(e);
 }
-function gp(e) {
+function yp(e) {
   if (e.filter((n) => n == null).length > 0)
     throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
     the f you passed encloses all operations that lead from x to y.`);
 }
-function YR(e) {
+function tD(e) {
   let n = { x: _(e, "x", "neg") };
-  return M.runKernel(Io, n);
+  return M.runKernel(Co, n);
 }
-var kt = L({ neg_: YR });
-function QR(e) {
+var kt = L({ neg_: tD });
+function nD(e) {
   let n = { x: _(e, "x", "softplus") };
   return M.runKernel($l, n);
 }
-var zl = L({ softplus_: QR });
-function ZR(e) {
+var zl = L({ softplus_: nD });
+function sD(e) {
   let t = _(e, "x", "logSigmoid");
   return js((s) => ({ value: kt(zl(kt(s))), gradFunc: (i) => V(i, qs(kt(s))) }))(t);
 }
-var tpe = L({ logSigmoid_: ZR });
-function JR(e, t = null, n = false) {
+var mpe = L({ logSigmoid_: sD });
+function rD(e, t = null, n = false) {
   let r = { x: _(e, "x", "max") }, a = { reductionIndices: t, keepDims: n };
-  return M.runKernel(Ua, r, a);
+  return M.runKernel(Ha, r, a);
 }
-var As = L({ max_: JR });
-function eD(e, t) {
+var As = L({ max_: rD });
+function aD(e, t) {
   let n = _(e, "a", "sub"), s = _(t, "b", "sub");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(ci, r);
+  return M.runKernel(hi, r);
 }
-var ge = L({ sub_: eD });
-function tD(e, t = null, n = false) {
+var ge = L({ sub_: aD });
+function iD(e, t = null, n = false) {
   let s = _(e, "x", "sum");
   s.dtype === "bool" && (s = ce(s, "int32"));
   let r = { x: s }, a = { axis: t, keepDims: n };
-  return M.runKernel(oi, r, a);
+  return M.runKernel(ci, r, a);
 }
-var ve = L({ sum_: tD });
-function nD(e, t = -1) {
+var ve = L({ sum_: iD });
+function oD(e, t = -1) {
   let n = _(e, "logits", "logSoftmax");
   if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
     throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and axis was ${t}`);
   return js((r, a) => {
-    let o = As(r, t, true), u = ge(r, o), l = ge(ce(u, "float32"), Yn(ve(Xn(u), t, true)));
+    let o = As(r, t, true), u = ge(r, o), l = ge(ce(u, "float32"), Qn(ve(Yn(u), t, true)));
     return a([l]), { value: l, gradFunc: (p, d) => {
-      let [h] = d, f = true, m = Xn(h);
+      let [h] = d, f = true, m = Yn(h);
       return ge(p, V(ve(p, t, f), m));
     } };
   })(n);
 }
-var iI = L({ logSoftmax_: nD });
-function Qg(e, t) {
+var cI = L({ logSoftmax_: oD });
+function Jg(e, t) {
   for (let n = 0; n < e.length; ++n)
     if (e[e.length - n - 1] !== t - 1 - n)
       return false;
   return true;
 }
-function oI(e, t, n) {
+function dI(e, t, n) {
   let s = e.length + t.length, r = [], a = 0, i = 0;
   for (let o = 0; o < s; o++)
     n.indexOf(o) === -1 ? r.push(e[a++]) : r.push(t[i++]);
   return r;
 }
-function uI(e, t) {
+function pI(e, t) {
   let n = [], s = e.length;
   for (let a = 0; a < s; a++)
     t.indexOf(a) === -1 && n.push(e[a]);
   let r = t.map((a) => e[a]);
   return [n, r];
 }
-function da(e, t) {
+function pa(e, t) {
   let n = t.map((s) => 1);
-  return oI(e, n, t);
+  return dI(e, n, t);
 }
-function sD(e, t, n) {
-  O(Qg(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
+function uD(e, t, n) {
+  O(Jg(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
 }
-function lI(e, t) {
-  if (Qg(e, t))
+function hI(e, t) {
+  if (Jg(e, t))
     return null;
   let n = [];
   for (let s = 0; s < t; ++s)
     e.indexOf(s) === -1 && n.push(s);
   return e.forEach((s) => n.push(s)), n;
 }
-function Zg(e) {
+function eb(e) {
   return e.map((t, n) => [n, t]).sort((t, n) => t[1] - n[1]).map((t) => t[0]);
 }
-function rD(e, t) {
+function lD(e, t) {
   let n = [];
   for (let s = t - e; s < t; ++s)
     n.push(s);
   return n;
 }
-function aD(e, t = null, n = false) {
-  let s = _(e, "x", "logSumExp"), r = ts(t, s.shape), a = As(s, r, true), i = ge(s, a), o = Xn(i), u = ve(o, r), l = Yn(u), c = ie(G(a, l.shape), l);
+function cD(e, t = null, n = false) {
+  let s = _(e, "x", "logSumExp"), r = ts(t, s.shape), a = As(s, r, true), i = ge(s, a), o = Yn(i), u = ve(o, r), l = Qn(u), c = ie(G(a, l.shape), l);
   if (n) {
-    let p = da(c.shape, r);
+    let p = pa(c.shape, r);
     return G(c, p);
   }
   return c;
 }
-var iD = L({ logSumExp_: aD });
-function oD(e, t) {
+var dD = L({ logSumExp_: cD });
+function pD(e, t) {
   let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
   ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(ko, r);
+  return M.runKernel(So, r);
 }
-var Ds = L({ logicalAnd_: oD });
-function uD(e) {
+var Ds = L({ logicalAnd_: pD });
+function hD(e) {
   let n = { x: _(e, "x", "logicalNot", "bool") };
   return M.runKernel(xl, n);
 }
-var Jg = L({ logicalNot_: uD });
-function lD(e, t) {
+var tb = L({ logicalNot_: hD });
+function fD(e, t) {
   let n = _(e, "a", "logicalOr", "bool"), s = _(t, "b", "logicalOr", "bool");
   ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(Jd, r);
+  return M.runKernel(tp, r);
 }
-var cI = L({ logicalOr_: lD });
-function cD(e, t) {
+var fI = L({ logicalOr_: fD });
+function mD(e, t) {
   let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
-  return ot(n.shape, s.shape), Ds(cI(e, t), Jg(Ds(e, t)));
+  return ot(n.shape, s.shape), Ds(fI(e, t), tb(Ds(e, t)));
 }
-var npe = L({ logicalXor_: cD });
-function dD(e, t, n, s, r) {
+var gpe = L({ logicalXor_: mD });
+function gD(e, t, n, s, r) {
   let a = _(e, "x", "maxPool"), i = 1, o = a, u = false;
   a.rank === 3 && (u = true, o = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(o.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.rank}.`), O(Ps(n, i), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`), hn("maxPool", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(Ha, l, c);
+  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(ja, l, c);
   return u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
-var eb = L({ maxPool_: dD });
-function pD(e, t = [1, 1, 1], n, s, r, a = "NDHWC") {
+var nb = L({ maxPool_: gD });
+function bD(e, t = [1, 1, 1], n, s, r, a = "NDHWC") {
   let i = _(e, "x", "maxPool3d"), o = i, u = false;
   i.rank === 4 && (u = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), hn("maxPool3d", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(tp, l, c);
+  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(sp, l, c);
   return u ? G(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
 }
-var dI = L({ maxPool3d_: pD });
-function hD(e, t, n, s, r = false) {
-  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = M.runKernel(Sg, i, o);
+var mI = L({ maxPool3d_: bD });
+function yD(e, t, n, s, r = false) {
+  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = M.runKernel(Ng, i, o);
   return { result: u[0], indexes: u[1] };
 }
-var fD = L({ maxPoolWithArgmax_: hD });
-function mD(e, t) {
+var vD = L({ maxPoolWithArgmax_: yD });
+function xD(e, t) {
   let n = _(e, "a", "maximum"), s = _(t, "b", "maximum");
   [n, s] = vt(n, s), n.dtype === "bool" && (n = ce(n, "int32"), s = ce(s, "int32")), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(Ga, r);
+  return M.runKernel(qa, r);
 }
-var $r = L({ maximum_: mD });
-function gD(e, t = null, n = false) {
+var _r = L({ maximum_: xD });
+function wD(e, t = null, n = false) {
   let r = { x: _(e, "x", "mean") }, a = { axis: t, keepDims: n };
-  return M.runKernel(qa, r, a);
+  return M.runKernel(Ka, r, a);
 }
-var St = L({ mean_: gD });
+var St = L({ mean_: wD });
 function $t(e, t = "float32") {
   if (t === "complex64") {
     let s = $t(e, "float32"), r = $t(e, "float32");
-    return oa(s, r);
+    return ua(s, r);
   }
-  let n = Hd(pt(e), t);
+  let n = jd(pt(e), t);
   return M.makeTensor(n, e, t);
 }
 function Mn(e, t = "float32") {
   if (t === "complex64") {
     let s = Mn(e, "float32"), r = $t(e, "float32");
-    return oa(s, r);
+    return ua(s, r);
   }
-  let n = ng(pt(e), t);
+  let n = rg(pt(e), t);
   return M.makeTensor(n, e, t);
 }
-function spe(e, t, { indexing: n = "xy" } = {}) {
+function bpe(e, t, { indexing: n = "xy" } = {}) {
   if (n !== "xy" && n !== "ij")
     throw new TypeError(`${n} is not a valid third argument to meshgrid`);
   if (e === void 0)
@@ -7542,19 +7542,19 @@ function spe(e, t, { indexing: n = "xy" } = {}) {
   let r = _(t, "y", "meshgrid", t instanceof et ? t.dtype : "float32"), a = pt(s.shape), i = pt(r.shape);
   return n === "xy" ? (s = G(s, [1, -1]), r = G(r, [-1, 1]), [We(Mn([i, 1], s.dtype), s), We(r, Mn([1, a], r.dtype))]) : (s = G(s, [-1, 1]), r = G(r, [1, -1]), [We(s, Mn([1, i], s.dtype)), We(Mn([a, 1], r.dtype), r)]);
 }
-function bD(e, t = null, n = false) {
+function kD(e, t = null, n = false) {
   let r = { x: _(e, "x", "min") }, a = { axis: t, keepDims: n };
-  return M.runKernel(ja, r, a);
+  return M.runKernel(Xa, r, a);
 }
-var fm = L({ min_: bD });
-function yD(e, t) {
+var gm = L({ min_: kD });
+function ID(e, t) {
   let n = _(e, "a", "minimum"), s = _(t, "b", "minimum");
   [n, s] = vt(n, s), n.dtype === "bool" && (n = ce(n, "int32"), s = ce(s, "int32")), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(Ka, r);
+  return M.runKernel(Ya, r);
 }
-var bp = L({ minimum_: yD });
-function vD(e, t, n) {
+var vp = L({ minimum_: ID });
+function SD(e, t, n) {
   O(n === "reflect" || n === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);
   let s = _(e, "x", "mirrorPad");
   if (s.rank === 0)
@@ -7564,31 +7564,31 @@ function vD(e, t, n) {
   for (let o = 0; o < s.rank; o++)
     O(t[o].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), O(t[o][0] >= 0 && t[o][0] <= s.shape[o] - r && t[o][1] >= 0 && t[o][1] <= s.shape[o] - r, () => `Padding in dimension ${o} cannot be greater than or equal to ${s.shape[o] - r} or less than 0 for input of shape ${s.shape}`);
   let a = { paddings: t, mode: n }, i = { x: s };
-  return M.runKernel(Xa, i, a);
+  return M.runKernel(Qa, i, a);
 }
-var xD = L({ mirrorPad_: vD });
-function wD(e, t) {
+var CD = L({ mirrorPad_: SD });
+function ND(e, t) {
   let n = _(e, "a", "mod"), s = _(t, "b", "mod");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
   return M.runKernel(wl, r);
 }
-var kD = L({ mod_: wD });
-function ID(e) {
+var TD = L({ mod_: ND });
+function $D(e) {
   let t = _(e, "x", "square"), n = {};
   return M.runKernel("Square", { x: t }, n);
 }
-var ct = L({ square_: ID });
-function SD(e, t = null, n = false) {
+var ct = L({ square_: $D });
+function _D(e, t = null, n = false) {
   e = _(e, "x", "moments");
   let s = ts(t, e.shape), r = St(e, s, n), a = r.shape;
-  n || (a = da(r.shape, s));
+  n || (a = pa(r.shape, s));
   let i = ct(ge(ce(e, "float32"), G(r, a))), o = St(i, s, n);
   return { mean: r, variance: o };
 }
-var tb = L({ moments_: SD });
-function CD(e, t, n, s) {
-  let r = _(t, "data", "multiRNNCell"), a = Gu(n, "c", "multiRNNCell"), i = Gu(s, "h", "multiRNNCell"), o = r, u = [];
+var sb = L({ moments_: _D });
+function AD(e, t, n, s) {
+  let r = _(t, "data", "multiRNNCell"), a = Hu(n, "c", "multiRNNCell"), i = Hu(s, "h", "multiRNNCell"), o = r, u = [];
   for (let p = 0; p < e.length; p++) {
     let d = e[p](o, a[p], i[p]);
     u.push(d[0]), u.push(d[1]), o = d[1];
@@ -7598,106 +7598,106 @@ function CD(e, t, n, s) {
     l.push(u[p]), c.push(u[p + 1]);
   return [l, c];
 }
-var rpe = L({ multiRNNCell_: CD });
-function ND(e, t, n, s = false) {
+var ype = L({ multiRNNCell_: AD });
+function ED(e, t, n, s = false) {
   let r = _(e, "logits", "multinomial"), a = r.size, i = r.rank;
   if (a < 2)
     throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${a}.`);
   if (i > 2)
     throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);
   n = n || Math.random();
-  let u = { logits: i === 1 ? G(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = M.runKernel(Cg, u, l);
+  let u = { logits: i === 1 ? G(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = M.runKernel(Tg, u, l);
   return i === 1 ? G(c, [c.size]) : c;
 }
-var TD = L({ multinomial_: ND });
-function $D(e, t) {
+var RD = L({ multinomial_: ED });
+function DD(e, t) {
   let n = _(e, "a", "notEqual", "string_or_numeric"), s = _(t, "b", "notEqual", "string_or_numeric");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(So, r);
+  return M.runKernel(No, r);
 }
-var Xu = L({ notEqual_: $D });
-function _D(e) {
+var Yu = L({ notEqual_: DD });
+function FD(e) {
   let n = { x: _(e, "x", "onesLike") };
-  return M.runKernel(To, n);
+  return M.runKernel(_o, n);
 }
-var Qn = L({ onesLike_: _D });
-function AD(e, t) {
+var Zn = L({ onesLike_: FD });
+function OD(e, t) {
   let n = _(e, "v1", "outerProduct"), s = _(t, "v2", "outerProduct");
   O(n.rank === 1 && s.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);
   let r = G(n, [-1, 1]), a = G(s, [1, -1]);
   return We(r, a);
 }
-var ape = L({ outerProduct_: AD });
-function ED(e, t, n = 0) {
+var vpe = L({ outerProduct_: OD });
+function PD(e, t, n = 0) {
   let s = _(e, "x", "pad");
   if (s.rank === 0)
     throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
   let r = { paddings: t, constantValue: n }, a = { x: s };
-  return M.runKernel(Qa, a, r);
+  return M.runKernel(Ja, a, r);
 }
-var fi = L({ pad_: ED });
-function RD(e, t, n = 0) {
-  return O(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), fi(e, [t], n);
+var bi = L({ pad_: PD });
+function zD(e, t, n = 0) {
+  return O(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), bi(e, [t], n);
 }
-var ipe = L({ pad1d_: RD });
-function DD(e, t, n = 0) {
-  return O(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), fi(e, t, n);
+var xpe = L({ pad1d_: zD });
+function MD(e, t, n = 0) {
+  return O(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bi(e, t, n);
 }
-var ope = L({ pad2d_: DD });
-function FD(e, t, n = 0) {
-  return O(t.length === 3 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), fi(e, t, n);
+var wpe = L({ pad2d_: MD });
+function LD(e, t, n = 0) {
+  return O(t.length === 3 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bi(e, t, n);
 }
-var upe = L({ pad3d_: FD });
-function OD(e, t, n = 0) {
-  return O(t.length === 4 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2 && t[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), fi(e, t, n);
+var kpe = L({ pad3d_: LD });
+function BD(e, t, n = 0) {
+  return O(t.length === 4 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2 && t[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), bi(e, t, n);
 }
-var lpe = L({ pad4d_: OD });
-function PD(e, t, n) {
+var Ipe = L({ pad4d_: BD });
+function VD(e, t, n) {
   let s = _(e, "x", "spaceToBatchND");
   O(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), O(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), O(s.shape.reduce((i, o, u) => u > 0 && u <= t.length ? i && (o + n[u - 1][0] + n[u - 1][1]) % t[u - 1] === 0 : i, true), () => `input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);
   let r = { x: s }, a = { blockShape: t, paddings: n };
-  return M.runKernel(Mo, r, a);
+  return M.runKernel(Lo, r, a);
 }
-var nb = L({ spaceToBatchND_: PD });
-function zD(e, t, n, s, r, a, i) {
+var rb = L({ spaceToBatchND_: VD });
+function WD(e, t, n, s, r, a, i) {
   r == null && (r = [1, 1]), a == null && (a = 1), s === 0 && (s = "valid");
   let o = _(e, "x", "maxPool"), u = o, l = false;
   o.rank === 3 && (l = true, u = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(Ps(a, r), () => `Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);
-  let c = jk(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
-  s === "same" ? d = LD([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
-  let h = p[0] === 1 && p[1] === 1, [f, m] = MD([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : nb(u, p, f), v = (n === "avg" ? () => Gg(b, t, a, g, i) : () => eb(b, t, a, g, i))(), x = h ? v : Hg(v, p, m);
+  let c = Qk(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
+  s === "same" ? d = GD([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
+  let h = p[0] === 1 && p[1] === 1, [f, m] = UD([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : rb(u, p, f), v = (n === "avg" ? () => qg(b, t, a, g, i) : () => nb(b, t, a, g, i))(), x = h ? v : jg(v, p, m);
   return l ? G(x, [x.shape[1], x.shape[2], x.shape[3]]) : x;
 }
-function MD(e, t, n) {
+function UD(e, t, n) {
   let s = n.map((c) => c[0]), r = n.map((c) => c[1]), a = e.concat(s, r), i = t.map((c, p) => (c - a[p] % c) % c), o = r.map((c, p) => c + i[p]), u = t.map((c, p) => [s[p], o[p]]), l = t.map((c, p) => [0, i[p]]);
   return [u, l];
 }
-function LD(e, t) {
+function GD(e, t) {
   let s = e.map((i, o) => i + (i - 1) * (t[o] - 1)).map((i) => i - 1), r = s.map((i) => Math.floor(i / 2)), a = s.map((i, o) => i - r[o]);
   return s.map((i, o) => [r[o], a[o]]);
 }
-var cpe = L({ pool_: zD });
-function BD(e, t) {
+var Spe = L({ pool_: WD });
+function HD(e, t) {
   let n = _(e, "base", "pow"), s = _(t, "exp", "pow");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(Za, r);
+  return M.runKernel(ei, r);
 }
-var pa = L({ pow_: BD });
-function VD(e, t) {
+var ha = L({ pow_: HD });
+function qD(e, t) {
   let n = _(e, "x", "prelu"), s = _(t, "alpha", "prelu"), r = { x: n, alpha: s };
-  return M.runKernel(Ja, r);
+  return M.runKernel(ti, r);
 }
-var sb = L({ prelu_: VD });
-function WD(e, t = null, n = false) {
+var ab = L({ prelu_: qD });
+function jD(e, t = null, n = false) {
   let s = _(e, "x", "prod");
   s.dtype === "bool" && (s = ce(s, "int32"));
   let r = { x: s }, a = { axis: t, keepDims: n };
-  return M.runKernel(Ao, r, a);
+  return M.runKernel(ni, r, a);
 }
-var pI = L({ prod_: WD });
-function UD(e, t, n) {
+var gI = L({ prod_: jD });
+function KD(e, t, n) {
   let s = pt(e), r = null;
   if (n == null || n === "float32")
     r = new Float32Array(s);
@@ -7711,13 +7711,13 @@ function UD(e, t, n) {
     r[a] = t();
   return M.makeTensor(r, e, n);
 }
-var dpe = L({ rand_: UD });
-var rb = xa(Wd());
-var ab = class {
+var Cpe = L({ rand_: KD });
+var ib = wa(Gd());
+var ob = class {
   constructor(e, t, n, s, r) {
     this.mean = e, this.stdDev = t, this.dtype = n, this.nextVal = NaN, this.truncated = s, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2);
     let a = r || Math.random();
-    this.random = rb.alea(a.toString());
+    this.random = ib.alea(a.toString());
   }
   nextValue() {
     if (!isNaN(this.nextVal)) {
@@ -7742,11 +7742,11 @@ var ab = class {
     return e <= this.upper && e >= this.lower;
   }
 };
-var GD = class {
+var XD = class {
   constructor(e, t, n, s) {
     this.alpha = e, this.beta = 1 / t, this.dtype = n;
     let r = s || Math.random();
-    this.randu = rb.alea(r.toString()), this.randn = new ab(0, 1, n, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d);
+    this.randu = ib.alea(r.toString()), this.randn = new ob(0, 1, n, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d);
   }
   nextValue() {
     let e, t, n, s, r, a;
@@ -7763,11 +7763,11 @@ var GD = class {
     return this.dtype === "float32" ? e : Math.round(e);
   }
 };
-var HD = class {
+var YD = class {
   constructor(e = 0, t = 1, n, s) {
     if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t - e, this.dtype = n, s == null && (s = Math.random()), typeof s == "number" && (s = s.toString()), !this.canReturnFloat() && this.range <= 1)
       throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);
-    this.random = rb.alea(s);
+    this.random = ib.alea(s);
   }
   convertValue(e) {
     return this.canReturnFloat() ? e : Math.round(e);
@@ -7776,116 +7776,116 @@ var HD = class {
     return this.convertValue(this.min + this.range * this.random());
   }
 };
-function qD(e, t, n = 1, s = "float32", r) {
+function QD(e, t, n = 1, s = "float32", r) {
   if (n == null && (n = 1), s == null && (s = "float32"), s !== "float32" && s !== "int32")
     throw new Error(`Unsupported data type ${s}`);
-  let a = new GD(t, n, s, r), i = De(e, s);
+  let a = new XD(t, n, s, r), i = De(e, s);
   for (let o = 0; o < i.values.length; o++)
     i.values[o] = a.nextValue();
   return i.toTensor();
 }
-var ppe = L({ randomGamma_: qD });
-function jD(e, t = 0, n = 1, s, r) {
+var Npe = L({ randomGamma_: QD });
+function ZD(e, t = 0, n = 1, s, r) {
   if (s != null && s === "bool")
     throw new Error(`Unsupported data type ${s}`);
-  let a = new ab(t, n, s, false, r), i = De(e, s);
+  let a = new ob(t, n, s, false, r), i = De(e, s);
   for (let o = 0; o < i.values.length; o++)
     i.values[o] = a.nextValue();
   return i.toTensor();
 }
-var KD = L({ randomNormal_: jD });
-function XD(e, t = 0, n = 1, s = "float32", r) {
-  let a = De(e, s), i = new HD(t, n, null, r);
+var JD = L({ randomNormal_: ZD });
+function e3(e, t = 0, n = 1, s = "float32", r) {
+  let a = De(e, s), i = new YD(t, n, null, r);
   for (let o = 0; o < a.values.length; o++)
     a.values[o] = i.nextValue();
   return a.toTensor();
 }
-var Ml = L({ randomUniform_: XD });
-function Yu(e, t, n = 1, s = "float32") {
+var Ml = L({ randomUniform_: e3 });
+function Qu(e, t, n = 1, s = "float32") {
   if (n === 0)
     throw new Error("Cannot have a step of zero");
   let r = { start: e, stop: t, step: n, dtype: s };
   return M.runKernel(Il, {}, r);
 }
-function YD(e) {
+function t3(e) {
   let n = { input: _(e, "input", "real") };
-  return M.runKernel(np, n);
+  return M.runKernel(rp, n);
 }
-var wd = L({ real_: YD });
-function QD(e) {
+var wd = L({ real_: t3 });
+function n3(e) {
   let n = { x: _(e, "x", "reciprocal") };
   return M.runKernel(Sl, n);
 }
-var ZD = L({ reciprocal_: QD });
-function JD(e) {
+var s3 = L({ reciprocal_: n3 });
+function r3(e) {
   let n = { x: _(e, "x", "relu") };
-  return M.runKernel(ei, n);
-}
-var Xs = L({ relu_: JD });
-function e3(e) {
-  let n = { x: _(e, "x", "relu6") };
-  return M.runKernel(ni, n);
-}
-var hI = L({ relu6_: e3 });
-function t3(e, t) {
-  let s = { x: _(e, "x", "reverse") }, r = { dims: t };
-  return M.runKernel(Ro, s, r);
-}
-var Zn = L({ reverse_: t3 });
-function n3(e) {
-  let t = _(e, "x", "reverse");
-  return O(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Zn(t, 0);
-}
-var hpe = L({ reverse1d_: n3 });
-function s3(e, t) {
-  let n = _(e, "x", "reverse");
-  return O(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Zn(n, t);
-}
-var fpe = L({ reverse2d_: s3 });
-function r3(e, t) {
-  let n = _(e, "x", "reverse");
-  return O(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Zn(n, t);
-}
-var mpe = L({ reverse3d_: r3 });
-function a3(e, t) {
-  let n = _(e, "x", "reverse");
-  return O(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Zn(n, t);
-}
-var gpe = L({ reverse4d_: a3 });
-function i3(e) {
-  let n = { x: _(e, "x", "round") };
-  return M.runKernel(Do, n);
-}
-var fI = L({ round_: i3 });
-function o3(e) {
-  let n = { x: _(e, "x", "rsqrt", "float32") };
   return M.runKernel(si, n);
 }
-var mI = L({ rsqrt_: o3 });
+var Ys = L({ relu_: r3 });
+function a3(e) {
+  let n = { x: _(e, "x", "relu6") };
+  return M.runKernel(ai, n);
+}
+var bI = L({ relu6_: a3 });
+function i3(e, t) {
+  let s = { x: _(e, "x", "reverse") }, r = { dims: t };
+  return M.runKernel(Do, s, r);
+}
+var Jn = L({ reverse_: i3 });
+function o3(e) {
+  let t = _(e, "x", "reverse");
+  return O(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Jn(t, 0);
+}
+var Tpe = L({ reverse1d_: o3 });
+function u3(e, t) {
+  let n = _(e, "x", "reverse");
+  return O(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Jn(n, t);
+}
+var $pe = L({ reverse2d_: u3 });
+function l3(e, t) {
+  let n = _(e, "x", "reverse");
+  return O(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Jn(n, t);
+}
+var _pe = L({ reverse3d_: l3 });
+function c3(e, t) {
+  let n = _(e, "x", "reverse");
+  return O(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Jn(n, t);
+}
+var Ape = L({ reverse4d_: c3 });
+function d3(e) {
+  let n = { x: _(e, "x", "round") };
+  return M.runKernel(Fo, n);
+}
+var yI = L({ round_: d3 });
+function p3(e) {
+  let n = { x: _(e, "x", "rsqrt", "float32") };
+  return M.runKernel(ii, n);
+}
+var vI = L({ rsqrt_: p3 });
 function we(e, t) {
   if ((Qt(e) && t !== "string" || Array.isArray(e)) && t !== "complex64")
     throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
   if (t === "string" && Qt(e) && !(e instanceof Uint8Array))
     throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
-  return Nr(e, [], [], t);
+  return Tr(e, [], [], t);
 }
-function u3(e) {
+function h3(e) {
   let n = { x: _(e, "x", "selu") };
   return M.runKernel(Nl, n);
 }
-var gI = L({ selu_: u3 });
-function l3(e, t, n, s, r, a = [1, 1], i = "NHWC") {
+var xI = L({ selu_: h3 });
+function f3(e, t, n, s, r, a = [1, 1], i = "NHWC") {
   let o = _(e, "x", "separableConv2d"), u = _(t, "depthwiseFilter", "separableConv2d"), l = _(n, "pointwiseFilter", "separableConv2d"), c = o, p = false;
   if (o.rank === 3 && (p = true, c = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), i === "NCHW")
     throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
   O(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), O(u.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`), O(l.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);
   let d = u.shape[2], h = u.shape[3];
   O(l.shape[2] === d * h, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${d * h}, but got ${l.shape[2]}.`);
-  let f = hp(c, u, s, r, i, a), g = ca(f, l, 1, "valid", i);
+  let f = mp(c, u, s, r, i, a), g = da(f, l, 1, "valid", i);
   return p ? G(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
 }
-var c3 = L({ separableConv2d_: l3 });
-async function d3(e, t) {
+var m3 = L({ separableConv2d_: f3 });
+async function g3(e, t) {
   let n = _(e, "x", "setdiff1d"), s = _(t, "y", "setdiff1d");
   O(n.dtype === s.dtype, () => `x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`), O(n.rank === 1, () => `x should be 1D tensor, but got x (${n.shape}).`), O(s.rank === 1, () => `y should be 1D tensor, but got y (${s.shape}).`);
   let r = await n.data(), a = await s.data(), i = new Set(a), o = 0;
@@ -7896,69 +7896,69 @@ async function d3(e, t) {
     i.has(r[c]) || (u.values[p] = r[c], l.values[p] = c, p++);
   return [u.toTensor(), l.toTensor()];
 }
-var p3 = d3;
-function h3(e) {
+var b3 = g3;
+function y3(e) {
   let n = { x: _(e, "x", "sign") };
   return M.runKernel(Tl, n);
 }
-var f3 = L({ sign_: h3 });
-function m3(e) {
+var v3 = L({ sign_: y3 });
+function x3(e) {
   let n = { x: _(e, "x", "sin", "float32") };
-  return M.runKernel(ri, n);
+  return M.runKernel(oi, n);
 }
-var bI = L({ sin_: m3 });
-function g3(e) {
+var wI = L({ sin_: x3 });
+function w3(e) {
   let n = { x: _(e, "x", "sinh") };
-  return M.runKernel(zo, n);
+  return M.runKernel(Mo, n);
 }
-var yI = L({ sinh_: g3 });
-function b3(e, t, n) {
+var kI = L({ sinh_: w3 });
+function k3(e, t, n) {
   let s = _(e, "x", "slice1d");
-  return O(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), He(s, [t], [n]);
+  return O(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), qe(s, [t], [n]);
 }
-var ib = L({ slice1d_: b3 });
-function y3(e, t, n) {
+var ub = L({ slice1d_: k3 });
+function I3(e, t, n) {
   let s = _(e, "x", "slice2d");
-  return O(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
+  return O(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
 }
-var vI = L({ slice2d_: y3 });
-function v3(e, t, n) {
+var II = L({ slice2d_: I3 });
+function S3(e, t, n) {
   let s = _(e, "x", "slice3d");
-  return O(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
+  return O(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
 }
-var ob = L({ slice3d_: v3 });
-function x3(e, t, n) {
+var lb = L({ slice3d_: S3 });
+function C3(e, t, n) {
   let s = _(e, "x", "slice4d");
-  return O(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
+  return O(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), qe(s, t, n);
 }
-var kd = L({ slice4d_: x3 });
-function w3(e, t = -1) {
+var kd = L({ slice4d_: C3 });
+function N3(e, t = -1) {
   let n = _(e, "logits", "softmax", "float32");
   if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
     throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);
   let s = { logits: n }, r = { dim: t };
-  return M.runKernel(ui, s, r);
+  return M.runKernel(di, s, r);
 }
-var ub = L({ softmax_: w3 });
-function k3(e) {
+var cb = L({ softmax_: N3 });
+function T3(e) {
   O(e.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);
   let t = { input: e };
-  return M.runKernel(yg, t);
+  return M.runKernel(xg, t);
 }
-var lb = L({ fft_: k3 });
-function I3(e) {
+var db = L({ fft_: T3 });
+function $3(e) {
   O(e.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);
   let t = { input: e };
-  return M.runKernel(vg, t);
+  return M.runKernel(wg, t);
 }
-var Id = L({ ifft_: I3 });
-function S3(e) {
+var Id = L({ ifft_: $3 });
+function _3(e) {
   let t = e.shape[e.shape.length - 1], n = e.size / t, s;
   if (t <= 2) {
     let r = G(e, [n, t]);
     s = Id(r);
   } else {
-    let r = [n, 2 * (t - 1)], a = G(wd(e), [n, t]), i = G(Kg(e), [n, t]), o = Zn(He(a, [0, 1], [n, t - 2]), 1), u = V(Zn(He(i, [0, 1], [n, t - 2]), 1), we(-1)), l = Ft([a, o], 1), c = Ft([i, u], 1), p = G(oa(l, c), [r[0], r[1]]);
+    let r = [n, 2 * (t - 1)], a = G(wd(e), [n, t]), i = G(Yg(e), [n, t]), o = Jn(qe(a, [0, 1], [n, t - 2]), 1), u = V(Jn(qe(i, [0, 1], [n, t - 2]), 1), we(-1)), l = Ft([a, o], 1), c = Ft([i, u], 1), p = G(ua(l, c), [r[0], r[1]]);
     s = Id(p);
   }
   if (s = wd(s), e.rank === 3 && e.shape[0] !== 0) {
@@ -7967,114 +7967,114 @@ function S3(e) {
   }
   return s;
 }
-var xI = L({ irfft_: S3 });
-function C3(e, t, n = 0) {
+var SI = L({ irfft_: _3 });
+function A3(e, t, n = 0) {
   let r = { x: _(e, "x", "split") }, a = { numOrSizeSplits: t, axis: n };
-  return M.runKernel(Lo, r, a);
+  return M.runKernel(Bo, r, a);
 }
-var Bn = L({ split_: C3 });
-function N3(e, t) {
+var Bn = L({ split_: A3 });
+function E3(e, t) {
   O(e.dtype === "float32", () => `The dtype for rfft() must be real value but got ${e.dtype}`);
   let n = e.shape[e.shape.length - 1], s = e.size / n, r;
   if (t != null && t < n) {
     let f = e.shape.map((g) => 0), m = e.shape.map((g) => g);
-    m[e.shape.length - 1] = t, r = He(e, f, m), n = t;
+    m[e.shape.length - 1] = t, r = qe(e, f, m), n = t;
   } else if (t != null && t > n) {
     let f = e.shape.map((m) => m);
     f[e.shape.length - 1] = t - n, r = Ft([e, $t(f)], e.shape.length - 1), n = t;
   } else
     r = e;
-  let a = je(r), i = G(oa(r, a), [s, n]), o = lb(i), u = Math.floor(n / 2) + 1, l = wd(o), c = Kg(o), p = Bn(l, [u, n - u], l.shape.length - 1), d = Bn(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
-  return h[r.shape.length - 1] = u, G(oa(p[0], d[0]), h);
+  let a = je(r), i = G(ua(r, a), [s, n]), o = db(i), u = Math.floor(n / 2) + 1, l = wd(o), c = Yg(o), p = Bn(l, [u, n - u], l.shape.length - 1), d = Bn(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
+  return h[r.shape.length - 1] = u, G(ua(p[0], d[0]), h);
 }
-var cb = L({ rfft_: N3 });
-function T3(e) {
+var pb = L({ rfft_: E3 });
+function R3(e) {
   let n = { x: _(e, "x", "sqrt", "float32") };
-  return M.runKernel(ii, n);
+  return M.runKernel(li, n);
 }
-var dn = L({ sqrt_: T3 });
-function $3(e, t) {
+var dn = L({ sqrt_: R3 });
+function D3(e, t) {
   let n = _(e, "a", "squaredDifference"), s = _(t, "b", "squaredDifference");
   [n, s] = vt(n, s), ot(n.shape, s.shape);
   let r = { a: n, b: s }, a = {};
-  return M.runKernel(li, r, a);
+  return M.runKernel(pi, r, a);
 }
-var wI = L({ squaredDifference_: $3 });
-function _3(e, t) {
+var CI = L({ squaredDifference_: D3 });
+function F3(e, t) {
   let n = _(e, "x", "squeeze");
-  return G(n, Gw(n.shape, t).newShape);
+  return G(n, Kw(n.shape, t).newShape);
 }
-var mr = L({ squeeze_: _3 });
-function A3(e, t = 0) {
-  let n = Gu(e, "tensors", "stack", "string_or_numeric");
+var gr = L({ squeeze_: F3 });
+function O3(e, t = 0) {
+  let n = Hu(e, "tensors", "stack", "string_or_numeric");
   O(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && O(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
   let s = n, r = { axis: t };
-  return M.runKernel(_o, s, r);
+  return M.runKernel(Eo, s, r);
 }
-var Jn = L({ stack_: A3 });
-function E3(e, t = 0) {
+var es = L({ stack_: O3 });
+function P3(e, t = 0) {
   let s = { x: _(e, "x", "step") }, r = { alpha: t };
-  return M.runKernel(hi, s, r);
+  return M.runKernel(gi, s, r);
 }
-var yp = L({ step_: E3 });
-function R3(e, t, n, s, r = 0, a = 0, i = 0, o = 0, u = 0) {
+var xp = L({ step_: P3 });
+function z3(e, t, n, s, r = 0, a = 0, i = 0, o = 0, u = 0) {
   let c = { x: _(e, "x", "stridedSlice", "string_or_numeric") }, p = { begin: t, end: n, strides: s, beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
-  return M.runKernel(Bo, c, p);
+  return M.runKernel(Vo, c, p);
 }
-var D3 = L({ stridedSlice_: R3 });
-function F3(e) {
+var M3 = L({ stridedSlice_: z3 });
+function L3(e) {
   let n = { x: _(e, "x", "tan", "float32") };
-  return M.runKernel(Vo, n);
+  return M.runKernel(Wo, n);
 }
-var O3 = L({ tan_: F3 });
+var B3 = L({ tan_: L3 });
 function Zt(e, t) {
-  wa(e);
+  ka(e);
   let n = Rs(e, t);
   if (n.length !== 1)
     throw new Error("tensor1d() requires values to be a flat/TypedArray");
-  return Nr(e, null, n, t);
+  return Tr(e, null, n, t);
 }
-function Ki(e, t, n) {
-  if (wa(e), t != null && t.length !== 2)
+function Yi(e, t, n) {
+  if (ka(e), t != null && t.length !== 2)
     throw new Error("tensor2d() requires shape to have two numbers");
   let s = Rs(e, n);
   if (s.length !== 2 && s.length !== 1)
     throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
-  return Nr(e, t, s, n);
+  return Tr(e, t, s, n);
 }
-function bpe(e, t, n) {
-  if (wa(e), t != null && t.length !== 4)
+function Epe(e, t, n) {
+  if (ka(e), t != null && t.length !== 4)
     throw new Error("tensor4d() requires shape to have four numbers");
   let s = Rs(e, n);
   if (s.length !== 4 && s.length !== 1)
     throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
-  return Nr(e, t, s, n);
+  return Tr(e, t, s, n);
 }
-function ype(e, t, n) {
-  if (wa(e), t != null && t.length !== 5)
+function Rpe(e, t, n) {
+  if (ka(e), t != null && t.length !== 5)
     throw new Error("tensor5d() requires shape to have five numbers");
   let s = Rs(e, n);
   if (s.length !== 5 && s.length !== 1)
     throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
-  return Nr(e, t, s, n);
+  return Tr(e, t, s, n);
 }
-function vpe(e, t, n) {
-  if (wa(e), t != null && t.length !== 6)
+function Dpe(e, t, n) {
+  if (ka(e), t != null && t.length !== 6)
     throw new Error("tensor6d() requires shape to have six numbers");
   let s = Rs(e, n);
   if (s.length !== 6 && s.length !== 1)
     throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
   if (s.length === 1 && t == null)
     throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
-  return t = t || s, Nr(e, t, s, n);
+  return t = t || s, Tr(e, t, s, n);
 }
-function P3(e, t = 1, n = true) {
+function V3(e, t = 1, n = true) {
   let s = _(e, "x", "topk");
   if (s.rank === 0)
     throw new Error("topk() expects the input to be of rank 1 or higher");
@@ -8083,44 +8083,44 @@ function P3(e, t = 1, n = true) {
     throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);
   if (t > r)
     throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);
-  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Wo, a, i);
+  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Uo, a, i);
   return { values: o, indices: u };
 }
-var z3 = L({ topk_: P3 });
-function M3(e, t = 0, n = 1, s, r) {
+var W3 = L({ topk_: V3 });
+function U3(e, t = 0, n = 1, s, r) {
   if (s != null && s === "bool")
     throw new Error("Unsupported data type $ { dtype }");
-  let a = new ab(t, n, s, true, r), i = De(e, s);
+  let a = new ob(t, n, s, true, r), i = De(e, s);
   for (let o = 0; o < i.values.length; o++)
     i.values[o] = a.nextValue();
   return i.toTensor();
 }
-var db = L({ truncatedNormal_: M3 });
-function L3(e, t = 0) {
+var hb = L({ truncatedNormal_: U3 });
+function G3(e, t = 0) {
   let n = _(e, "x", "unique", "string_or_numeric");
   O(n.rank > 0, () => "The input tensor must be at least 1D");
-  let s = { x: n }, r = { axis: t }, [a, i] = M.runKernel(Ag, s, r);
+  let s = { x: n }, r = { axis: t }, [a, i] = M.runKernel(Rg, s, r);
   return { values: a, indices: i };
 }
-var hx = L({ unique_: L3 });
-function B3(e, t, n) {
+var mx = L({ unique_: G3 });
+function H3(e, t, n) {
   let s = _(e, "x", "unsortedSegmentSum"), r = _(t, "segmentIds", "unsortedSegmentSum", "int32");
-  O(Yi(n), () => "numSegments must be of dtype int");
+  O(Zi(n), () => "numSegments must be of dtype int");
   let a = { x: s, segmentIds: r }, i = { numSegments: n };
-  return M.runKernel(up, a, i);
+  return M.runKernel(cp, a, i);
 }
-var V3 = L({ unsortedSegmentSum_: B3 });
-function W3(e, t = 0) {
+var q3 = L({ unsortedSegmentSum_: H3 });
+function j3(e, t = 0) {
   let n = _(e, "x", "unstack", "string_or_numeric");
   O(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
   let s = { value: n }, r = { axis: t };
-  return M.runKernel(Go, s, r);
+  return M.runKernel(Ho, s, r);
 }
-var Fs = L({ unstack_: W3 });
-function U3(e, t = true, n, s) {
+var Fs = L({ unstack_: j3 });
+function K3(e, t = true, n, s) {
   return M.makeVariable(e, t, n, s);
 }
-function kI(e, t) {
+function NI(e, t) {
   let n = [];
   for (let a = 0; a < t.length; a++)
     t[a] && n.push(a);
@@ -8131,44 +8131,44 @@ function kI(e, t) {
   }
   return r.toTensor();
 }
-async function G3(e) {
-  let t = _(e, "condition", "whereAsync", "bool"), n = await t.data(), s = kI(t.shape, n);
+async function X3(e) {
+  let t = _(e, "condition", "whereAsync", "bool"), n = await t.data(), s = NI(t.shape, n);
   return e !== t && t.dispose(), s;
 }
-var II = G3;
-async function H3(e, t, n) {
+var TI = X3;
+async function Y3(e, t, n) {
   let s = _(e, "tensor", "boolMask"), r = _(t, "mask", "boolMask", "bool"), a = n == null ? 0 : n, i = r.rank, o = s.shape;
   O(i > 0, () => "mask cannot be scalar"), pn(o.slice(a, a + i), r.shape, "mask's shape must match the first K dimensions of tensor's shape,");
   let u = 1;
   for (let m = a; m < a + i; m++)
     u *= o[m];
-  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = G(s, l), p = G(r, [-1]), d = await II(p), h = mr(d, [1]), f = Ku(c, h, a);
+  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = G(s, l), p = G(r, [-1]), d = await TI(p), h = gr(d, [1]), f = Xu(c, h, a);
   return e !== s && s.dispose(), t !== r && r.dispose(), h.dispose(), c.dispose(), p.dispose(), d.dispose(), f;
 }
-var xpe = H3;
-function q3(e, t = "euclidean", n = null, s = false) {
+var Fpe = Y3;
+function Q3(e, t = "euclidean", n = null, s = false) {
   e = _(e, "x", "norm");
-  let r = SI(e, t, n), a = r.shape;
+  let r = $I(e, t, n), a = r.shape;
   if (s) {
     let i = ts(n, e.shape);
-    a = da(r.shape, i);
+    a = pa(r.shape, i);
   }
   return G(r, a);
 }
-function SI(e, t, n = null) {
+function $I(e, t, n = null) {
   if (e.rank === 0)
     return Mt(e);
   if (e.rank !== 1 && n === null)
-    return SI(G(e, [-1]), t, n);
+    return $I(G(e, [-1]), t, n);
   if (e.rank === 1 || typeof n == "number" || Array.isArray(n) && n.length === 1) {
     if (t === 1)
       return ve(Mt(e), n);
     if (t === 1 / 0)
       return As(Mt(e), n);
     if (t === -1 / 0)
-      return fm(Mt(e), n);
+      return gm(Mt(e), n);
     if (t === "euclidean" || t === 2)
-      return dn(ve(pa(Mt(e), we(2, "int32")), n));
+      return dn(ve(ha(Mt(e), we(2, "int32")), n));
     throw new Error(`Error in norm: invalid ord value: ${t}`);
   }
   if (Array.isArray(n) && n.length === 2) {
@@ -8177,34 +8177,34 @@ function SI(e, t, n = null) {
     if (t === 1 / 0)
       return As(ve(Mt(e), n[1]), n[0]);
     if (t === -1 / 0)
-      return fm(ve(Mt(e), n[1]), n[0]);
+      return gm(ve(Mt(e), n[1]), n[0]);
     if (t === "fro" || t === "euclidean")
       return dn(ve(ct(e), n));
     throw new Error(`Error in norm: invalid ord value: ${t}`);
   }
   throw new Error(`Error in norm: invalid axis: ${n}`);
 }
-var CI = L({ norm_: q3 });
-function j3(e, t, n, s, r = true) {
+var _I = L({ norm_: Q3 });
+function Z3(e, t, n, s, r = true) {
   let a = _(e, "v", "movingAverage"), i = _(t, "x", "movingAverage"), o = _(n, "decay", "movingAverage");
-  uk(a, i), O(kr(a.shape, i.shape), () => "Shape mismatch in v and x");
+  pk(a, i), O(Ir(a.shape, i.shape), () => "Shape mismatch in v and x");
   let u = we(1), l = ge(u, o), c = V(ge(i, a), l);
   if (r) {
     O(s != null, () => "When using zeroDebias: true, step is required.");
     let p = _(s, "step", "movingAverage");
-    c = xe(c, ge(u, pa(o, p)));
+    c = xe(c, ge(u, ha(o, p)));
   }
   return ie(a, c);
 }
-var wpe = L({ movingAverage_: j3 });
-function K3(e, t, n) {
+var Ope = L({ movingAverage_: Z3 });
+function J3(e, t, n) {
   let s = _(e, "indices", "scatterND", "int32"), r = _(t, "updates", "scatterND");
-  Bg(r, s, n);
+  Wg(r, s, n);
   let a = { indices: s, updates: r }, i = { shape: n };
-  return M.runKernel(Fo, a, i);
+  return M.runKernel(Oo, a, i);
 }
-var X3 = L({ scatterND_: K3 });
-function Y3(e, t, n, s) {
+var eF = L({ scatterND_: J3 });
+function tF(e, t, n, s) {
   if (e.dtype !== "int32")
     throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${e.dtype}.`);
   if (e.rank > 2)
@@ -8218,22 +8218,22 @@ function Y3(e, t, n, s) {
   if (t.dtype !== s.dtype)
     throw new Error("sparseValues.dtype must match defaultValues.dtype");
 }
-function Q3(e, t, n, s = 0) {
+function nF(e, t, n, s = 0) {
   let r = _(e, "sparseIndices", "sparseToDense", "int32"), a = _(t, "sparseValues", "sparseToDense"), i = _(s, "defaultValue", "sparseToDense", a.dtype);
-  Y3(r, a, n, i);
+  tF(r, a, n, i);
   let o = { sparseIndices: r, sparseValues: a, defaultValue: i }, u = { outputShape: n };
-  return M.runKernel(ip, o, u);
+  return M.runKernel(up, o, u);
 }
-var NI = L({ sparseToDense_: Q3 });
-function Z3(e, t) {
+var AI = L({ sparseToDense_: nF });
+function sF(e, t) {
   let n = _(t, "indices", "gatherND", "int32"), r = { params: _(e, "x", "gatherND", "string_or_numeric"), indices: n };
-  return M.runKernel(yo, r);
+  return M.runKernel(xo, r);
 }
-var J3 = L({ gatherND_: Z3 });
-function eF(e, t) {
+var rF = L({ gatherND_: sF });
+function aF(e, t) {
   if (t == null)
     return e.shape.slice();
-  if (kr(e.shape, t))
+  if (Ir(e.shape, t))
     return t;
   if (e.shape.length === t.length) {
     let n = [];
@@ -8243,18 +8243,18 @@ function eF(e, t) {
   }
   return t;
 }
-function tF(e, t, n, s) {
+function iF(e, t, n, s) {
   let r = _(e, "x", "dropout");
   if (O(r.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`), O(t >= 0 && t < 1, () => `rate must be a float in the range [0, 1), but got ${t}.`), t === 0)
     return e instanceof et ? r.clone() : r;
-  let a = eF(r, n), i = 1 - t, o = xe(mp(ie(Ml(a, 0, 1, "float32", s), i)), i);
+  let a = aF(r, n), i = 1 - t, o = xe(bp(ie(Ml(a, 0, 1, "float32", s), i)), i);
   return V(r, o);
 }
-var nF = L({ dropout_: tF });
-function sF(e) {
+var oF = L({ dropout_: iF });
+function uF(e) {
   return Math.floor(Math.pow(2, Math.ceil(Math.log(e) / Math.log(2))));
 }
-function TI(e, t, n) {
+function EI(e, t, n) {
   let s = 1 - e % 2, r = new Float32Array(e);
   for (let a = 0; a < e; ++a) {
     let i = 2 * Math.PI * a / (e + s - 1);
@@ -8262,12 +8262,12 @@ function TI(e, t, n) {
   }
   return Zt(r, "float32");
 }
-async function rF(e, t, n = 1) {
+async function lF(e, t, n = 1) {
   let s = _(e, "predictions", "inTopK"), r = _(t, "targets", "inTopK");
   O(s.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`), O(s.rank - 1 === r.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`), pn(s.shape.slice(0, s.shape.length - 1), r.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
   let a = s.shape[s.shape.length - 1];
   O(n > 0 && n <= a, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);
-  let i = await s.data(), o = await r.data(), [u, l] = [i.length / a, a], c = Hw("bool", u);
+  let i = await s.data(), o = await r.data(), [u, l] = [i.length / a, a], c = Xw("bool", u);
   for (let p = 0; p < u; p++) {
     let d = p * l, h = i.subarray(d, d + l), f = [];
     for (let m = 0; m < h.length; m++)
@@ -8281,10 +8281,10 @@ async function rF(e, t, n = 1) {
   }
   return e !== s && s.dispose(), t !== r && r.dispose(), fs(c, r.shape, "bool");
 }
-var kpe = rF;
-var ha = {};
-Ae(ha, { conv2d: () => oF, depthwiseConv2d: () => dF, matMul: () => hF });
-function aF(e, t, n, s, r, a = "NHWC", i) {
+var Ppe = lF;
+var fa = {};
+Ae(fa, { conv2d: () => pF, depthwiseConv2d: () => gF, matMul: () => yF });
+function cF(e, t, n, s, r, a = "NHWC", i) {
   let o = e;
   e.rank === 3 && (o = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
   let u = t;
@@ -8292,42 +8292,42 @@ function aF(e, t, n, s, r, a = "NHWC", i) {
   let l = a === "NHWC" ? o.shape[3] : o.shape[1], c = a === "NHWC" ? u.shape[3] : u.shape[1];
   O(l === n[2], () => `Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`), O(c === n[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`), hn("conv2dDerFilter", r, i);
   let p = { x: o, dy: u }, d = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, filterShape: n };
-  return M.runKernel(cg, p, d);
+  return M.runKernel(pg, p, d);
 }
-var pb = L({ conv2DBackpropFilter_: aF });
-function vp(e, t, n) {
+var fb = L({ conv2DBackpropFilter_: cF });
+function wp(e, t, n) {
   if (n == null || n === "linear")
     return e;
   if (n === "relu")
-    return V(e, yp(t));
+    return V(e, xp(t));
   throw new Error(`Cannot compute gradient for fused activation ${n}.`);
 }
-function xp(e, t) {
+function kp(e, t) {
   let n = t, s = _t(e.shape, t.shape);
   return s.length > 0 && (n = ve(n, s)), G(n, e.shape);
 }
-function wp(e, t, n, s) {
+function Ip(e, t, n, s) {
   if (t === "linear")
     return e;
   if (t === "relu")
-    return Xs(e);
+    return Ys(e);
   if (t === "elu")
-    return fp(e);
+    return gp(e);
   if (t === "relu6")
-    return hI(e);
+    return bI(e);
   if (t === "prelu")
-    return sb(e, n);
+    return ab(e, n);
   if (t === "leakyrelu")
-    return Xg(e, s);
+    return Qg(e, s);
   if (t === "sigmoid")
     return qs(e);
   throw new Error(`Unknown fused activation ${t}.`);
 }
-var kp = (e, t) => !(e > 0) || t === "linear";
-function iF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
-  if (u = u || "linear", kp(M.state.gradientDepth, u) === false) {
-    let k = ca(e, t, n, s, r, a, i);
-    return o != null && (k = ie(k, o)), wp(k, u, l, c);
+var Sp = (e, t) => !(e > 0) || t === "linear";
+function dF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
+  if (u = u || "linear", Sp(M.state.gradientDepth, u) === false) {
+    let k = da(e, t, n, s, r, a, i);
+    return o != null && (k = ie(k, o)), Ip(k, u, l, c);
   }
   let p = _(e, "x", "conv2d", "float32"), d = _(t, "filter", "conv2d", "float32"), h = p, f = false;
   p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), hn("fused conv2d", s, i), O(h.shape[3] === d.shape[2], () => `Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NHWC", () => `Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);
@@ -8336,60 +8336,15 @@ function iF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilat
   let b;
   l != null && (b = _(l, "prelu weights", "fused conv2d"));
   let y = (k, T) => {
-    let [N, E, A, P] = T, R = vp(k, A, u);
-    O(fr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
-    let F = qg(E.shape, R, N, n, s), $ = pb(E, R, N.shape, n, s), z = [F, $];
+    let [N, E, A, P] = T, R = wp(k, A, u);
+    O(mr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
+    let F = Kg(E.shape, R, N, n, s), $ = fb(E, R, N.shape, n, s), z = [F, $];
     if (P != null) {
-      let W = xp(P, R);
+      let W = kp(P, R);
       z.push(W);
     }
     return z;
   }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
-  return o == null ? js((T, N, E) => {
-    let A = M.runKernel(aa, v, x);
-    return E([N, T, A]), f && (A = G(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: y };
-  })(h, d) : js((T, N, E, A) => {
-    let P = M.runKernel(aa, v, x);
-    return A([N, T, P, E]), f && (P = G(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
-  })(h, d, g);
-}
-var oF = L({ fusedConv2d_: iF });
-function uF(e, t, n, s, r, a = [1, 1], i) {
-  let o = e;
-  e.rank === 3 && (o = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
-  let u = t;
-  u.rank === 3 && (u = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
-  let l = { x: o, dy: u }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, filterShape: n };
-  return M.runKernel(fg, l, c);
-}
-var $I = L({ depthwiseConv2dNativeBackpropFilter_: uF });
-function lF(e, t, n, s, r, a = [1, 1], i) {
-  let o = t, u = false;
-  t.rank === 3 && (u = true, o = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
-  let l = { dy: o, filter: n }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, inputShape: e }, p = M.runKernel(mg, l, c);
-  return u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
-}
-var _I = L({ depthwiseConv2dNativeBackpropInput_: lF });
-function cF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
-  if (kp(M.state.gradientDepth, u) === false) {
-    let k = hp(e, t, n, s, r, a, i);
-    return o != null && (k = ie(k, o)), wp(k, u, l, c);
-  }
-  let p = _(e, "x", "depthwiseConv2d", "float32"), d = _(t, "filter", "depthwiseConv2d", "float32"), h = p, f = false;
-  p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), O(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), O(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), hn("fused depthwiseConv2d", s, i);
-  let m = Ol(h.shape, d.shape, n, a, s, i, true), g;
-  o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), ot(m.outShape, g.shape));
-  let b;
-  l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
-  let y = (k, T) => {
-    O(fr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
-    let [N, E, A, P] = T, R = vp(k, A, u), F = _I(E.shape, R, N, n, s, a, i), $ = $I(E, R, N.shape, n, s, a, i);
-    if (P != null) {
-      let z = xp(g, R);
-      return [F, $, z];
-    }
-    return [F, $];
-  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
   return o == null ? js((T, N, E) => {
     let A = M.runKernel(ia, v, x);
     return E([N, T, A]), f && (A = G(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: y };
@@ -8398,11 +8353,56 @@ function cF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilat
     return A([N, T, P, E]), f && (P = G(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
   })(h, d, g);
 }
-var dF = L({ fusedDepthwiseConv2d_: cF });
-function pF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: i, leakyreluAlpha: o }) {
-  if (kp(M.state.gradientDepth, a) === false) {
+var pF = L({ fusedConv2d_: dF });
+function hF(e, t, n, s, r, a = [1, 1], i) {
+  let o = e;
+  e.rank === 3 && (o = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
+  let u = t;
+  u.rank === 3 && (u = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
+  let l = { x: o, dy: u }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, filterShape: n };
+  return M.runKernel(gg, l, c);
+}
+var RI = L({ depthwiseConv2dNativeBackpropFilter_: hF });
+function fF(e, t, n, s, r, a = [1, 1], i) {
+  let o = t, u = false;
+  t.rank === 3 && (u = true, o = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
+  let l = { dy: o, filter: n }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, inputShape: e }, p = M.runKernel(bg, l, c);
+  return u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
+}
+var DI = L({ depthwiseConv2dNativeBackpropInput_: fF });
+function mF({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
+  if (Sp(M.state.gradientDepth, u) === false) {
+    let k = mp(e, t, n, s, r, a, i);
+    return o != null && (k = ie(k, o)), Ip(k, u, l, c);
+  }
+  let p = _(e, "x", "depthwiseConv2d", "float32"), d = _(t, "filter", "depthwiseConv2d", "float32"), h = p, f = false;
+  p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), O(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), O(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), hn("fused depthwiseConv2d", s, i);
+  let m = Ol(h.shape, d.shape, n, a, s, i, true), g;
+  o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), ot(m.outShape, g.shape));
+  let b;
+  l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
+  let y = (k, T) => {
+    O(mr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
+    let [N, E, A, P] = T, R = wp(k, A, u), F = DI(E.shape, R, N, n, s, a, i), $ = RI(E, R, N.shape, n, s, a, i);
+    if (P != null) {
+      let z = kp(g, R);
+      return [F, $, z];
+    }
+    return [F, $];
+  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
+  return o == null ? js((T, N, E) => {
+    let A = M.runKernel(oa, v, x);
+    return E([N, T, A]), f && (A = G(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: y };
+  })(h, d) : js((T, N, E, A) => {
+    let P = M.runKernel(oa, v, x);
+    return A([N, T, P, E]), f && (P = G(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
+  })(h, d, g);
+}
+var gF = L({ fusedDepthwiseConv2d_: mF });
+function bF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: i, leakyreluAlpha: o }) {
+  if (Sp(M.state.gradientDepth, a) === false) {
     let R = We(e, t, n, s);
-    return r != null && (R = ie(R, r)), wp(R, a, i, o);
+    return r != null && (R = ie(R, r)), Ip(R, a, i, o);
   }
   let u = _(e, "a", "fused matMul"), l = _(t, "b", "fused matMul");
   [u, l] = vt(u, l);
@@ -8413,99 +8413,99 @@ function pF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r,
   let N;
   i != null && (N = _(i, "prelu weights", "fused matMul"));
   let E = (R, F) => {
-    let [$, z, W, q] = F, K = vp(G(R, W.shape), W, a), Y, Z;
+    let [$, z, W, q] = F, K = wp(G(R, W.shape), W, a), Y, Z;
     if (!n && !s ? (Y = We(K, z, false, true), Z = We($, K, true, false)) : !n && s ? (Y = We(K, z, false, false), Z = We(K, $, true, false)) : n && !s ? (Y = We(z, K, false, true), Z = We($, K, false, false)) : (Y = We(z, K, true, true), Z = We(K, $, true, true)), r != null) {
-      let te = xp(q, K);
+      let te = kp(q, K);
       return [Y, Z, te];
     } else
       return [Y, Z];
   }, A = { a: x, b: k, bias: T, preluActivationWeights: N }, P = { transposeA: n, transposeB: s, activation: a, leakyreluAlpha: o };
   return r == null ? js((F, $, z) => {
-    let W = M.runKernel(ra, A, P);
+    let W = M.runKernel(aa, A, P);
     return z([F, $, W]), { value: G(W, v), gradFunc: E };
   })(x, k) : js((F, $, z, W) => {
-    let q = M.runKernel(ra, A, P);
+    let q = M.runKernel(aa, A, P);
     return W([F, $, q, z]), { value: G(q, v), gradFunc: E };
   })(x, k, T);
 }
-var hF = L({ fusedMatMul_: pF });
-function fF(e) {
-  return TI(e, 0.54, 0.46);
+var yF = L({ fusedMatMul_: bF });
+function vF(e) {
+  return EI(e, 0.54, 0.46);
 }
-var mF = L({ hammingWindow_: fF });
-function gF(e) {
-  return TI(e, 0.5, 0.5);
+var xF = L({ hammingWindow_: vF });
+function wF(e) {
+  return EI(e, 0.5, 0.5);
 }
-var AI = L({ hannWindow_: gF });
-function bF(e, t, n, s = false, r = 0) {
+var FI = L({ hannWindow_: wF });
+function kF(e, t, n, s = false, r = 0) {
   let a = 0, i = [];
   for (; a + t <= e.size; )
-    i.push(He(e, a, t)), a += n;
+    i.push(qe(e, a, t)), a += n;
   if (s)
     for (; a < e.size; ) {
-      let o = a + t - e.size, u = Ft([He(e, a, t - o), Pl([o], r)]);
+      let o = a + t - e.size, u = Ft([qe(e, a, t - o), Pl([o], r)]);
       i.push(u), a += n;
     }
-  return i.length === 0 ? Ki([], [0, t]) : G(Ft(i), [i.length, t]);
+  return i.length === 0 ? Yi([], [0, t]) : G(Ft(i), [i.length, t]);
 }
-var EI = L({ frame_: bF });
-function yF(e, t, n, s, r = AI) {
-  s == null && (s = sF(t));
-  let a = EI(e, t, n), i = V(a, r(t));
-  return cb(i, s);
+var OI = L({ frame_: kF });
+function IF(e, t, n, s, r = FI) {
+  s == null && (s = uF(t));
+  let a = OI(e, t, n), i = V(a, r(t));
+  return pb(i, s);
 }
-var vF = L({ stft_: yF });
-function xF(e, t, n, s, r = "bilinear", a = 0) {
+var SF = L({ stft_: IF });
+function CF(e, t, n, s, r = "bilinear", a = 0) {
   let i = _(e, "image", "cropAndResize"), o = _(t, "boxes", "cropAndResize", "float32"), u = _(n, "boxInd", "cropAndResize", "int32"), l = o.shape[0];
   O(i.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && o.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`), O(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`), O(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), O(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), O(r === "bilinear" || r === "nearest", () => `method must be bilinear or nearest, but was ${r}`);
   let c = { image: i, boxes: o, boxInd: u }, p = { method: r, extrapolationValue: a, cropSize: s };
-  return M.runKernel(co, c, p);
+  return M.runKernel(ho, c, p);
 }
-var wF = L({ cropAndResize_: xF });
-function kF(e) {
+var NF = L({ cropAndResize_: CF });
+function TF(e) {
   let t = _(e, "image", "flipLeftRight", "float32");
   O(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
   let n = { image: t };
-  return M.runKernel(go, n, {});
+  return M.runKernel(yo, n, {});
 }
-var IF = L({ flipLeftRight_: kF });
-function SF(e) {
+var $F = L({ flipLeftRight_: TF });
+function _F(e) {
   let t = _(e, "image", "grayscaleToRGB"), n = t.rank - 1, s = t.shape[n];
   O(t.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`), O(s === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${s}.`);
   let r = new Array(t.rank);
   return r.fill(1, 0, n), r[n] = 3, ps(t, r);
 }
-var CF = L({ grayscaleToRGB_: SF });
-function NF(e, t, n = 0, s = 0.5) {
+var AF = L({ grayscaleToRGB_: _F });
+function EF(e, t, n = 0, s = 0.5) {
   let r = _(e, "image", "rotateWithOffset", "float32");
   O(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
   let a = { image: r }, i = { radians: t, fillValue: n, center: s };
-  return M.runKernel(qo, a, i);
+  return M.runKernel(jo, a, i);
 }
-var TF = L({ rotateWithOffset_: NF });
-function Yo(e, t, n, s, r, a) {
+var RF = L({ rotateWithOffset_: EF });
+function Qo(e, t, n, s, r, a) {
   s == null && (s = 0.5), r == null && (r = Number.NEGATIVE_INFINITY), a == null && (a = 0);
   let i = e.shape[0];
   return n = Math.min(n, i), O(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), O(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), O(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), O(t.rank === 1, () => "scores must be a 1D tensor"), O(t.shape[0] === i, () => `scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`), O(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
 }
-function $F(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Yo(a, i, n, s, r);
+function DF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
+  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Qo(a, i, n, s, r);
   n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
   let u = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r };
-  return M.runKernel(Co, { boxes: a, scores: i }, u);
+  return M.runKernel(To, { boxes: a, scores: i }, u);
 }
-var _F = L({ nonMaxSuppression_: $F });
-function AF(e, t, n) {
-  let s = EF(e, t, n), r = s < 0 ? -(s + 1) : s;
+var FF = L({ nonMaxSuppression_: DF });
+function OF(e, t, n) {
+  let s = PF(e, t, n), r = s < 0 ? -(s + 1) : s;
   e.splice(r, 0, t);
 }
-function EF(e, t, n) {
-  return DF(e, t, n || RF);
+function PF(e, t, n) {
+  return MF(e, t, n || zF);
 }
-function RF(e, t) {
+function zF(e, t) {
   return e > t ? 1 : e < t ? -1 : 0;
 }
-function DF(e, t, n) {
+function MF(e, t, n) {
   let s = 0, r = e.length, a = 0, i = false;
   for (; s < r; ) {
     a = s + (r - s >>> 1);
@@ -8514,20 +8514,20 @@ function DF(e, t, n) {
   }
   return i ? s : -s - 1;
 }
-function RI(e, t, n, s, r) {
-  return hb(e, t, n, s, r, 0);
+function PI(e, t, n, s, r) {
+  return mb(e, t, n, s, r, 0);
 }
-function DI(e, t, n, s, r, a) {
-  return hb(e, t, n, s, r, 0, false, a, true);
+function zI(e, t, n, s, r, a) {
+  return mb(e, t, n, s, r, 0, false, a, true);
 }
-function FI(e, t, n, s, r, a) {
-  return hb(e, t, n, s, r, a, true);
+function MI(e, t, n, s, r, a) {
+  return mb(e, t, n, s, r, a, true);
 }
-function hb(e, t, n, s, r, a, i = false, o = false, u = false) {
+function mb(e, t, n, s, r, a, i = false, o = false, u = false) {
   let l = [];
   for (let g = 0; g < t.length; g++)
     t[g] > r && l.push({ score: t[g], boxIndex: g, suppressBeginIndex: 0 });
-  l.sort(fx);
+  l.sort(gx);
   let c = a > 0 ? -0.5 / a : 0, p = [], d = [];
   for (; p.length < n && l.length > 0; ) {
     let g = l.pop(), { score: b, boxIndex: y, suppressBeginIndex: v } = g;
@@ -8535,76 +8535,76 @@ function hb(e, t, n, s, r, a, i = false, o = false, u = false) {
       break;
     let x = false;
     for (let k = p.length - 1; k >= v; --k) {
-      let T = FF(e, y, p[k]);
+      let T = LF(e, y, p[k]);
       if (T >= s) {
         x = true;
         break;
       }
-      if (g.score = g.score * OF(s, c, T), g.score <= r)
+      if (g.score = g.score * BF(s, c, T), g.score <= r)
         break;
     }
-    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && AF(l, g, fx));
+    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && OF(l, g, gx));
   }
   let h = p.length, f = n - h;
   o && f > 0 && (p.push(...new Array(f).fill(0)), d.push(...new Array(f).fill(0)));
   let m = { selectedIndices: p };
   return i && (m.selectedScores = d), u && (m.validOutputs = h), m;
 }
-function FF(e, t, n) {
+function LF(e, t, n) {
   let s = e.subarray(t * 4, t * 4 + 4), r = e.subarray(n * 4, n * 4 + 4), a = Math.min(s[0], s[2]), i = Math.min(s[1], s[3]), o = Math.max(s[0], s[2]), u = Math.max(s[1], s[3]), l = Math.min(r[0], r[2]), c = Math.min(r[1], r[3]), p = Math.max(r[0], r[2]), d = Math.max(r[1], r[3]), h = (o - a) * (u - i), f = (p - l) * (d - c);
   if (h <= 0 || f <= 0)
     return 0;
   let m = Math.max(a, l), g = Math.max(i, c), b = Math.min(o, p), y = Math.min(u, d), v = Math.max(b - m, 0) * Math.max(y - g, 0);
   return v / (h + f - v);
 }
-function OF(e, t, n) {
+function BF(e, t, n) {
   let s = Math.exp(t * n * n);
   return n <= e ? s : 0;
 }
-function fx(e, t) {
+function gx(e, t) {
   return e.score - t.score || e.score === t.score && t.boxIndex - e.boxIndex;
 }
-async function PF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Yo(a, i, n, s, r);
+async function VF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
+  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Qo(a, i, n, s, r);
   n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
-  let u = await Promise.all([a.data(), i.data()]), l = u[0], c = u[1], { selectedIndices: p } = RI(l, c, n, s, r);
+  let u = await Promise.all([a.data(), i.data()]), l = u[0], c = u[1], { selectedIndices: p } = PI(l, c, n, s, r);
   return a !== e && a.dispose(), i !== t && i.dispose(), Zt(p, "int32");
 }
-var zF = PF;
-function MF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Yo(i, o, n, s, r, a);
+var WF = VF;
+function UF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
+  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Qo(i, o, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
-  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel(No, l, c);
+  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel($o, l, c);
   return { selectedIndices: p[0], selectedScores: p[1] };
 }
-var LF = L({ nonMaxSuppressionWithScore_: MF });
-async function BF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Yo(i, o, n, s, r, a);
+var GF = L({ nonMaxSuppressionWithScore_: UF });
+async function HF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
+  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Qo(i, o, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
-  let l = await Promise.all([i.data(), o.data()]), c = l[0], p = l[1], { selectedIndices: d, selectedScores: h } = FI(c, p, n, s, r, a);
+  let l = await Promise.all([i.data(), o.data()]), c = l[0], p = l[1], { selectedIndices: d, selectedScores: h } = MI(c, p, n, s, r, a);
   return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(d, "int32"), selectedScores: Zt(h) };
 }
-var VF = BF;
-function WF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Yo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(kl, d, h);
+var qF = HF;
+function jF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
+  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Qo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(kl, d, h);
   return { selectedIndices: f[0], validOutputs: f[1] };
 }
-var UF = L({ nonMaxSuppressionPadded_: WF });
-async function GF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Yo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = DI(d, h, l, c, p, a);
+var KF = L({ nonMaxSuppressionPadded_: jF });
+async function XF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
+  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Qo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = zI(d, h, l, c, p, a);
   return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Zt(f, "int32"), validOutputs: we(m, "int32") };
 }
-var HF = GF;
-function qF(e, t, n = false, s = false) {
+var YF = XF;
+function QF(e, t, n = false, s = false) {
   let r = _(e, "images", "resizeBilinear");
   O(r.rank === 3 || r.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${t}.`), O(s === false || n === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");
   let a = r, i = false;
   r.rank === 3 && (i = true, a = G(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
-  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(ti, o, u);
+  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(ri, o, u);
   return i ? G(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
 }
-var jF = L({ resizeBilinear_: qF });
-function KF(e, t, n = false, s = false) {
+var ZF = L({ resizeBilinear_: QF });
+function JF(e, t, n = false, s = false) {
   let r = _(e, "images", "resizeNearestNeighbor");
   O(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), O(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), O(s === false || n === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
   let a = r, i = false;
@@ -8612,8 +8612,8 @@ function KF(e, t, n = false, s = false) {
   let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Cl, o, u);
   return i ? G(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
 }
-var XF = L({ resizeNearestNeighbor_: KF });
-function YF(e, t = "binary", n = false, s = 0.5) {
+var eO = L({ resizeNearestNeighbor_: JF });
+function tO(e, t = "binary", n = false, s = 0.5) {
   let r = _(e, "image", "threshold"), a = 0.2989, i = 0.587, o = 0.114, u = r.shape[0] * r.shape[1], l = V(Zt([s]), 255), c, p, d, h;
   if (O(r.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${r.rank}.`), O(r.shape[2] === 3 || r.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`), O(r.dtype === "int32" || r.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`), O(t === "otsu" || t === "binary", () => `Method must be binary or otsu, but was ${t}`), r.shape[2] === 3) {
     [c, p, d] = Bn(r, [1, 1, 1], -1);
@@ -8622,36 +8622,36 @@ function YF(e, t = "binary", n = false, s = 0.5) {
   } else
     h = e;
   if (t === "otsu") {
-    let g = Qk(ce(fI(h), "int32"), fs([]), 256);
-    l = QF(g, u);
+    let g = tI(ce(yI(h), "int32"), fs([]), 256);
+    l = nO(g, u);
   }
-  let f = n ? Xo(h, l) : Wn(h, l);
+  let f = n ? Yo(h, l) : Un(h, l);
   return ce(V(f, 255), "int32");
 }
-function QF(e, t) {
+function nO(e, t) {
   let n = Zt([-1]), s = Zt([0]), r = Zt([0]), a, i, o, u, l, c;
   for (let p = 0; p < e.size - 1; p++) {
-    a = He(e, 0, p + 1), i = He(e, p + 1), l = xe(ve(a), t), c = xe(ve(i), t);
-    let d = ve(V(a, Yu(0, a.size)));
+    a = qe(e, 0, p + 1), i = qe(e, p + 1), l = xe(ve(a), t), c = xe(ve(i), t);
+    let d = ve(V(a, Qu(0, a.size)));
     o = xe(d, ve(a));
-    let h = Pl(i.shape, a.size), f = ie(Yu(0, i.size), h), m = V(i, f);
+    let h = Pl(i.shape, a.size), f = ie(Qu(0, i.size), h), m = V(i, f);
     u = xe(ve(m), ve(i));
     let g = ge(o, u), b = ge(o, u), y = V(l, c);
     r = V(V(y, g), b);
-    let v = Wn(r, s);
+    let v = Un(r, s);
     s = vn(v, r, s), n = vn(v, Zt([p]), n);
   }
   return n;
 }
-var ZF = L({ threshold_: YF });
-function JF(e, t, n = "nearest", s = "constant", r = 0, a) {
+var sO = L({ threshold_: tO });
+function rO(e, t, n = "nearest", s = "constant", r = 0, a) {
   let i = _(e, "image", "transform", "float32"), o = _(t, "transforms", "transform", "float32");
   O(i.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && (o.shape[0] === i.shape[0] || o.shape[0] === 1) && o.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), O(a == null || a.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${a}.`);
   let u = { image: i, transforms: o }, l = { interpolation: n, fillMode: s, fillValue: r, outputShape: a };
-  return M.runKernel(Uo, u, l);
+  return M.runKernel(Go, u, l);
 }
-var eO = L({ transform_: JF });
-function tO(e, t, n) {
+var aO = L({ transform_: rO });
+function iO(e, t, n) {
   O(t % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t}.`), O(n % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${n}.`);
   let s = _(e, "a", "bandPart");
   O(s.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${s.rank}.`);
@@ -8661,11 +8661,11 @@ function tO(e, t, n) {
   if (!(n <= i))
     throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);
   t < 0 && (t = a), n < 0 && (n = i);
-  let o = G(Yu(0, a, 1, "int32"), [-1, 1]), u = Yu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Xo(l, we(+t, "int32")), Ko(l, we(-n, "int32"))), p = $t([a, i], s.dtype);
-  return G(Jn(Fs(G(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
+  let o = G(Qu(0, a, 1, "int32"), [-1, 1]), u = Qu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Yo(l, we(+t, "int32")), Xo(l, we(-n, "int32"))), p = $t([a, i], s.dtype);
+  return G(es(Fs(G(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
 }
-var nO = L({ bandPart_: tO });
-function sO(e) {
+var oO = L({ bandPart_: iO });
+function uO(e) {
   let t;
   if (Array.isArray(e)) {
     t = false, O(e != null && e.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
@@ -8673,7 +8673,7 @@ function sO(e) {
     for (let a = 1; a < e.length; ++a)
       O(e[a].shape[0] === r, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`);
   } else
-    t = true, e = Bn(e, e.shape[0], 0).map((r) => mr(r, [0]));
+    t = true, e = Bn(e, e.shape[0], 0).map((r) => gr(r, [0]));
   O(e.length <= e[0].shape[0], () => `Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);
   let n = [], s = e;
   for (let r = 0; r < e.length; ++r)
@@ -8684,56 +8684,56 @@ function sO(e) {
           let o = V(ve(V(n[i], a)), n[i]);
           a = ge(a, o);
         }
-      return xe(a, CI(a, "euclidean"));
+      return xe(a, _I(a, "euclidean"));
     }));
-  return t ? Jn(n, 0) : n;
+  return t ? es(n, 0) : n;
 }
-var rO = L({ gramSchmidt_: sO });
-function aO(e, t = false) {
+var lO = L({ gramSchmidt_: uO });
+function cO(e, t = false) {
   if (O(e.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`), e.rank === 2)
-    return mx(e, t);
+    return bx(e, t);
   {
     let n = e.shape.slice(0, e.shape.length - 2).reduce((u, l) => u * l), s = Fs(G(e, [n, e.shape[e.shape.length - 2], e.shape[e.shape.length - 1]]), 0), r = [], a = [];
     s.forEach((u) => {
-      let [l, c] = mx(u, t);
+      let [l, c] = bx(u, t);
       r.push(l), a.push(c);
     });
-    let i = G(Jn(r, 0), e.shape), o = G(Jn(a, 0), e.shape);
+    let i = G(es(r, 0), e.shape), o = G(es(a, 0), e.shape);
     return [i, o];
   }
 }
-function mx(e, t = false) {
+function bx(e, t = false) {
   return M.tidy(() => {
     O(e.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);
-    let n = e.shape[0], s = e.shape[1], r = rI(n), a = lr(e), i = Ki([[1]], [1, 1]), o = lr(i), u = n >= s ? s : n;
+    let n = e.shape[0], s = e.shape[1], r = uI(n), a = cr(e), i = Yi([[1]], [1, 1]), o = cr(i), u = n >= s ? s : n;
     for (let l = 0; l < u; ++l) {
       let c = a, p = o, d = r;
       [o, a, r] = M.tidy(() => {
-        let h = He(a, [l, l], [n - l, 1]), f = CI(h), m = He(a, [l, l], [1, 1]), g = vn(Wn(m, 0), Ki([[-1]]), Ki([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
-        y.shape[0] === 1 ? o = lr(i) : o = Ft([i, He(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
-        let v = kt(xe(We(g, b), f)), x = He(a, [l, 0], [n - l, s]), k = V(v, o), T = qe(o);
+        let h = qe(a, [l, l], [n - l, 1]), f = _I(h), m = qe(a, [l, l], [1, 1]), g = vn(Un(m, 0), Yi([[-1]]), Yi([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
+        y.shape[0] === 1 ? o = cr(i) : o = Ft([i, qe(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
+        let v = kt(xe(We(g, b), f)), x = qe(a, [l, 0], [n - l, s]), k = V(v, o), T = Ge(o);
         if (l === 0)
           a = ge(x, We(k, We(T, x)));
         else {
           let A = ge(x, We(k, We(T, x)));
-          a = Ft([He(a, [0, 0], [l, s]), A], 0);
+          a = Ft([qe(a, [0, 0], [l, s]), A], 0);
         }
-        let N = qe(k), E = He(r, [0, l], [n, r.shape[1] - l]);
+        let N = Ge(k), E = qe(r, [0, l], [n, r.shape[1] - l]);
         if (l === 0)
           r = ge(E, We(We(E, o), N));
         else {
           let A = ge(E, We(We(E, o), N));
-          r = Ft([He(r, [0, 0], [n, l]), A], 1);
+          r = Ft([qe(r, [0, 0], [n, l]), A], 1);
         }
         return [o, a, r];
       }), Re([c, p, d]);
     }
-    return !t && n > s && (r = He(r, [0, 0], [n, s]), a = He(a, [0, 0], [s, s])), [r, a];
+    return !t && n > s && (r = qe(r, [0, 0], [n, s]), a = qe(a, [0, 0], [s, s])), [r, a];
   });
 }
-var iO = L({ qr_: aO });
-var oO = ((e) => (e[e.NONE = 0] = "NONE", e[e.MEAN = 1] = "MEAN", e[e.SUM = 2] = "SUM", e[e.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS", e))(oO || {});
-function uO(e, t, n = 3) {
+var dO = L({ qr_: cO });
+var pO = ((e) => (e[e.NONE = 0] = "NONE", e[e.MEAN = 1] = "MEAN", e[e.SUM = 2] = "SUM", e[e.SUM_BY_NONZERO_WEIGHTS = 3] = "SUM_BY_NONZERO_WEIGHTS", e))(pO || {});
+function hO(e, t, n = 3) {
   let s = _(e, "losses", "computeWeightedLoss"), r = null;
   t != null && (r = _(t, "weights", "computeWeightedLoss"));
   let a = r == null ? s : V(s, r);
@@ -8753,97 +8753,97 @@ function uO(e, t, n = 3) {
     if (r == null)
       return xe(ve(a), we(s.size));
     {
-      let i = V(r, Mn(s.shape)), o = ce(ve(Xu(i, we(0))), "float32");
+      let i = V(r, Mn(s.shape)), o = ce(ve(Yu(i, we(0))), "float32");
       return xe(ve(a), o);
     }
   }
   throw Error(`Unknown reduction: ${n}`);
 }
-var Ys = L({ computeWeightedLoss_: uO });
-function lO(e, t, n, s = 3) {
+var Qs = L({ computeWeightedLoss_: hO });
+function fO(e, t, n, s = 3) {
   let r = _(e, "labels", "absoluteDifference"), a = _(t, "predictions", "absoluteDifference"), i = null;
   n != null && (i = _(n, "weights", "absoluteDifference")), pn(r.shape, a.shape, "Error in absoluteDifference: ");
   let o = Mt(ge(r, a));
-  return Ys(o, i, s);
+  return Qs(o, i, s);
 }
-var cO = L({ absoluteDifference_: lO });
-function dO(e, t, n, s, r = 3) {
+var mO = L({ absoluteDifference_: fO });
+function gO(e, t, n, s, r = 3) {
   let a = _(e, "labels", "cosineDistance"), i = _(t, "predictions", "cosineDistance"), o = null;
   s != null && (o = _(s, "weights", "cosineDistance")), pn(a.shape, i.shape, "Error in cosineDistance: ");
   let u = we(1), l = ge(u, ve(V(a, i), n, true));
-  return Ys(l, o, r);
+  return Qs(l, o, r);
 }
-var pO = L({ cosineDistance_: dO });
-function hO(e, t, n, s = 3) {
+var bO = L({ cosineDistance_: gO });
+function yO(e, t, n, s = 3) {
   let r = _(e, "labels", "hingeLoss"), a = _(t, "predictions", "hingeLoss"), i = null;
   n != null && (i = _(n, "weights", "hingeLoss")), pn(r.shape, a.shape, "Error in hingeLoss: ");
   let o = we(1);
   r = ge(V(we(2), r), o);
-  let u = Xs(ge(o, V(r, a)));
-  return Ys(u, i, s);
+  let u = Ys(ge(o, V(r, a)));
+  return Qs(u, i, s);
 }
-var fO = L({ hingeLoss_: hO });
-function mO(e, t, n, s = 1, r = 3) {
+var vO = L({ hingeLoss_: yO });
+function xO(e, t, n, s = 1, r = 3) {
   let a = _(e, "labels", "huberLoss"), i = _(t, "predictions", "huberLoss"), o = null;
   n != null && (o = _(n, "weights", "huberLoss")), pn(a.shape, i.shape, "Error in huberLoss: ");
-  let u = we(s), l = Mt(ge(i, a)), c = bp(l, u), p = ge(l, c), d = ie(V(we(0.5), ct(c)), V(u, p));
-  return Ys(d, o, r);
+  let u = we(s), l = Mt(ge(i, a)), c = vp(l, u), p = ge(l, c), d = ie(V(we(0.5), ct(c)), V(u, p));
+  return Qs(d, o, r);
 }
-var gO = L({ huberLoss_: mO });
-function bO(e, t, n, s = 1e-7, r = 3) {
+var wO = L({ huberLoss_: xO });
+function kO(e, t, n, s = 1e-7, r = 3) {
   let a = _(e, "labels", "logLoss"), i = _(t, "predictions", "logLoss"), o = null;
   n != null && (o = _(n, "weights", "logLoss")), pn(a.shape, i.shape, "Error in logLoss: ");
-  let u = we(1), l = we(s), c = kt(V(a, Yn(ie(i, l)))), p = V(ge(u, a), Yn(ie(ge(u, i), l))), d = ge(c, p);
-  return Ys(d, o, r);
+  let u = we(1), l = we(s), c = kt(V(a, Qn(ie(i, l)))), p = V(ge(u, a), Qn(ie(ge(u, i), l))), d = ge(c, p);
+  return Qs(d, o, r);
 }
-var yO = L({ logLoss_: bO });
-function vO(e, t, n, s = 3) {
+var IO = L({ logLoss_: kO });
+function SO(e, t, n, s = 3) {
   let r = _(e, "labels", "meanSquaredError"), a = _(t, "predictions", "meanSquaredError"), i = null;
   n != null && (i = _(n, "weights", "meanSquaredError")), pn(r.shape, a.shape, "Error in meanSquaredError: ");
-  let o = wI(r, a);
-  return Ys(o, i, s);
+  let o = CI(r, a);
+  return Qs(o, i, s);
 }
-var xO = L({ meanSquaredError_: vO });
-function wO(e, t) {
+var CO = L({ meanSquaredError_: SO });
+function NO(e, t) {
   let n = _(e, "labels", "sigmoidCrossEntropyWithLogits"), s = _(t, "logits", "sigmoidCrossEntropyWithLogits");
   pn(n.shape, s.shape, "Error in sigmoidCrossEntropyWithLogits: ");
-  let r = Xs(s), a = V(s, n), i = Yg(Xn(kt(Mt(s))));
+  let r = Ys(s), a = V(s, n), i = Zg(Yn(kt(Mt(s))));
   return ie(ge(r, a), i);
 }
-function kO(e, t, n, s = 0, r = 3) {
+function TO(e, t, n, s = 0, r = 3) {
   let a = _(e, "multiClassLabels", "sigmoidCrossEntropy"), i = _(t, "logits", "sigmoidCrossEntropy"), o = null;
   if (n != null && (o = _(n, "weights", "sigmoidCrossEntropy")), pn(a.shape, i.shape, "Error in sigmoidCrossEntropy: "), s > 0) {
     let l = we(s), c = we(1), p = we(0.5);
     a = ie(V(a, ge(c, l)), V(p, l));
   }
-  let u = wO(a, i);
-  return Ys(u, o, r);
+  let u = NO(a, i);
+  return Qs(u, o, r);
 }
-var IO = L({ sigmoidCrossEntropy_: kO });
-function SO(e, t, n = -1) {
+var $O = L({ sigmoidCrossEntropy_: TO });
+function _O(e, t, n = -1) {
   if (n === -1 && (n = t.rank - 1), n !== t.rank - 1)
     throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);
   return js((r, a, i) => {
-    let u = iD(a, [n], true), l = ge(ce(a, "float32"), u);
+    let u = dD(a, [n], true), l = ge(ce(a, "float32"), u);
     i([r, l]);
     let c = kt(V(l, r));
     return { value: ve(c, [n]), gradFunc: (h, f) => {
-      let [m, g] = f, b = da(h.shape, [n]);
-      return [V(G(h, b), ge(ce(m, "float32"), Xn(g))), V(G(h, b), ge(Xn(g), ce(m, "float32")))];
+      let [m, g] = f, b = pa(h.shape, [n]);
+      return [V(G(h, b), ge(ce(m, "float32"), Yn(g))), V(G(h, b), ge(Yn(g), ce(m, "float32")))];
     } };
   })(e, t);
 }
-function CO(e, t, n, s = 0, r = 3) {
+function AO(e, t, n, s = 0, r = 3) {
   let a = _(e, "onehotLabels", "softmaxCrossEntropy"), i = _(t, "logits", "softmaxCrossEntropy"), o = null;
   if (n != null && (o = _(n, "weights", "softmaxCrossEntropy")), pn(a.shape, i.shape, "Error in softmaxCrossEntropy: "), s > 0) {
     let l = we(s), c = we(1), p = we(a.shape[1]);
     a = ie(V(a, ge(c, l)), xe(l, p));
   }
-  let u = SO(a, i);
-  return Ys(u, o, r);
+  let u = _O(a, i);
+  return Qs(u, o, r);
 }
-var NO = L({ softmaxCrossEntropy_: CO });
-function TO(e, t, n, s) {
+var EO = L({ softmaxCrossEntropy_: AO });
+function RO(e, t, n, s) {
   let r = _(e, "indices", "sparseFillEmptyRows", "int32"), a = _(t, "values", "sparseFillEmptyRows"), i = _(n, "denseShape", "sparseFillEmptyRows", "int32"), o = _(s, "defaultValue", "sparseFillEmptyRows", a.dtype);
   if (r.rank !== 2)
     throw new Error(`Indices should be Tensor2D but received shape
@@ -8854,11 +8854,11 @@ function TO(e, t, n, s) {
     throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);
   if (o.rank !== 0)
     throw new Error(`Default value should be a scalar but received shape ${o.shape}`);
-  let u = { indices: r, values: a, denseShape: i, defaultValue: o }, l = M.runKernel(sp, u);
+  let u = { indices: r, values: a, denseShape: i, defaultValue: o }, l = M.runKernel(ap, u);
   return { outputIndices: l[0], outputValues: l[1], emptyRowIndicator: l[2], reverseIndexMap: l[3] };
 }
-var $O = L({ sparseFillEmptyRows_: TO });
-function _O(e, t, n) {
+var DO = L({ sparseFillEmptyRows_: RO });
+function FO(e, t, n) {
   let s = _(e, "inputIndices", "sparseReshape", "int32"), r = _(t, "inputShape", "sparseReshape", "int32"), a = _(n, "newShape", "sparseReshape", "int32");
   if (s.rank !== 2)
     throw new Error(`Input indices should be Tensor2D but received shape
@@ -8870,8 +8870,8 @@ function _O(e, t, n) {
   let i = { inputIndices: s, inputShape: r, newShape: a }, o = M.runKernel(_l, i);
   return { outputIndices: o[0], outputShape: o[1] };
 }
-var AO = L({ sparseReshape_: _O });
-function EO(e, t, n) {
+var OO = L({ sparseReshape_: FO });
+function PO(e, t, n) {
   let s = _(e, "data", "sparseSegmentMean"), r = _(t, "indices", "sparseSegmentMean", "int32"), a = _(n, "segmentIds", "sparseSegmentMean", "int32");
   if (s.rank < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -8882,10 +8882,10 @@ function EO(e, t, n) {
     throw new Error(`Segment ids should be Tensor1D but received shape
           ${a.shape}`);
   let i = { data: s, indices: r, segmentIds: a };
-  return M.runKernel(rp, i);
+  return M.runKernel(ip, i);
 }
-var RO = L({ sparseSegmentMean_: EO });
-function DO(e, t, n) {
+var zO = L({ sparseSegmentMean_: PO });
+function MO(e, t, n) {
   let s = _(e, "data", "sparseSegmentSum"), r = _(t, "indices", "sparseSegmentSum", "int32"), a = _(n, "segmentIds", "sparseSegmentSum", "int32");
   if (s.rank < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -8896,10 +8896,10 @@ function DO(e, t, n) {
     throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);
   let i = { data: s, indices: r, segmentIds: a };
-  return M.runKernel(ap, i);
+  return M.runKernel(op, i);
 }
-var FO = L({ sparseSegmentSum_: DO });
-function OO(e, t, n, s, r, a, i, o) {
+var LO = L({ sparseSegmentSum_: MO });
+function BO(e, t, n, s, r, a, i, o) {
   let u = _(e, "data", "stringNGrams", "string");
   if (u.dtype !== "string")
     throw new Error("Data must be of datatype string");
@@ -8908,36 +8908,36 @@ function OO(e, t, n, s, r, a, i, o) {
   let l = _(t, "dataSplits", "stringNGrams");
   if (l.dtype !== "int32")
     throw new Error("Data splits must be of datatype int32");
-  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: i, preserveShortSequences: o }, p = { data: u, dataSplits: l }, d = M.runKernel(op, p, c);
+  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: i, preserveShortSequences: o }, p = { data: u, dataSplits: l }, d = M.runKernel(lp, p, c);
   return { nGrams: d[0], nGramsSplits: d[1] };
 }
-var PO = L({ stringNGrams_: OO });
-function zO(e, t, n = true) {
+var VO = L({ stringNGrams_: BO });
+function WO(e, t, n = true) {
   let s = _(e, "input", "stringSplit", "string"), r = _(t, "delimiter", "stringSplit", "string");
   if (s.rank !== 1)
     throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);
   if (r.rank !== 0)
     throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);
-  let a = { skipEmpty: n }, i = { input: s, delimiter: r }, o = M.runKernel($g, i, a);
+  let a = { skipEmpty: n }, i = { input: s, delimiter: r }, o = M.runKernel(Ag, i, a);
   return { indices: o[0], values: o[1], shape: o[2] };
 }
-var MO = L({ stringSplit_: zO });
-function LO(e, t) {
+var UO = L({ stringSplit_: WO });
+function GO(e, t) {
   let n = _(e, "input", "stringToHashBucketFast", "string"), s = { numBuckets: t };
   if (t <= 0)
     throw new Error("Number of buckets must be at least 1");
   let r = { input: n };
-  return M.runKernel(_g, r, s);
+  return M.runKernel(Eg, r, s);
 }
-var BO = L({ stringToHashBucketFast_: LO });
-var Ipe = { fft: lb, ifft: Id, rfft: cb, irfft: xI };
-var Spe = { hammingWindow: mF, hannWindow: AI, frame: EI, stft: vF };
-var qn = { flipLeftRight: IF, grayscaleToRGB: CF, resizeNearestNeighbor: XF, resizeBilinear: jF, rotateWithOffset: TF, cropAndResize: wF, nonMaxSuppression: _F, nonMaxSuppressionAsync: zF, nonMaxSuppressionWithScore: LF, nonMaxSuppressionWithScoreAsync: VF, nonMaxSuppressionPadded: UF, nonMaxSuppressionPaddedAsync: HF, threshold: ZF, transform: eO };
-var VO = { bandPart: nO, gramSchmidt: rO, qr: iO };
-var Cpe = { absoluteDifference: cO, computeWeightedLoss: Ys, cosineDistance: pO, hingeLoss: fO, huberLoss: gO, logLoss: yO, meanSquaredError: xO, sigmoidCrossEntropy: IO, softmaxCrossEntropy: NO };
-var Wc = { sparseFillEmptyRows: $O, sparseReshape: AO, sparseSegmentMean: RO, sparseSegmentSum: FO };
-var Mf = { stringNGrams: PO, stringSplit: MO, stringToHashBucketFast: BO };
-var _r = class extends Vk {
+var HO = L({ stringToHashBucketFast_: GO });
+var zpe = { fft: db, ifft: Id, rfft: pb, irfft: SI };
+var Mpe = { hammingWindow: xF, hannWindow: FI, frame: OI, stft: SF };
+var jn = { flipLeftRight: $F, grayscaleToRGB: AF, resizeNearestNeighbor: eO, resizeBilinear: ZF, rotateWithOffset: RF, cropAndResize: NF, nonMaxSuppression: FF, nonMaxSuppressionAsync: WF, nonMaxSuppressionWithScore: GF, nonMaxSuppressionWithScoreAsync: qF, nonMaxSuppressionPadded: KF, nonMaxSuppressionPaddedAsync: YF, threshold: sO, transform: aO };
+var qO = { bandPart: oO, gramSchmidt: lO, qr: dO };
+var Lpe = { absoluteDifference: mO, computeWeightedLoss: Qs, cosineDistance: bO, hingeLoss: vO, huberLoss: wO, logLoss: IO, meanSquaredError: CO, sigmoidCrossEntropy: $O, softmaxCrossEntropy: EO };
+var Wc = { sparseFillEmptyRows: DO, sparseReshape: OO, sparseSegmentMean: zO, sparseSegmentSum: LO };
+var Bf = { stringNGrams: VO, stringSplit: UO, stringToHashBucketFast: HO };
+var Ar = class extends Hk {
   minimize(e, t = false, n) {
     let { value: s, grads: r } = this.computeGradients(e, n);
     if (n != null) {
@@ -8954,7 +8954,7 @@ var _r = class extends Vk {
     this.iterations_ = this.iterations + 1;
   }
   computeGradients(e, t) {
-    return XR(e, t);
+    return eD(e, t);
   }
   dispose() {
     this.iterations_ != null && Re(this.iterations_);
@@ -8972,8 +8972,8 @@ var _r = class extends Vk {
     return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
   }
 };
-Object.defineProperty(_r, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
-var fb = class extends _r {
+Object.defineProperty(Ar, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
+var gb = class extends Ar {
   constructor(e, t, n = null) {
     super(), this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = M.backend.epsilon());
   }
@@ -9012,9 +9012,9 @@ var fb = class extends _r {
     return new e(t.learningRate, t.rho, t.epsilon);
   }
 };
-fb.className = "Adadelta";
-Tr(fb);
-var mb = class extends _r {
+gb.className = "Adadelta";
+$r(gb);
+var bb = class extends Ar {
   constructor(e, t = 0.1) {
     super(), this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
   }
@@ -9052,9 +9052,9 @@ var mb = class extends _r {
     return new e(t.learningRate, t.initialAccumulatorValue);
   }
 };
-mb.className = "Adagrad";
-Tr(mb);
-var gb = class extends _r {
+bb.className = "Adagrad";
+$r(bb);
+var yb = class extends Ar {
   constructor(e, t, n, s = null) {
     super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], j(() => {
       this.accBeta1 = we(t).variable(), this.accBeta2 = we(n).variable();
@@ -9086,7 +9086,7 @@ var gb = class extends _r {
   }
   async setWeights(e) {
     e = await this.extractIterations(e), j(() => {
-      this.accBeta1.assign(pa(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(pa(this.beta2, this.iterations_ + 1));
+      this.accBeta1.assign(ha(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(ha(this.beta2, this.iterations_ + 1));
     });
     let t = e.length / 2, n = false;
     this.accumulatedFirstMoment = e.slice(0, t).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.accumulatedSecondMoment = e.slice(t, t * 2).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) }));
@@ -9098,9 +9098,9 @@ var gb = class extends _r {
     return new e(t.learningRate, t.beta1, t.beta2, t.epsilon);
   }
 };
-gb.className = "Adam";
-Tr(gb);
-var bb = class extends _r {
+yb.className = "Adam";
+$r(yb);
+var vb = class extends Ar {
   constructor(e, t, n, s = null, r = 0) {
     super(), this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.decay = r, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], j(() => {
       this.iteration = we(0).variable(), this.accBeta1 = we(t).variable();
@@ -9116,7 +9116,7 @@ var bb = class extends _r {
         let u = Array.isArray(e) ? e[a].tensor : e[r];
         if (u == null)
           return;
-        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedWeightedInfNorm[a].variable, p = ie(V(l, this.beta1), V(u, 1 - this.beta1)), d = V(c, this.beta2), h = Mt(u), f = $r(d, h);
+        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedWeightedInfNorm[a].variable, p = ie(V(l, this.beta1), V(u, 1 - this.beta1)), d = V(c, this.beta2), h = Mt(u), f = _r(d, h);
         l.assign(p), c.assign(f);
         let m = ie(V(xe(s, n), xe(p, ie(f, this.epsilon))), i);
         i.assign(m);
@@ -9139,9 +9139,9 @@ var bb = class extends _r {
     return new e(t.learningRate, t.beta1, t.beta2, t.epsilon, t.decay);
   }
 };
-bb.className = "Adamax";
-Tr(bb);
-var Ip = class extends _r {
+vb.className = "Adamax";
+$r(vb);
+var Cp = class extends Ar {
   constructor(e) {
     super(), this.learningRate = e, this.setLearningRate(e);
   }
@@ -9177,9 +9177,9 @@ var Ip = class extends _r {
     return new e(t.learningRate);
   }
 };
-Ip.className = "SGD";
-Tr(Ip);
-var yb = class extends Ip {
+Cp.className = "SGD";
+$r(Cp);
+var xb = class extends Cp {
   constructor(e, t, n = false) {
     super(e), this.learningRate = e, this.momentum = t, this.useNesterov = n, this.accumulations = [], this.m = we(this.momentum);
   }
@@ -9215,9 +9215,9 @@ var yb = class extends Ip {
     return new e(t.learningRate, t.momentum, t.useNesterov);
   }
 };
-yb.className = "Momentum";
-Tr(yb);
-var vb = class extends _r {
+xb.className = "Momentum";
+$r(xb);
+var wb = class extends Ar {
   constructor(e, t = 0.9, n = 0, s = null, r = false) {
     if (super(), this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = M.backend.epsilon()), e == null)
       throw new Error("learningRate for RMSPropOptimizer must be defined.");
@@ -9265,39 +9265,39 @@ var vb = class extends _r {
     return new e(t.learningRate, t.decay, t.momentum, t.epsilon, t.centered);
   }
 };
-vb.className = "RMSProp";
-Tr(vb);
-var Wr = class {
+wb.className = "RMSProp";
+$r(wb);
+var Ur = class {
   static sgd(e) {
-    return new Ip(e);
+    return new Cp(e);
   }
   static momentum(e, t, n = false) {
-    return new yb(e, t, n);
+    return new xb(e, t, n);
   }
   static rmsprop(e, t = 0.9, n = 0, s = null, r = false) {
-    return new vb(e, t, n, s, r);
+    return new wb(e, t, n, s, r);
   }
   static adam(e = 1e-3, t = 0.9, n = 0.999, s = null) {
-    return new gb(e, t, n, s);
+    return new yb(e, t, n, s);
   }
   static adadelta(e = 1e-3, t = 0.95, n = null) {
-    return new fb(e, t, n);
+    return new gb(e, t, n);
   }
   static adamax(e = 2e-3, t = 0.9, n = 0.999, s = null, r = 0) {
-    return new bb(e, t, n, s, r);
+    return new vb(e, t, n, s, r);
   }
   static adagrad(e, t = 0.1) {
-    return new mb(e, t);
+    return new bb(e, t);
   }
 };
-var Pi = { sgd: Wr.sgd, momentum: Wr.momentum, adadelta: Wr.adadelta, adagrad: Wr.adagrad, rmsprop: Wr.rmsprop, adamax: Wr.adamax, adam: Wr.adam };
-var WO = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (e) => e())();
-function OI() {
-  return new Promise((e) => WO(() => e()));
+var Mi = { sgd: Ur.sgd, momentum: Ur.momentum, adadelta: Ur.adadelta, adagrad: Ur.adagrad, rmsprop: Ur.rmsprop, adamax: Ur.adamax, adam: Ur.adam };
+var jO = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (e) => e())();
+function LI() {
+  return new Promise((e) => jO(() => e()));
 }
 var S = {};
-Ae(S, { ERF_A1: () => JO, ERF_A2: () => eP, ERF_A3: () => tP, ERF_A4: () => nP, ERF_A5: () => sP, ERF_P: () => ZO, PARALLELIZE_THRESHOLD: () => xb, SELU_SCALE: () => zI, SELU_SCALEALPHA: () => PI, applyActivation: () => wp, assertAndGetBroadcastShape: () => ot, assertAxesAreInnerMostDims: () => sD, assertParamsConsistent: () => UO, assignToTypedArray: () => lP, axesAreInnerMostDims: () => Qg, calculateShapes: () => Ek, checkEinsumDimSizes: () => mP, checkPadOnDimRoundingMode: () => hn, combineLocations: () => oI, complexWithEvenIndex: () => iP, complexWithOddIndex: () => oP, computeConv2DInfo: () => Ol, computeConv3DInfo: () => Kk, computeDefaultPad: () => Ug, computeDilation2DInfo: () => cE, computeOptimalWindowSize: () => HO, computeOutAndReduceShapes: () => uI, computeOutShape: () => GO, computePool2DInfo: () => jk, computePool3DInfo: () => dE, convertConv2DDataFormat: () => Xk, decodeEinsumEquation: () => hP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => da, exponent: () => dP, exponents: () => cP, fromStringArrayToUint8: () => PP, fromUint8ToStringArray: () => OP, getAxesPermutation: () => lI, getBroadcastDims: () => Ck, getComplexWithIndex: () => uP, getEinsumComputePath: () => gP, getEinsumPermutation: () => fP, getFusedBiasGradient: () => xp, getFusedDyActivation: () => vp, getImageCenter: () => qO, getInnerMostAxes: () => rD, getPermuted: () => KO, getReductionAxes: () => _t, getReshaped: () => jO, getReshapedPermuted: () => XO, getSliceBeginCoords: () => YO, getSliceSize: () => QO, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => xP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => wP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => kP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => CP, getSparseReshapeInputOutputMismatchErrorMessage: () => TP, getSparseReshapeInputOutputMultipleErrorMessage: () => NP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => IP, getSparseReshapeNegativeOutputDimErrorMessage: () => SP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => EP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => $P, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => _P, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => AP, getUndoAxesPermutation: () => Zg, isIdentityPermutation: () => bP, log: () => R$, mergeRealAndImagArrays: () => rP, prepareAndValidate: () => _k, prepareSplitSize: () => vP, segment_util: () => MI, shouldFuse: () => kp, slice_util: () => wt, splitRealAndImagArrays: () => aP, tupleValuesAreOne: () => fr, upcastType: () => cn, validateInput: () => Bg, validateUpdateShape: () => Lg, warn: () => ar });
-function UO(e, t) {
+Ae(S, { ERF_A1: () => rP, ERF_A2: () => aP, ERF_A3: () => iP, ERF_A4: () => oP, ERF_A5: () => uP, ERF_P: () => sP, PARALLELIZE_THRESHOLD: () => kb, SELU_SCALE: () => VI, SELU_SCALEALPHA: () => BI, applyActivation: () => Ip, assertAndGetBroadcastShape: () => ot, assertAxesAreInnerMostDims: () => uD, assertParamsConsistent: () => KO, assignToTypedArray: () => fP, axesAreInnerMostDims: () => Jg, calculateShapes: () => Ok, checkEinsumDimSizes: () => xP, checkPadOnDimRoundingMode: () => hn, combineLocations: () => dI, complexWithEvenIndex: () => dP, complexWithOddIndex: () => pP, computeConv2DInfo: () => Ol, computeConv3DInfo: () => Zk, computeDefaultPad: () => Hg, computeDilation2DInfo: () => gE, computeOptimalWindowSize: () => YO, computeOutAndReduceShapes: () => pI, computeOutShape: () => XO, computePool2DInfo: () => Qk, computePool3DInfo: () => bE, convertConv2DDataFormat: () => Jk, decodeEinsumEquation: () => yP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => pa, exponent: () => gP, exponents: () => mP, fromStringArrayToUint8: () => VP, fromUint8ToStringArray: () => BP, getAxesPermutation: () => hI, getBroadcastDims: () => _k, getComplexWithIndex: () => hP, getEinsumComputePath: () => wP, getEinsumPermutation: () => vP, getFusedBiasGradient: () => kp, getFusedDyActivation: () => wp, getImageCenter: () => QO, getInnerMostAxes: () => lD, getPermuted: () => JO, getReductionAxes: () => _t, getReshaped: () => ZO, getReshapedPermuted: () => eP, getSliceBeginCoords: () => tP, getSliceSize: () => nP, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => CP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => NP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => TP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => AP, getSparseReshapeInputOutputMismatchErrorMessage: () => RP, getSparseReshapeInputOutputMultipleErrorMessage: () => EP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => $P, getSparseReshapeNegativeOutputDimErrorMessage: () => _P, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => PP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => DP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => FP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => OP, getUndoAxesPermutation: () => eb, isIdentityPermutation: () => kP, log: () => M$, mergeRealAndImagArrays: () => lP, prepareAndValidate: () => Dk, prepareSplitSize: () => SP, segment_util: () => WI, shouldFuse: () => Sp, slice_util: () => wt, splitRealAndImagArrays: () => cP, tupleValuesAreOne: () => mr, upcastType: () => cn, validateInput: () => Wg, validateUpdateShape: () => Vg, warn: () => ir });
+function KO(e, t) {
   let n = e[0].length;
   e.forEach((r, a) => {
     O(r.length === n, () => `Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`);
@@ -9308,21 +9308,21 @@ function UO(e, t) {
       O(i === t || r[i] === s[i], () => `Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`);
   });
 }
-function GO(e, t) {
+function XO(e, t) {
   let n = e[0].slice();
   for (let s = 1; s < e.length; s++)
     n[t] += e[s][t];
   return n;
 }
-var xb = 30;
-function HO(e) {
-  return e <= xb ? e : hd(e, Math.floor(Math.sqrt(e)));
+var kb = 30;
+function YO(e) {
+  return e <= kb ? e : hd(e, Math.floor(Math.sqrt(e)));
 }
-function qO(e, t, n) {
+function QO(e, t, n) {
   let s = n * (typeof e == "number" ? e : e[0]), r = t * (typeof e == "number" ? e : e[1]);
   return [s, r];
 }
-function jO(e, t, n, s = true) {
+function ZO(e, t, n, s = true) {
   let r = [];
   if (s)
     r = r.concat(t.slice(0)), r.push(e[0] / n), r = r.concat(e.slice(1));
@@ -9335,7 +9335,7 @@ function jO(e, t, n, s = true) {
   }
   return r;
 }
-function KO(e, t, n = true) {
+function JO(e, t, n = true) {
   let s = [];
   if (n) {
     s.push(t);
@@ -9349,34 +9349,34 @@ function KO(e, t, n = true) {
   }
   return s;
 }
-function XO(e, t, n, s = true) {
+function eP(e, t, n, s = true) {
   let r = [];
   s ? r.push(e[0] / n) : r.push(e[0] * n);
   for (let a = 1; a < e.length; ++a)
     a <= t.length ? s ? r.push(t[a - 1] * e[a]) : r.push(e[a] / t[a - 1]) : r.push(e[a]);
   return r;
 }
-function YO(e, t) {
+function tP(e, t) {
   let n = [0];
   for (let s = 0; s < t; ++s)
     n.push(e[s][0]);
   return n;
 }
-function QO(e, t, n) {
+function nP(e, t, n) {
   let s = e.slice(0, 1);
   for (let r = 0; r < n; ++r)
     s.push(e[r + 1] - t[r][0] - t[r][1]);
   return s;
 }
-var PI = 1.7580993408473768;
-var zI = 1.0507009873554805;
-var ZO = 0.3275911;
-var JO = 0.254829592;
-var eP = -0.284496736;
-var tP = 1.421413741;
-var nP = -1.453152027;
-var sP = 1.061405429;
-function rP(e, t) {
+var BI = 1.7580993408473768;
+var VI = 1.0507009873554805;
+var sP = 0.3275911;
+var rP = 0.254829592;
+var aP = -0.284496736;
+var iP = 1.421413741;
+var oP = -1.453152027;
+var uP = 1.061405429;
+function lP(e, t) {
   if (e.length !== t.length)
     throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);
   let n = new Float32Array(e.length * 2);
@@ -9384,32 +9384,32 @@ function rP(e, t) {
     n[s] = e[s / 2], n[s + 1] = t[s / 2];
   return n;
 }
-function aP(e) {
+function cP(e) {
   let t = new Float32Array(e.length / 2), n = new Float32Array(e.length / 2);
   for (let s = 0; s < e.length; s += 2)
     t[s / 2] = e[s], n[s / 2] = e[s + 1];
   return { real: t, imag: n };
 }
-function iP(e) {
+function dP(e) {
   let t = Math.ceil(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
   for (let r = 0; r < e.length; r += 4)
     n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
   return { real: n, imag: s };
 }
-function oP(e) {
+function pP(e) {
   let t = Math.floor(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
   for (let r = 2; r < e.length; r += 4)
     n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
   return { real: n, imag: s };
 }
-function uP(e, t) {
+function hP(e, t) {
   let n = e[t * 2], s = e[t * 2 + 1];
   return { real: n, imag: s };
 }
-function lP(e, t, n, s) {
+function fP(e, t, n, s) {
   e[s * 2] = t, e[s * 2 + 1] = n;
 }
-function cP(e, t) {
+function mP(e, t) {
   let n = new Float32Array(e / 2), s = new Float32Array(e / 2);
   for (let r = 0; r < Math.ceil(e / 2); r++) {
     let a = (t ? 2 : -2) * Math.PI * (r / e);
@@ -9417,24 +9417,24 @@ function cP(e, t) {
   }
   return { real: n, imag: s };
 }
-function dP(e, t, n) {
+function gP(e, t, n) {
   let s = (n ? 2 : -2) * Math.PI * (e / t), r = Math.cos(s), a = Math.sin(s);
   return { real: r, imag: a };
 }
-var Lf = "->";
-var pP = /->/g;
-var gx = ",";
-var bx = "...";
-function hP(e, t) {
+var Vf = "->";
+var bP = /->/g;
+var yx = ",";
+var vx = "...";
+function yP(e, t) {
   e = e.replace(/\s/g, "");
-  let n = (e.length - e.replace(pP, "").length) / Lf.length;
+  let n = (e.length - e.replace(bP, "").length) / Vf.length;
   if (n < 1)
     throw new Error("Equations without an arrow are not supported.");
   if (n > 1)
-    throw new Error(`Equation must contain exactly one arrow ("${Lf}").`);
-  let [s, r] = e.split(Lf);
-  O(s.indexOf(bx) === -1, () => `The ellipsis notation ("${bx}") is not supported yet.`);
-  let a = s.split(gx), i = a.length;
+    throw new Error(`Equation must contain exactly one arrow ("${Vf}").`);
+  let [s, r] = e.split(Vf);
+  O(s.indexOf(vx) === -1, () => `The ellipsis notation ("${vx}") is not supported yet.`);
+  let a = s.split(yx), i = a.length;
   if (t !== i)
     throw new Error(`Expected ${i} input tensors, received ${t}`);
   if (i > 2)
@@ -9448,7 +9448,7 @@ function hP(e, t) {
   }
   for (let d = 0; d < s.length; ++d) {
     let h = s[d];
-    o.indexOf(h) === -1 && h !== gx && o.push(h);
+    o.indexOf(h) === -1 && h !== yx && o.push(h);
   }
   let u = new Array(a.length);
   for (let d = 0; d < i; ++d) {
@@ -9463,7 +9463,7 @@ function hP(e, t) {
     p.push(d);
   return { allDims: o, summedDims: p, idDims: u };
 }
-function fP(e, t) {
+function vP(e, t) {
   let n = new Array(e);
   n.fill(-1);
   for (let r = 0; r < t.length; ++r)
@@ -9473,7 +9473,7 @@ function fP(e, t) {
     n[r] === -1 && s.push(r);
   return n = n.filter((r) => r !== -1), { permutationIndices: n, expandDims: s };
 }
-function mP(e, t, n) {
+function xP(e, t, n) {
   let s = new Array(e);
   for (let r = 0; r < n.length; ++r) {
     let a = n[r].shape;
@@ -9481,29 +9481,29 @@ function mP(e, t, n) {
       s[t[r][i]] === void 0 ? s[t[r][i]] = a[i] : O(s[t[r][i]] === a[i], () => `Expected dimension ${s[t[r][i]]} at axis ${i} of input shaped ${JSON.stringify(a)}, but got dimension ${a[i]}`);
   }
 }
-function gP(e, t) {
+function wP(e, t) {
   let n = e, s = [], r = 0;
   e.length === 0 && n.push(-1), r = e.length + 1;
   for (let i = 0; i < r; ++i)
     s.push([]);
   let a = [];
   for (let i = 0; i < n.length; ++i) {
-    let o = n[i], u = yP(t, o);
+    let o = n[i], u = IP(t, o);
     for (let l of u)
       a.indexOf(l) === -1 && (s[i].push(l), a.push(l));
   }
   return { path: n, steps: s };
 }
-function bP(e) {
+function kP(e) {
   return e.every((t, n) => t === n);
 }
-function yP(e, t) {
+function IP(e, t) {
   let n = [];
   for (let s = 0; s < e.length; ++s)
     (e[s].length === 0 || e[s].indexOf(t) !== -1 || t === -1) && n.push(s);
   return n;
 }
-function vP(e, t, n = 0) {
+function SP(e, t, n = 0) {
   let s = [];
   if (typeof t == "number")
     O(e.shape[n] % t === 0, () => "Number of splits must evenly divide the axis."), s = new Array(t).fill(e.shape[n] / t);
@@ -9519,61 +9519,61 @@ function vP(e, t, n = 0) {
   }
   return s;
 }
-function xP(e) {
+function CP(e) {
   return `Received SparseTensor with denseShape[0] = 0 but
   indices.shape[0] = ${e}`;
 }
-function wP(e, t) {
+function NP(e, t) {
   return `indices(${e}, 0) is invalid: ${t} < 0`;
 }
-function kP(e, t, n) {
+function TP(e, t, n) {
   return `indices(${e}, 0) is invalid: ${t} >= ${n}`;
 }
-function IP(e, t) {
+function $P(e, t) {
   return `only one output dimension may be -1, not both ${e} and ${t}`;
 }
-function SP(e, t) {
+function _P(e, t) {
   return `size ${e} must be non-negative, not ${t}`;
 }
-function CP() {
+function AP() {
   return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
 }
-function NP(e, t) {
+function EP(e, t) {
   let n = pt(e), s = pt(t);
   return `Input to reshape is a SparseTensor with ${n}
   dense values, but the requested shape requires a multiple of ${s}. inputShape=${e} outputShape= ${t}`;
 }
-function TP(e, t) {
+function RP(e, t) {
   let n = pt(e), s = pt(t);
   return `Input to reshape is a tensor with ${n} dense values, but the requested shape has ${s}. inputShape=${e} outputShape=${t}`;
 }
-function $P() {
+function DP() {
   return "segment ids must be >= 0";
 }
-function _P() {
+function FP() {
   return "segment ids are not increasing";
 }
-function AP(e, t) {
+function OP(e, t) {
   return `Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`;
 }
-function EP(e, t, n) {
+function PP(e, t, n) {
   return `Bad: indices[${e}] == ${t} out of range [0, ${n})`;
 }
-var MI = {};
-Ae(MI, { collectGatherOpShapeInfo: () => FP, computeOutShape: () => DP, segOpComputeOptimalWindowSize: () => RP });
-function RP(e, t) {
+var WI = {};
+Ae(WI, { collectGatherOpShapeInfo: () => LP, computeOutShape: () => MP, segOpComputeOptimalWindowSize: () => zP });
+function zP(e, t) {
   let n = false, s;
-  for (e <= xb ? (s = e, n = true) : s = hd(e, Math.floor(Math.sqrt(e))); !n; )
+  for (e <= kb ? (s = e, n = true) : s = hd(e, Math.floor(Math.sqrt(e))); !n; )
     s > t || s === e ? n = true : s = hd(e, s + 1);
   return s;
 }
-function DP(e, t, n) {
+function MP(e, t, n) {
   let s = [], r = e.length;
   for (let a = 0; a < r; a++)
     a !== t ? s.push(e[a]) : s.push(n);
   return s;
 }
-function FP(e, t, n, s) {
+function LP(e, t, n, s) {
   let r = t.shape.length, a = e.shape.length;
   if (s !== 0 && (s < -r || s > r))
     throw new Error(`Expect batchDims in the range of [-${r}, ${r}], but got ${s}`);
@@ -9596,600 +9596,18 @@ function FP(e, t, n, s) {
     o.push(e.shape[p]), c *= e.shape[p];
   return { batchSize: u, sliceSize: c, outerSize: l, dimSize: i, outputShape: o };
 }
-function OP(e) {
+function BP(e) {
   try {
     return e.map((t) => md(t));
   } catch (t) {
     throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`);
   }
 }
-function PP(e) {
+function VP(e) {
   return e.map((t) => Dl(t));
 }
 var xs = {};
-Ae(xs, { nonMaxSuppressionV3Impl: () => RI, nonMaxSuppressionV4Impl: () => DI, nonMaxSuppressionV5Impl: () => FI, whereImpl: () => kI });
-var LI = { kernelName: io, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(e, yp(ce(n, "float32"), -1)) };
-} };
-var zP = { kernelName: sl, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => {
-    let s = ct(ce(n, "float32")), r = dn(ge(we(1), s));
-    return kt(xe(e, r));
-  } };
-} };
-var MP = { kernelName: rl, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => {
-    let s = dn(ge(ct(ce(n, "float32")), 1));
-    return xe(e, s);
-  } };
-} };
-var LP = { kernelName: Ir, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = ot(n.shape, s.shape);
-  return { a: () => {
-    let o = e, u = _t(n.shape, r);
-    return u.length > 0 && (o = ve(o, u)), G(o, n.shape);
-  }, b: () => {
-    let o = e, u = _t(s.shape, r);
-    return u.length > 0 && (o = ve(o, u)), G(o, s.shape);
-  } };
-} };
-var BP = { kernelName: ka, saveAllInputs: true, gradFunc: (e, t) => {
-  let n = {};
-  return t.forEach((s, r) => {
-    n[r] = () => e.clone();
-  }), n;
-} };
-var VP = { kernelName: Ia, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => je(n) };
-} };
-var WP = { kernelName: ol, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => je(n) };
-} };
-var UP = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => xe(e, dn(ge(we(1), ct(ce(n, "float32"))))) };
-} };
-var GP = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => {
-    let s = dn(ie(we(1), ct(ce(n, "float32"))));
-    return xe(e, s);
-  } };
-} };
-var HP = { kernelName: pl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = ot(n.shape, s.shape);
-  return { a: () => {
-    let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = _t(n.shape, r);
-    return l.length > 0 && (u = ve(u, l)), G(u, n.shape);
-  }, b: () => {
-    let o = ie(ct(n), ct(s)), u = kt(V(e, xe(n, o))), l = _t(s.shape, r);
-    return l.length > 0 && (u = ve(u, l)), G(u, s.shape);
-  } };
-} };
-var qP = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => xe(e, ie(ct(ce(n, "float32")), 1)) };
-} };
-var jP = { kernelName: dl, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => xe(e, ge(we(1), ct(ce(n, "float32")))) };
-} };
-function KP(e, t, n, s, r, a) {
-  let i = _(e, "dy", "avgPool3dGrad"), o = _(t, "input", "avgPool3dGrad"), u = i, l = o, c = false;
-  o.rank === 4 && (c = true, u = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]]), l = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), O(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), hn("avgPool3dGrad", r, a);
-  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = M.runKernel(og, p, d);
-  return c ? G(h, [h.shape[1], h.shape[2], h.shape[3], h.shape[4]]) : h;
-}
-var XP = L({ avgPool3dGrad_: KP });
-var YP = { kernelName: qd, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { filterSize: r, strides: a, pad: i, dimRoundingMode: o } = n;
-  return { x: () => XP(e, s, r, a, i, o) };
-} };
-function QP(e, t, n, s, r) {
-  let a = _(e, "dy", "avgPoolGrad"), i = _(t, "input", "avgPoolGrad");
-  O(i.rank === a.rank, () => `Rank of input (${i.rank}) does not match rank of dy (${a.rank})`);
-  let o = i, u = a, l = false;
-  i.rank === 3 && (l = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2]]), u = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), O(o.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);
-  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = M.runKernel(ig, c, p);
-  return l ? G(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
-}
-var ZP = L({ avgPoolGrad_: QP });
-var JP = { kernelName: Sa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { filterSize: r, strides: a, pad: i } = n;
-  return { x: () => ZP(e, s, r, a, i) };
-} };
-var ez = { kernelName: Ca, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { transposeA: a, transposeB: i } = n;
-  return !a && !i ? { a: () => We(e, r, false, true), b: () => We(s, e, true, false) } : !a && i ? { a: () => We(e, r, false, false), b: () => We(e, s, true, false) } : a && !i ? { a: () => We(r, e, false, true), b: () => We(s, e, false, false) } : { a: () => We(r, e, true, true), b: () => We(e, s, true, true) };
-} };
-var tz = { kernelName: oo, gradFunc: (e, t, n) => {
-  let { blockShape: s, crops: r } = n;
-  return { x: () => nb(e, s, r) };
-} };
-var nz = { kernelName: A$, gradFunc: (e, t, n) => {
-  let s = n, r = s.inputShape, a = s.shape, i = Array.from(a);
-  for (let u = r.length - 1; u >= 0; u--)
-    if (r[u] === a[u])
-      i[u] = 1;
-    else if (r[u] !== 1)
-      throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);
-  let o = [];
-  for (let u = 0; u < i.length; u++)
-    i[u] > 1 && o.push(u);
-  return { x: () => ve(e, o, true) };
-} };
-var sz = { kernelName: Na, gradFunc: (e) => ({ x: () => e.clone() }) };
-var rz = { kernelName: Ta, gradFunc: (e) => ({ x: () => je(e) }) };
-var az = { kernelName: Sr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
-  return { x: () => vn(Ds(Ko(s, r), Xo(s, a)), e, je(e)) };
-} };
-var iz = { kernelName: Kd, inputsToSave: ["x"], gradFunc: LI.gradFunc };
-var oz = { kernelName: uo, saveAllInputs: true, gradFunc: (e, t, n) => {
-  let s = t.map((u) => u.shape), { axis: r } = n, a = ts(r, t[0].shape)[0], i = s.map((u) => u[a]);
-  return Bn(e, i, a).map((u) => () => u);
-} };
-var uz = { kernelName: $a, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { dilations: a, strides: i, pad: o, dataFormat: u } = n;
-  return O(fr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => qg(s.shape, e, r, i, o, u), filter: () => pb(s, e, r.shape, i, o, u) };
-} };
-var lz = { kernelName: _a, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { strides: a, pad: i, dataFormat: o, dimRoundingMode: u } = n;
-  return { dy: () => ca(e, r, a, i, o, 1, u), filter: () => pb(e, s, r.shape, a, i, o, u) };
-} };
-function cz(e, t, n, s, r) {
-  let a = e;
-  e.rank === 4 && (a = G(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]));
-  let i = t;
-  i.rank === 4 && (i = G(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]])), O(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), O(i.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`), O(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), O(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), O(i.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);
-  let o = { x: a, dy: i }, u = { strides: s, pad: r, filterShape: n };
-  return M.runKernel(dg, o, u);
-}
-var dz = L({ conv3DBackpropFilter_: cz });
-var pz = { kernelName: Xd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
-  let { dilations: s, strides: r, pad: a } = n;
-  O(fr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
-  let [i, o] = t;
-  return { x: () => tI(i.shape, e, o, r, a), filter: () => dz(i, e, o.shape, r, a) };
-} };
-var hz = { kernelName: Aa, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(kt(bI(ce(n, "float32"))), e) };
-} };
-var fz = { kernelName: Ea, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(yI(ce(n, "float32")), e) };
-} };
-var mz = { kernelName: lo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
-  return { x: () => {
-    let o = lI([r], s.rank), u = sI(e, r, a, !i);
-    return o != null && (u = qe(u, o)), u;
-  } };
-} };
-var gz = { kernelName: Ra, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
-  let { dilations: s, strides: r, pad: a, dimRoundingMode: i } = n, o = s == null ? [1, 1] : s;
-  O(fr(o), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);
-  let [u, l] = t;
-  return O(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), O(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), O(Ps(r, o), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${o}'.`), hn("depthwiseConv2d", a, i), { x: () => _I(u.shape, e, l, r, a, o, i), filter: () => $I(u, e, l.shape, r, a, o, i) };
-} };
-var bz = { kernelName: Yd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
-  let [s, r] = t, a = { x: s, filter: r, dy: e }, i = { x: s, filter: r, dy: e };
-  return { x: () => M.runKernel(Yf, a, n), filter: () => M.runKernel(Qf, i, n) };
-} };
-var yz = { kernelName: Fa, outputsToSave: [true], gradFunc: (e, t) => {
-  let [n] = t, s = { dy: e, y: n };
-  return { x: () => M.runKernel(bg, s) };
-} };
-var vz = { kernelName: fl, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t, s = V(Xn(kt(ct(n))), 2 / Math.sqrt(Math.PI));
-  return { x: () => V(e, s) };
-} };
-var xz = { kernelName: Oa, outputsToSave: [true], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(e, n) };
-} };
-var wz = { kernelName: fo, inputsToSave: ["input"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { input: () => G(e, n.shape) };
-} };
-var kz = { kernelName: mo, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(e, Xn(n)) };
-} };
-var Iz = { kernelName: Pa, gradFunc: (e) => ({ x: () => je(e) }) };
-var Sz = { kernelName: za, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = ot(n.shape, s.shape);
-  return { a: () => {
-    let o = xe(e, ce(s, "float32")), u = _t(n.shape, r);
-    return u.length > 0 ? G(ve(o, u), n.shape) : o;
-  }, b: () => {
-    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
-    u.length > 0 && (o = G(ve(o, u), s.shape));
-    let l = ct(s);
-    return kt(xe(o, ce(l, "float32")));
-  } };
-} };
-var Cz = { kernelName: Ma, inputsToSave: ["x", "mean", "variance", "scale"], gradFunc: (e, t, n) => {
-  let { varianceEpsilon: s } = n, [r, a, i, o] = t, u = o == null ? we(1) : o, l = _t(a.shape, r.shape), c = [];
-  if (a.rank === 1) {
-    for (let x = 0; x < r.shape.length - 1; ++x)
-      c.push(r.shape[x]);
-    c.push(1);
-  }
-  let p = ge(r, a), d = V(e, u), h = mI(ie(i, we(s))), f = V(V(V(h, h), h), we(-0.5));
-  return { x: () => a.rank === 1 ? G(V(V(e, ps(G(h, [1, 1, 1, a.shape[0]]), c)), u), r.shape) : G(V(V(e, h), u), r.shape), mean: () => {
-    let x = V(V(h, we(-1)), d);
-    return a.rank === 1 && (x = ve(x, l)), G(x, a.shape);
-  }, variance: () => {
-    let x = V(V(f, p), d);
-    return a.rank === 1 && (x = ve(x, l)), G(x, a.shape);
-  }, scale: () => {
-    let x = V(p, h), k = V(e, x);
-    return a.rank === 1 && (k = ve(k, l)), G(k, a.shape);
-  }, offset: () => {
-    let x = e;
-    return a.rank === 1 && (x = ve(x, l)), G(x, a.shape);
-  } };
-} };
-var Nz = { kernelName: bo, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
-  let [s, r] = t, { axis: a } = n, i = ts(a, s.shape)[0];
-  return { x: () => {
-    let u = s.shape, l = r.size, c = u.slice(0, i), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = yx(0, p), m = yx(p + 1, p + 1 + h), g = vx([c, [l], d]), b = G(e, g), y = G(r, [l]), v = vx([[p], f, m]), x = qe(b, v), k = V3(x, y, s.shape[i]), T = Zg(v);
-    return k = qe(k, T), k;
-  }, indices: () => r };
-} };
-function yx(e, t) {
-  let n = [];
-  for (let s = e; s < t; ++s)
-    n.push(s);
-  return n;
-}
-function vx(e) {
-  let t = [];
-  for (let n = 0; n < e.length; ++n)
-    for (let s = 0; s < e[n].length; ++s)
-      t.push(e[n][s]);
-  return t;
-}
-var Tz = { kernelName: La, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t;
-  return { a: () => je(n), b: () => je(s) };
-} };
-var $z = { kernelName: Ba, gradFunc: (e) => ({ x: () => ce(e, "float32") }) };
-var _z = { kernelName: gl, gradFunc: (e) => ({ x: () => je(e) }) };
-var Az = { kernelName: bl, gradFunc: (e) => ({ x: () => je(e) }) };
-var Ez = { kernelName: yl, gradFunc: (e) => ({ x: () => je(e) }) };
-var Rz = { kernelName: Va, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { alpha: r } = n, a = Wn(s, 0);
-  return { x: () => vn(a, e, V(e, r)) };
-} };
-var Dz = { kernelName: vl, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => xe(e, ie(n, 1)) };
-} };
-var Fz = { kernelName: Wa, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => xe(e, ce(n, "float32")) };
-} };
-var Oz = { kernelName: E$, inputsToSave: [], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s] = t, { axis: r } = n;
-  return { logits: () => {
-    let i = Xn(s);
-    return ge(e, V(ve(e, r, true), i));
-  } };
-} };
-function Pz(e, t, n, s = 5, r = 1, a = 1, i = 0.5) {
-  let o = { x: e, y: t, dy: n }, u = { depthRadius: s, bias: r, alpha: a, beta: i };
-  return M.runKernel(wg, o, u);
-}
-var zz = L({ localResponseNormalizationBackprop_: Pz });
-var Mz = { kernelName: ep, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s, r] = t, { depthRadius: a, bias: i, alpha: o, beta: u } = n;
-  return { x: () => zz(s, r, e, a, i, o, u) };
-} };
-function BI(e, t, n, s) {
-  return t.rank < n.rank && (t = G(t, da(t.shape, s))), e.rank < n.rank && (e = G(e, da(e.shape, s))), { x: () => V(e, ce(Kn(n, t), e.dtype)) };
-}
-var xx = { kernelName: Ua, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let s = n, { reductionIndices: r } = s, a = t[0], i = t[1], o = ts(r, a.shape), u = BI(e, i, a, o);
-  return { x: () => u.x() };
-} };
-var Lz = { kernelName: Ga, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t;
-  return { a: () => V(e, ce(Ko(n, s), "float32")), b: () => V(e, ce(aI(n, s), "float32")) };
-} };
-function Bz(e, t, n, s, r, a, i) {
-  let o = _(e, "dy", "maxPool3dGrad"), u = _(t, "input", "maxPool3dGrad"), l = _(n, "output", "maxPool3dGrad"), c = o, p = u, d = l, h = false;
-  u.rank === 4 && (h = true, c = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]]), p = G(u, [1, u.shape[0], u.shape[1], u.shape[2], u.shape[3]]), d = G(l, [1, l.shape[0], l.shape[1], l.shape[2], l.shape[3]])), O(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), O(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), O(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), hn("maxPool3dGrad", a, i);
-  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = M.runKernel(Ig, f, m);
-  return h ? G(g, [g.shape[1], g.shape[2], g.shape[3], g.shape[4]]) : g;
-}
-var Vz = L({ maxPool3dGrad_: Bz });
-var Wz = { kernelName: tp, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s, r] = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = n;
-  return { x: () => Vz(e, s, r, a, i, o, u) };
-} };
-function Uz(e, t, n, s, r, a, i) {
-  let o = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
-  O(u.rank === o.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${o.rank})`), O(o.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`), O(u.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`), hn("maxPoolGrad", a, i);
-  let c = { dy: o, input: u, output: l }, p = { filterSize: s, strides: r, pad: a, dimRoundingMode: i };
-  return M.runKernel(kg, c, p);
-}
-var Gz = L({ maxPoolGrad_: Uz });
-var Hz = { kernelName: Ha, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let [s, r] = t, { filterSize: a, strides: i, pad: o } = n;
-  return { x: () => Gz(e, s, r, a, i, o) };
-} };
-var qz = { kernelName: qa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { axis: r } = n, a = ts(r, s.shape), o = uI(s.shape, a)[1], u = pt(o);
-  return { x: () => {
-    let c = s.shape.slice();
-    a.forEach((h) => {
-      c[h] = 1;
-    });
-    let p = G(e, c);
-    return xe(V(p, Mn(s.shape, "float32")), u);
-  } };
-} };
-var jz = { kernelName: ja, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
-  let s = n, { axis: r } = s, [a, i] = t, o = ts(r, a.shape), u = BI(e, i, a, o);
-  return { x: () => u.x() };
-} };
-var Kz = { kernelName: Ka, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t;
-  return { a: () => V(e, ce(Xo(n, s), "float32")), b: () => V(e, ce(Wn(n, s), "float32")) };
-} };
-var Xz = { kernelName: Xa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
-  return { x: () => He(e, a, s.shape) };
-} };
-var Yz = { kernelName: wl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = ot(n.shape, s.shape);
-  return { a: () => {
-    let o = _t(n.shape, r);
-    return o.length > 0 ? G(ve(e, o), n.shape) : e;
-  }, b: () => {
-    let o = V(e, kt(mp(xe(n, s)))), u = _t(s.shape, r);
-    return u.length > 0 ? G(ve(o, u), s.shape) : o;
-  } };
-} };
-var Qz = { kernelName: Ya, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = ot(n.shape, s.shape);
-  return { a: () => {
-    let o = V(e, ce(s, "float32")), u = _t(n.shape, r);
-    return u.length > 0 ? G(ve(o, u), n.shape) : o;
-  }, b: () => {
-    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
-    return u.length > 0 ? G(ve(o, u), s.shape) : o;
-  } };
-} };
-var Zz = { kernelName: Io, gradFunc: (e) => ({ x: () => kt(e) }) };
-var Jz = { kernelName: $o, inputsToSave: ["indices"], gradFunc: (e, t) => {
-  let n = t[0];
-  return { indices: () => $t(n.shape, "float32") };
-} };
-var eM = { kernelName: To, gradFunc: (e) => ({ x: () => je(e) }) };
-var tM = { kernelName: _o, saveAllInputs: true, gradFunc: (e, t, n) => {
-  let { axis: s } = n;
-  return Fs(e, s).map((a) => () => a);
-} };
-var wx = { kernelName: Qa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
-  return { x: () => He(e, a, s.shape) };
-} };
-var nM = { kernelName: Za, inputsToSave: ["a", "b"], outputsToSave: [true], gradFunc: (e, t) => {
-  let [n, s, r] = t, a = n, i = s, o = ot(a.shape, i.shape);
-  return { a: () => {
-    let c = ce(i, "float32"), p = V(e, V(c, pa(a, ge(c, we(1))))), d = _t(a.shape, o);
-    return d.length > 0 && (p = ve(p, d)), G(p, a.shape);
-  }, b: () => {
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-var aM = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => xe(e, kt(ct(n))) };
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-var iM = { kernelName: ni, inputsToSave: ["x"], gradFunc: (e, t) => {
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-var lM = { kernelName: ti, inputsToSave: ["images"], gradFunc: (e, t, n) => {
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-  return { images: () => M.runKernel(Tg, r, n) };
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-var cM = { kernelName: Cl, inputsToSave: ["images"], gradFunc: (e, t, n) => {
-  let [s] = t, r = { dy: e, images: s };
-  return { images: () => M.runKernel(Ng, r, n) };
-} };
-var dM = { kernelName: Ro, gradFunc: (e, t, n) => {
-  let { dims: s } = n, r = ts(s, e.shape);
-  return { x: () => Zn(e, r) };
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-var pM = { kernelName: Do, gradFunc: (e) => ({ x: () => je(e) }) };
-var hM = { kernelName: si, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => kt(xe(e, V(pa(n, 1.5), 2))) };
-} };
-var fM = { kernelName: Oo, inputsToSave: ["condition"], gradFunc: (e, t) => {
-  let [n] = t;
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-var mM = { kernelName: Nl, inputsToSave: ["x"], gradFunc: (e, t) => {
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-  return { x: () => {
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-  return { x: () => V(e, V(n, ge(we(1), n))) };
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-var bM = { kernelName: Tl, gradFunc: (e) => ({ x: () => je(e) }) };
-var yM = { kernelName: ri, inputsToSave: ["x"], gradFunc: (e, t) => {
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-var vM = { kernelName: zo, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(nI(ce(n, "float32")), e) };
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-var IM = { kernelName: ii, inputsToSave: ["x"], gradFunc: (e, t) => {
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-  return { x: () => xe(e, V(dn(ce(n, "float32")), 2)) };
-} };
-var SM = { kernelName: Al, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(e, V(ce(n, "float32"), 2)) };
-} };
-var CM = { kernelName: li, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
-  let [n, s] = t, r = we(2);
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-  let o = G(e, r), u = V(o, Mn(s.shape, "float32"));
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-var _M = { kernelName: Vo, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => xe(e, ct(jg(n))) };
-} };
-var AM = { kernelName: di, outputsToSave: [true], gradFunc: (e, t) => {
-  let [n] = t;
-  return { x: () => V(ge(we(1), ct(n)), e) };
-} };
-var EM = { kernelName: Cr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
-  let [s] = t, { reps: r } = n;
-  return { x: () => {
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-      for (let o = 0; o < r[0]; ++o)
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-      for (let o = 0; o < r[0]; ++o)
-        for (let u = 0; u < r[1]; ++u)
-          for (let l = 0; l < r[2]; ++l)
-            for (let c = 0; c < r[3]; ++c)
-              i = ie(i, He(e, [o * s.shape[0], u * s.shape[1], l * s.shape[2], c * s.shape[3]], [s.shape[0], s.shape[1], s.shape[2], s.shape[3]]));
-    else
-      throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);
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-} };
-var RM = { kernelName: pi, gradFunc: (e, t, n) => {
-  let s = n, { perm: r } = s, a = Zg(r);
-  return { x: () => qe(e, a) };
-} };
-var DM = { kernelName: Go, gradFunc: (e, t, n) => {
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-  return { value: () => Jn(e, r) };
-} };
-var FM = { kernelName: up, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
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-  return { x: () => OM(e, n) };
-} };
-function OM(e, t) {
-  let n = $r(t, je(t)), s = Ku(e, n), r = Ko(t, we(0, "int32")), a = s.rank - r.rank;
-  for (let o = 0; o < a; ++o)
-    r = Pn(r, o + 1);
-  r = Ds(r, Mn(s.shape, "bool"));
-  let i = je(s);
-  return vn(r, s, i);
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-var PM = { kernelName: Ho, gradFunc: (e) => ({ x: () => je(e) }) };
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-for (let e of zM)
-  D$(e);
-var MM = {};
-Ae(MM, { maxNorm: () => WM, minMaxNorm: () => HM, nonNeg: () => GM, unitNorm: () => UM });
-var Bf;
-function Rt() {
-  return Bf == null && (Bf = zA().epsilon()), Bf;
-}
-function bs() {
-  return "channelsLast";
-}
+Ae(xs, { nonMaxSuppressionV3Impl: () => PI, nonMaxSuppressionV4Impl: () => zI, nonMaxSuppressionV5Impl: () => MI, whereImpl: () => NI });
 var Vs = class extends Error {
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@@ -10210,12 +9628,43 @@ var Fe = class extends Error {
     super(e), Object.setPrototypeOf(this, Fe.prototype);
   }
 };
-var VI = class extends Error {
+var UI = class extends Error {
   constructor(e) {
-    super(e), Object.setPrototypeOf(this, VI.prototype);
+    super(e), Object.setPrototypeOf(this, UI.prototype);
   }
 };
-function fa(e, t) {
+var GI = class {
+  constructor(e) {
+    this.maxEntries = e || 100, this.cache = /* @__PURE__ */ new Map();
+  }
+  get(e) {
+    let t;
+    return this.cache.has(e) && (t = this.cache.get(e), this.cache.delete(e), this.cache.set(e, t)), t;
+  }
+  put(e, t) {
+    if (this.cache.has(e))
+      this.cache.delete(e);
+    else if (this.cache.size >= this.maxEntries) {
+      let n = this.cache.keys().next().value;
+      this.cache.delete(n);
+    }
+    this.cache.set(e, t);
+  }
+  getMaxEntries() {
+    return this.maxEntries;
+  }
+  setMaxEntries(e) {
+    if (e < 0)
+      throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${e}.`);
+    if (this.maxEntries > e)
+      for (let t = 0; t < this.maxEntries - e; t++) {
+        let n = this.cache.keys().next().value;
+        this.cache.delete(n);
+      }
+    this.maxEntries = e;
+  }
+};
+function ma(e, t) {
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     let n = [];
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@@ -10228,9 +9677,9 @@ function fa(e, t) {
 }
 function Cs(e, t) {
   if (!e)
-    throw new VI(t);
+    throw new UI(t);
 }
-function Sx(e, t) {
+function xx(e, t) {
   let n = 0;
   for (let s of e)
     s === t && n++;
@@ -10246,25 +9695,25 @@ function Ws(e) {
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 }
-function Kr(e) {
+function Xr(e) {
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-var Gn = {};
-function wb(e) {
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+function Ib(e) {
   if (e == null)
     return null;
   let t = {};
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-function mm(e) {
+function bm(e) {
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-      e.forEach((t) => mm(t));
+      e.forEach((t) => bm(t));
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-        s != null && typeof s == "object" && (!Array.isArray(s) && s.type === "ndarray" && typeof s.value == "number" ? e[n] = s.value : mm(s));
+        s != null && typeof s == "object" && (!Array.isArray(s) && s.type === "ndarray" && typeof s.value == "number" ? e[n] = s.value : bm(s));
       }
     }
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@@ -10273,8 +9722,8 @@ function Ll(e, t = {}, n = {}, s = "object", r = false) {
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     if (a in n)
       i = n[a];
-    else if (a in Gn)
-      i = Gn[a];
+    else if (a in Hn)
+      i = Hn[a];
     else if (i = t[a], i == null)
       throw new U(`Unknown ${s}: ${e}. This may be due to one of the following reasons:
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@@ -10286,40 +9735,40 @@ function Ll(e, t = {}, n = {}, s = "object", r = false) {
       throw new U(`${s}: Improper config format: ${JSON.stringify(a)}.
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-    if (i in n ? [o, u] = n[i] : i in Gn ? [o, u] = Gn.className : i in t && ([o, u] = t[i]), o == null)
+    if (i in n ? [o, u] = n[i] : i in Hn ? [o, u] = Hn.className : i in t && ([o, u] = t[i]), o == null)
       throw new U(`Unknown ${s}: ${i}. This may be due to one of the following reasons:
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     if (u != null) {
       let l = {};
-      for (let h of Object.keys(Gn))
-        l[h] = Gn[h];
+      for (let h of Object.keys(Hn))
+        l[h] = Hn[h];
       for (let h of Object.keys(n))
         l[h] = n[h];
       let c = a.config;
       c.customObjects = l;
-      let p = { ...Gn };
+      let p = { ...Hn };
       for (let h of Object.keys(n))
-        Gn[h] = n[h];
-      mm(a.config);
+        Hn[h] = n[h];
+      bm(a.config);
       let d = u(o, a.config, n, r);
-      return Gn = { ...p }, d;
+      return Hn = { ...p }, d;
     } else {
-      let l = { ...Gn };
+      let l = { ...Hn };
       for (let p of Object.keys(n))
-        Gn[p] = n[p];
+        Hn[p] = n[p];
       let c = new o(a.config);
-      return Gn = { ...l }, c;
+      return Hn = { ...l }, c;
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+function WP(e, t) {
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 function Uc(e, t) {
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+  return -1 * WP(e, t);
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-function cr(e) {
+function dr(e) {
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   return t;
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-function BM(e) {
+function UP(e) {
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   for (let t in e)
@@ -10335,182 +9784,100 @@ function BM(e) {
       return false;
   return true;
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-function mi(e, t, n) {
+function yi(e, t, n) {
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     throw new U(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`);
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-function kb(e, t, n = 0, s = 1 / 0) {
+function Sb(e, t, n = 0, s = 1 / 0) {
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 function Bt(e, t) {
-  Array.isArray(e) ? (w.assert(e.length > 0, () => `${t} is unexpectedly an empty array.`), e.forEach((n, s) => Bt(n, `element ${s + 1} of ${t}`))) : w.assert(Number.isInteger(e) && e > 0, () => `Expected ${t} to be a positive integer, but got ${WI(e)}.`);
+  Array.isArray(e) ? (w.assert(e.length > 0, () => `${t} is unexpectedly an empty array.`), e.forEach((n, s) => Bt(n, `element ${s + 1} of ${t}`))) : w.assert(Number.isInteger(e) && e > 0, () => `Expected ${t} to be a positive integer, but got ${HI(e)}.`);
 }
-function WI(e) {
-  return e === null ? "null" : Array.isArray(e) ? "[" + e.map((t) => WI(t)).join(",") + "]" : typeof e == "string" ? `"${e}"` : `${e}`;
+function HI(e) {
+  return e === null ? "null" : Array.isArray(e) ? "[" + e.map((t) => HI(t)).join(",") + "]" : typeof e == "string" ? `"${e}"` : `${e}`;
 }
-function VM(e, t, n) {
+function GP(e, t, n) {
   let s = n != null ? n() : w.now(), r;
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     let o = n != null ? n() : w.now();
     return o - s < t || (s = o, r = e(...i)), r;
   };
 }
-function UI(e) {
+function qI(e) {
   return e === "relu" ? "relu" : e === "linear" ? "linear" : e === "elu" ? "elu" : null;
 }
-function Ib(e, t) {
-  return j(() => dn(ve(V(e, e), t, true)));
+var HP = 0;
+function jI() {
+  return HP++;
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-var Bl = class extends ae.Serializable {
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-ae.registerClass(Cb);
-var Nb = class extends Bl {
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-ae.registerClass(Nb);
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-  apply(e) {
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-var Cx = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
-function Ot(e) {
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+var Gc = {};
+function Np(e = "") {
+  return e in Gc || (Gc[e] = 0), Gc[e] += 1, e + Gc[e].toString();
 }
-function Nx(e, t = {}) {
-  return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "constraint");
-}
-function Pt(e) {
-  if (e == null)
-    return null;
-  if (typeof e == "string") {
-    let n = { className: e in Cx ? Cx[e] : e, config: {} };
-    return Nx(n);
-  } else
-    return e instanceof Bl ? e : Nx(e);
-}
-function WM(e) {
-  return new Sb(e);
-}
-function UM(e) {
-  return new Cb(e);
-}
-function GM() {
-  return new Nb();
-}
-function HM(e) {
-  return new Tb(e);
-}
-var qM = {};
-Ae(qM, { constant: () => mL, glorotNormal: () => kL, glorotUniform: () => wL, heNormal: () => IL, heUniform: () => SL, identity: () => vL, leCunNormal: () => CL, leCunUniform: () => NL, ones: () => fL, orthogonal: () => TL, randomNormal: () => bL, randomUniform: () => gL, truncatedNormal: () => yL, varianceScaling: () => xL, zeros: () => hL });
-var jM = ["channelsFirst", "channelsLast"];
-var KM = ["nearest", "bilinear"];
-var XM = ["valid", "same", "causal"];
-var YM = ["max", "avg"];
-var QM = ["sum", "mul", "concat", "ave"];
-var zi = /* @__PURE__ */ new Map();
+var qP = ["channelsFirst", "channelsLast"];
+var jP = ["nearest", "bilinear"];
+var KP = ["valid", "same", "causal"];
+var XP = ["max", "avg"];
+var YP = ["sum", "mul", "concat", "ave"];
+var Li = /* @__PURE__ */ new Map();
 function Ct(e) {
-  mi(jM, "DataFormat", e);
+  yi(qP, "DataFormat", e);
 }
-function ZM(e) {
-  mi(KM, "InterpolationFormat", e);
+function QP(e) {
+  yi(jP, "InterpolationFormat", e);
 }
-function Un(e) {
-  mi(XM, "PaddingMode", e);
+function Gn(e) {
+  yi(KP, "PaddingMode", e);
 }
-function GI(e) {
-  mi(YM, "PoolMode", e);
+function KI(e) {
+  yi(XP, "PoolMode", e);
 }
-var Mu = [];
-var Tx = "/";
-function ea(e, t) {
-  Mu.push(e);
+var Lu = [];
+var wx = "/";
+function ta(e, t) {
+  Lu.push(e);
   try {
     let n = t();
-    return Mu.pop(), n;
+    return Lu.pop(), n;
   } catch (n) {
-    throw Mu.pop(), n;
+    throw Lu.pop(), n;
   }
 }
-function JM() {
-  return Mu.length === 0 ? "" : Mu.join(Tx) + Tx;
+function ZP() {
+  return Lu.length === 0 ? "" : Lu.join(wx) + wx;
 }
-function HI(e) {
-  if (!jI(e))
+function XI(e) {
+  if (!QI(e))
     throw new Error("Not a valid tensor name: '" + e + "'");
-  return JM() + e;
+  return ZP() + e;
 }
-function qI(e) {
-  if (!jI(e))
+function YI(e) {
+  if (!QI(e))
     throw new Error("Not a valid tensor name: '" + e + "'");
-  zi.has(e) || zi.set(e, 0);
-  let t = zi.get(e);
-  if (zi.set(e, zi.get(e) + 1), t > 0) {
+  Li.has(e) || Li.set(e, 0);
+  let t = Li.get(e);
+  if (Li.set(e, Li.get(e) + 1), t > 0) {
     let n = `${e}_${t}`;
-    return zi.set(n, 1), n;
+    return Li.set(n, 1), n;
   } else
     return e;
 }
-var eL = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
-function jI(e) {
-  return !!e.match(eL);
+var JP = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
+function QI(e) {
+  return !!e.match(JP);
 }
-function tL(e) {
+function ez(e) {
   return e === parseInt(e.toString(), 10);
 }
-function dr(e, t, n) {
+function pr(e, t, n) {
   t == null && (t = 0), n == null && (n = e.length);
   let s = 1;
   for (let r = t; r < n; ++r)
     s *= e[r];
   return s;
 }
-function Zi(e) {
+function eo(e) {
   if (e.length === 0)
     return Number.NaN;
   let t = Number.POSITIVE_INFINITY;
@@ -10520,7 +9887,7 @@ function Zi(e) {
   }
   return t;
 }
-function gr(e) {
+function br(e) {
   if (e.length === 0)
     return Number.NaN;
   let t = Number.NEGATIVE_INFINITY;
@@ -10530,7 +9897,7 @@ function gr(e) {
   }
   return t;
 }
-function ys(e, t) {
+function bs(e, t) {
   if (t < e)
     throw new U(`end (${t}) < begin (${e}) is forbidden.`);
   let n = [];
@@ -10538,60 +9905,67 @@ function ys(e, t) {
     n.push(s);
   return n;
 }
-function Sp(e, t) {
+var Wf;
+function Rt() {
+  return Wf == null && (Wf = UA().epsilon()), Wf;
+}
+function ys() {
+  return "channelsLast";
+}
+function Tp(e, t) {
   return ce(e, t);
 }
-function Vl(e, t = -1) {
+function Bl(e, t = -1) {
   let n = e.shape.slice();
   return t < 0 && (t = n.length + t + 1), n.splice(t, 0, 1), G(e, n);
 }
-function nL(e, t) {
+function tz(e, t) {
   return j(() => {
     if (e.shape.length !== 2)
       throw new U(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);
-    let n = Vl(e, 1);
-    return gm(n, [1, t, 1]);
+    let n = Bl(e, 1);
+    return ym(n, [1, t, 1]);
   });
 }
-function sL(e) {
-  let t = [dr(e.shape)];
+function nz(e) {
+  let t = [pr(e.shape)];
   return G(e, t);
 }
-function rL(e) {
+function sz(e) {
   if (e.rank <= 1)
     throw new U(`batchFlatten requires a minimum rank of 2. Got rank: ${e.rank}.`);
-  let t = [e.shape[0], dr(e.shape, 1)];
+  let t = [e.shape[0], pr(e.shape, 1)];
   return G(e, t);
 }
-function ta(e, t, n) {
+function na(e, t, n) {
   return j(() => {
     switch (e.rank) {
       case 1:
-        return ib(e, t, n);
+        return ub(e, t, n);
       case 2:
-        return vI(e, [t, 0], [n, e.shape[1]]);
+        return II(e, [t, 0], [n, e.shape[1]]);
       case 3:
-        return ob(e, [t, 0, 0], [n, e.shape[1], e.shape[2]]);
+        return lb(e, [t, 0, 0], [n, e.shape[1], e.shape[2]]);
       case 4:
         return kd(e, [t, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3]]);
       case 5:
-        return He(e, [t, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4]]);
+        return qe(e, [t, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4]]);
       case 6:
-        return He(e, [t, 0, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4], e.shape[5]]);
+        return qe(e, [t, 0, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4], e.shape[5]]);
       default:
         throw new U(`sliceAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`);
     }
   });
 }
-function Vf(e, t, n) {
+function Uf(e, t, n) {
   return j(() => {
     switch (e.rank) {
       case 1:
-        return ib(e, t, n);
+        return ub(e, t, n);
       case 2:
-        return vI(e, [0, t], [e.shape[0], n]);
+        return II(e, [0, t], [e.shape[0], n]);
       case 3:
-        return ob(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
+        return lb(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
       case 4:
         return kd(e, [0, 0, 0, t], [e.shape[0], e.shape[1], e.shape[2], n]);
       default:
@@ -10599,41 +9973,41 @@ function Vf(e, t, n) {
     }
   });
 }
-function Gc(e, t, n, s) {
+function Hc(e, t, n, s) {
   return j(() => {
     switch (e.rank) {
       case 1:
-        return ib(e, t, n);
+        return ub(e, t, n);
       case 2:
         switch (s) {
           case 1:
-            return ta(e, t, n);
+            return na(e, t, n);
           case 2:
-            return Vf(e, t, n);
+            return Uf(e, t, n);
           default:
             throw new U(`The axis is not within the rank of the tensor ${s}`);
         }
       case 3:
         switch (s) {
           case 1:
-            return ta(e, t, n);
+            return na(e, t, n);
           case 2:
-            return ob(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
+            return lb(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
           case 3:
-            return Vf(e, t, n);
+            return Uf(e, t, n);
           default:
             throw new U(`The axis is not within the rank of the tensor ${s}`);
         }
       case 4:
         switch (s) {
           case 1:
-            return ta(e, t, n);
+            return na(e, t, n);
           case 2:
             return kd(e, [0, t, 0, 0], [e.shape[0], n, e.shape[2], e.shape[3]]);
           case 3:
             return kd(e, [0, 0, t, 0], [e.shape[0], e.shape[1], n, e.shape[3]]);
           case 4:
-            return Vf(e, t, n);
+            return Uf(e, t, n);
           default:
             throw new U(`The axis is not within the rank of the tensor ${s}`);
         }
@@ -10642,31 +10016,31 @@ function Gc(e, t, n, s) {
     }
   });
 }
-function $b(e, t = -1) {
+function Cb(e, t = -1) {
   let n;
   return t < 0 && (n = e[0].rank, n !== 0 ? t = n : t = 0), t === e[0].rank && (t = -1), Ft(e, t);
 }
-function $x(e, t) {
+function kx(e, t) {
   switch (e.rank) {
     case 1:
-      return VE([e, t]);
+      return jE([e, t]);
     case 2:
-      return UE([e, t], 0);
+      return XE([e, t], 0);
     case 3:
-      return HE([e, t], 0);
+      return QE([e, t], 0);
     case 4:
-      return jE([e, t], 0);
+      return JE([e, t], 0);
     default:
       throw new U(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`);
   }
 }
-function gm(e, t) {
+function ym(e, t) {
   if (Array.isArray(t) || (t = [t]), e.rank !== t.length)
     throw new U(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);
   return ps(e, t);
 }
-function Cp(e, t = 0, n = 1, s, r) {
-  return KD(e, t, n, s, r);
+function $p(e, t = 0, n = 1, s, r) {
+  return JD(e, t, n, s, r);
 }
 function Es(e, t, n, s) {
   if (e.rank < 2 || t.rank < 2)
@@ -10677,23 +10051,23 @@ function Es(e, t, n, s) {
       throw new Fe(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and  y shape = ${t.shape}`);
   }
   if (e.rank === 2 && t.rank === 2)
-    return ha.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? bm(e.rank, s, bs()) : null, activation: n });
+    return fa.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? vm(e.rank, s, ys()) : null, activation: n });
   {
     let r = e.shape.slice(), a = r.pop();
     e = G(e, [-1, a]);
     let i = t.shape.slice(), o = i.pop(), u = i.pop(), l = [...i, o], c = Array.from({ length: t.rank }, (f, m) => m === 0 ? t.rank - 2 : m <= t.rank - 2 ? m - 1 : m);
-    t = G(qe(t, c), [u, -1]);
+    t = G(Ge(t, c), [u, -1]);
     let p = [...r, ...l], d = false, h = false;
-    return G(ha.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? bm(e.rank, s, bs()) : null, activation: n }), p);
+    return G(fa.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? vm(e.rank, s, ys()) : null, activation: n }), p);
   }
 }
-function KI(e, t, n) {
-  return j(() => (Array.isArray(t) ? t = Zt(t, "int32") : t = ce(t, "int32"), Ku(e, t, n)));
+function ZI(e, t, n) {
+  return j(() => (Array.isArray(t) ? t = Zt(t, "int32") : t = ce(t, "int32"), Xu(e, t, n)));
 }
-function Wl(e) {
+function Vl(e) {
   return V(e, e);
 }
-function bm(e, t, n) {
+function vm(e, t, n) {
   let s = t.shape;
   if (t.rank !== 1 && t.rank !== e)
     throw new U(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);
@@ -10717,35 +10091,35 @@ function bm(e, t, n) {
   throw new U(`Unsupported input rank by biasAdd: ${t.rank}`);
 }
 function ws(e, t, n) {
-  return j(() => (n == null && (n = bs()), Ct(n), ie(e, bm(e.rank, t, n))));
+  return j(() => (n == null && (n = ys()), Ct(n), ie(e, vm(e.rank, t, n))));
 }
-function aL(e, t = 1) {
+function rz(e, t = 1) {
   if (t !== 1)
     throw new Fe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);
-  return fp(e);
+  return gp(e);
 }
-function iL(e) {
+function az(e) {
   return j(() => xe(e, ie(Mt(e), 1)));
 }
-function XI(e, t, n, s) {
-  return j(() => nF(e, t, n, s));
+function JI(e, t, n, s) {
+  return j(() => oF(e, t, n, s));
 }
-function oL(e) {
+function iz(e) {
   return j(() => {
     let t = ie(0.5, V(0.2, e));
     return Vn(t, 0, 1);
   });
 }
-function Ul(e, t, n = false) {
+function Wl(e, t, n = false) {
   return n ? e() : t();
 }
-var uL = ["fanIn", "fanOut", "fanAvg"];
-var lL = ["normal", "uniform", "truncatedNormal"];
-function cL(e) {
-  mi(uL, "FanMode", e);
+var oz = ["fanIn", "fanOut", "fanAvg"];
+var uz = ["normal", "uniform", "truncatedNormal"];
+function lz(e) {
+  yi(oz, "FanMode", e);
 }
-function dL(e) {
-  mi(lL, "Distribution", e);
+function cz(e) {
+  yi(uz, "Distribution", e);
 }
 var ns = class extends ae.Serializable {
   fromConfigUsesCustomObjects() {
@@ -10755,21 +10129,21 @@ var ns = class extends ae.Serializable {
     return {};
   }
 };
-var _b = class extends ns {
+var Nb = class extends ns {
   apply(e, t) {
     return $t(e, t);
   }
 };
-_b.className = "Zeros";
-ae.registerClass(_b);
-var Np = class extends ns {
+Nb.className = "Zeros";
+ae.registerClass(Nb);
+var _p = class extends ns {
   apply(e, t) {
     return Mn(e, t);
   }
 };
-Np.className = "Ones";
-ae.registerClass(Np);
-var Ab = class extends ns {
+_p.className = "Ones";
+ae.registerClass(_p);
+var Tb = class extends ns {
   constructor(e) {
     if (super(), typeof e != "object")
       throw new U(`Expected argument of type ConstantConfig but got ${e}`);
@@ -10784,9 +10158,9 @@ var Ab = class extends ns {
     return { value: this.value };
   }
 };
-Ab.className = "Constant";
-ae.registerClass(Ab);
-var Eb = class extends ns {
+Tb.className = "Constant";
+ae.registerClass(Tb);
+var $b = class extends ns {
   constructor(e) {
     super(), this.DEFAULT_MINVAL = -0.05, this.DEFAULT_MAXVAL = 0.05, this.minval = e.minval || this.DEFAULT_MINVAL, this.maxval = e.maxval || this.DEFAULT_MAXVAL, this.seed = e.seed;
   }
@@ -10797,39 +10171,39 @@ var Eb = class extends ns {
     return { minval: this.minval, maxval: this.maxval, seed: this.seed };
   }
 };
-Eb.className = "RandomUniform";
-ae.registerClass(Eb);
-var Rb = class extends ns {
+$b.className = "RandomUniform";
+ae.registerClass($b);
+var _b = class extends ns {
   constructor(e) {
     super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
   }
   apply(e, t) {
     if (t = t || "float32", t !== "float32" && t !== "int32")
       throw new Fe(`randomNormal does not support dType ${t}.`);
-    return Cp(e, this.mean, this.stddev, t, this.seed);
+    return $p(e, this.mean, this.stddev, t, this.seed);
   }
   getConfig() {
     return { mean: this.mean, stddev: this.stddev, seed: this.seed };
   }
 };
-Rb.className = "RandomNormal";
-ae.registerClass(Rb);
-var Db = class extends ns {
+_b.className = "RandomNormal";
+ae.registerClass(_b);
+var Ab = class extends ns {
   constructor(e) {
     super(), this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
   }
   apply(e, t) {
     if (t = t || "float32", t !== "float32" && t !== "int32")
       throw new Fe(`truncatedNormal does not support dType ${t}.`);
-    return db(e, this.mean, this.stddev, t, this.seed);
+    return hb(e, this.mean, this.stddev, t, this.seed);
   }
   getConfig() {
     return { mean: this.mean, stddev: this.stddev, seed: this.seed };
   }
 };
-Db.className = "TruncatedNormal";
-ae.registerClass(Db);
-var Fb = class extends ns {
+Ab.className = "TruncatedNormal";
+ae.registerClass(Ab);
+var Eb = class extends ns {
   constructor(e) {
     super(), this.gain = e.gain != null ? e.gain : 1;
   }
@@ -10837,29 +10211,29 @@ var Fb = class extends ns {
     return j(() => {
       if (e.length !== 2 || e[0] !== e[1])
         throw new U("Identity matrix initializer can only be used for 2D square matrices.");
-      return V(this.gain, rI(e[0]));
+      return V(this.gain, uI(e[0]));
     });
   }
   getConfig() {
     return { gain: this.gain };
   }
 };
-Fb.className = "Identity";
-ae.registerClass(Fb);
-function pL(e, t = "channelsLast") {
+Eb.className = "Identity";
+ae.registerClass(Eb);
+function dz(e, t = "channelsLast") {
   let n, s;
   if (Ct(t), e.length === 2)
     n = e[0], s = e[1];
   else if ([3, 4, 5].indexOf(e.length) !== -1) {
     if (t === "channelsFirst") {
-      let r = dr(e, 2);
+      let r = pr(e, 2);
       n = e[1] * r, s = e[0] * r;
     } else if (t === "channelsLast") {
-      let r = dr(e, 0, e.length - 2);
+      let r = pr(e, 0, e.length - 2);
       n = e[e.length - 2] * r, s = e[e.length - 1] * r;
     }
   } else {
-    let r = dr(e);
+    let r = pr(e);
     n = Math.sqrt(r), s = Math.sqrt(r);
   }
   return [n, s];
@@ -10868,15 +10242,15 @@ var xn = class extends ns {
   constructor(e) {
     if (super(), e.scale < 0)
       throw new U(`scale must be a positive float. Got: ${e.scale}`);
-    this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, cL(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, dL(this.distribution), this.seed = e.seed;
+    this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, lz(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, cz(this.distribution), this.seed = e.seed;
   }
   apply(e, t) {
-    let n = pL(e), s = n[0], r = n[1], a = this.scale;
+    let n = dz(e), s = n[0], r = n[1], a = this.scale;
     if (this.mode === "fanIn" ? a /= Math.max(1, s) : this.mode === "fanOut" ? a /= Math.max(1, r) : a /= Math.max(1, (s + r) / 2), this.distribution === "normal") {
       let i = Math.sqrt(a);
       if (t = t || "float32", t !== "float32" && t !== "int32")
         throw new Fe(`${this.getClassName()} does not support dType ${t}.`);
-      return db(e, 0, i, t, this.seed);
+      return hb(e, 0, i, t, this.seed);
     } else {
       let i = Math.sqrt(3 * a);
       return Ml(e, -i, i, t);
@@ -10888,7 +10262,7 @@ var xn = class extends ns {
 };
 xn.className = "VarianceScaling";
 ae.registerClass(xn);
-var Tp = class extends xn {
+var Ap = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanAvg", distribution: "uniform", seed: e == null ? null : e.seed });
   }
@@ -10896,9 +10270,9 @@ var Tp = class extends xn {
     return xn.className;
   }
 };
-Tp.className = "GlorotUniform";
-ae.registerClass(Tp);
-var $p = class extends xn {
+Ap.className = "GlorotUniform";
+ae.registerClass(Ap);
+var Ep = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanAvg", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10906,9 +10280,9 @@ var $p = class extends xn {
     return xn.className;
   }
 };
-$p.className = "GlorotNormal";
-ae.registerClass($p);
-var _p = class extends xn {
+Ep.className = "GlorotNormal";
+ae.registerClass(Ep);
+var Rp = class extends xn {
   constructor(e) {
     super({ scale: 2, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10916,9 +10290,9 @@ var _p = class extends xn {
     return xn.className;
   }
 };
-_p.className = "HeNormal";
-ae.registerClass(_p);
-var Ap = class extends xn {
+Rp.className = "HeNormal";
+ae.registerClass(Rp);
+var Dp = class extends xn {
   constructor(e) {
     super({ scale: 2, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
   }
@@ -10926,9 +10300,9 @@ var Ap = class extends xn {
     return xn.className;
   }
 };
-Ap.className = "HeUniform";
-ae.registerClass(Ap);
-var Ep = class extends xn {
+Dp.className = "HeUniform";
+ae.registerClass(Dp);
+var Fp = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10936,9 +10310,9 @@ var Ep = class extends xn {
     return xn.className;
   }
 };
-Ep.className = "LeCunNormal";
-ae.registerClass(Ep);
-var Rp = class extends xn {
+Fp.className = "LeCunNormal";
+ae.registerClass(Fp);
+var Op = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
   }
@@ -10946,9 +10320,9 @@ var Rp = class extends xn {
     return xn.className;
   }
 };
-Rp.className = "LeCunNormal";
-ae.registerClass(Rp);
-var Ob = class extends ns {
+Op.className = "LeCunNormal";
+ae.registerClass(Op);
+var Rb = class extends ns {
   constructor(e) {
     if (super(), this.DEFAULT_GAIN = 1, this.gain = e.gain == null ? this.DEFAULT_GAIN : e.gain, this.seed = e.seed, this.seed != null)
       throw new Fe("Random seed is not implemented for Orthogonal Initializer yet.");
@@ -10958,101 +10332,46 @@ var Ob = class extends ns {
       if (e.length < 2)
         throw new Fe("Shape must be at least 2D.");
       e[0] * e[1] > 2e3 && console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0] * e[1]}) elements: Slowness may result.`);
-      let n = e[0] > e[1] ? [e[1], e[0]] : e, s = Cp(n, 0, 1, "float32"), r = VO.gramSchmidt(s);
-      return e[0] > e[1] && (r = qe(r)), V(this.gain, r);
+      let n = e[0] > e[1] ? [e[1], e[0]] : e, s = $p(n, 0, 1, "float32"), r = qO.gramSchmidt(s);
+      return e[0] > e[1] && (r = Ge(r)), V(this.gain, r);
     });
   }
   getConfig() {
     return { gain: this.gain, seed: this.seed };
   }
 };
-Ob.className = "Orthogonal";
-ae.registerClass(Ob);
-var _x = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
-function Ax(e, t = {}) {
+Rb.className = "Orthogonal";
+ae.registerClass(Rb);
+var Ix = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
+function Sx(e, t = {}) {
   return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "initializer");
 }
 function yt(e) {
-  return wb(e);
+  return Ib(e);
 }
 function ht(e) {
   if (typeof e == "string") {
-    let t = e in _x ? _x[e] : e;
+    let t = e in Ix ? Ix[e] : e;
     if (t === "GlorotNormal")
-      return new $p();
-    if (t === "GlorotUniform")
-      return new Tp();
-    if (t === "HeNormal")
-      return new _p();
-    if (t === "HeUniform")
-      return new Ap();
-    if (t === "LeCunNormal")
       return new Ep();
-    if (t === "LeCunUniform")
+    if (t === "GlorotUniform")
+      return new Ap();
+    if (t === "HeNormal")
       return new Rp();
+    if (t === "HeUniform")
+      return new Dp();
+    if (t === "LeCunNormal")
+      return new Fp();
+    if (t === "LeCunUniform")
+      return new Op();
     {
       let n = {};
-      return n.className = t, n.config = {}, Ax(n);
+      return n.className = t, n.config = {}, Sx(n);
     }
   } else
-    return e instanceof ns ? e : Ax(e);
+    return e instanceof ns ? e : Sx(e);
 }
-function hL() {
-  return new _b();
-}
-function fL() {
-  return new Np();
-}
-function mL(e) {
-  return new Ab(e);
-}
-function gL(e) {
-  return new Eb(e);
-}
-function bL(e) {
-  return new Rb(e);
-}
-function yL(e) {
-  return new Db(e);
-}
-function vL(e) {
-  return new Fb(e);
-}
-function xL(e) {
-  return new xn(e);
-}
-function wL(e) {
-  return new Tp(e);
-}
-function kL(e) {
-  return new $p(e);
-}
-function IL(e) {
-  return new _p(e);
-}
-function SL(e) {
-  return new Ap(e);
-}
-function CL(e) {
-  return new Ep(e);
-}
-function NL(e) {
-  return new Rp(e);
-}
-function TL(e) {
-  return new Ob(e);
-}
-var $L = {};
-Ae($L, { Layer: () => Ge, RNN: () => Ar, RNNCell: () => ql, activation: () => cV, add: () => vV, alphaDropout: () => nW, average: () => xV, averagePooling1d: () => Hy, averagePooling2d: () => qy, averagePooling3d: () => jy, avgPool1d: () => _V, avgPool2d: () => EV, avgPool3d: () => DV, avgPooling1d: () => AV, avgPooling2d: () => RV, avgPooling3d: () => FV, batchNormalization: () => NV, bidirectional: () => KV, concatenate: () => wV, conv1d: () => tV, conv2d: () => nV, conv2dTranspose: () => sV, conv3d: () => rV, conv3dTranspose: () => aV, convLstm2d: () => GV, convLstm2dCell: () => HV, cropping2D: () => oV, dense: () => dV, depthwiseConv2d: () => lV, dot: () => CV, dropout: () => pV, elu: () => XB, embedding: () => yV, flatten: () => fV, gaussianDropout: () => tW, gaussianNoise: () => eW, globalAveragePooling1d: () => OV, globalAveragePooling2d: () => PV, globalMaxPool1d: () => YV, globalMaxPool2d: () => QV, globalMaxPooling1d: () => GS, globalMaxPooling2d: () => HS, gru: () => MV, gruCell: () => LV, input: () => OB, inputLayer: () => KB, layerNormalization: () => TV, leakyReLU: () => QB, lstm: () => BV, lstmCell: () => VV, masking: () => sW, maxPool1d: () => ZV, maxPool2d: () => JV, maxPooling1d: () => qS, maxPooling2d: () => jS, maxPooling3d: () => zV, maximum: () => kV, minimum: () => IV, multiply: () => SV, permute: () => bV, prelu: () => ZB, reLU: () => YB, repeatVector: () => mV, reshape: () => gV, rnn: () => qV, separableConv2d: () => iV, simpleRNN: () => WV, simpleRNNCell: () => UV, softmax: () => JB, spatialDropout1d: () => hV, stackedRNNCells: () => jV, thresholdedReLU: () => eV, timeDistributed: () => XV, upSampling2d: () => uV, zeroPadding2d: () => $V });
-var _L = 0;
-function YI() {
-  return _L++;
-}
-var Hc = {};
-function Dp(e = "") {
-  return e in Hc || (Hc[e] = 0), Hc[e] += 1, e + Hc[e].toString();
-}
-function ym(e) {
+function xm(e) {
   return Array.isArray(e) && Array.isArray(e[0]);
 }
 function Sd(e) {
@@ -11082,16 +10401,16 @@ function Cd(e) {
     n.shape.length === 0 ? t += 1 : t += n.shape.reduce((s, r) => s * r);
   return t;
 }
-var Ex = "Variable";
-var AL = class {
-  constructor(e, t = "float32", n = Ex, s = true, r = null) {
-    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = YI(), n = n == null ? Ex : n, this.originalName = HI(n), this.name = qI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = U3(e, this.trainable_, this.name, this.dtype);
+var Cx = "Variable";
+var pz = class {
+  constructor(e, t = "float32", n = Cx, s = true, r = null) {
+    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = jI(), n = n == null ? Cx : n, this.originalName = XI(n), this.name = YI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = K3(e, this.trainable_, this.name, this.dtype);
   }
   read() {
     return this.assertNotDisposed(), this.val;
   }
   write(e) {
-    return this.assertNotDisposed(), EL(this.val, e), this.val.id !== e.id && (this.val.assign(e), this.constraint != null && this.val.assign(this.constraint.apply(this.val))), this;
+    return this.assertNotDisposed(), hz(this.val, e), this.val.id !== e.id && (this.val.assign(e), this.constraint != null && this.val.assign(this.constraint.apply(this.val))), this;
   }
   dispose() {
     this.assertNotDisposed(), this.val.dispose();
@@ -11107,14 +10426,14 @@ var AL = class {
     this.trainable_ = e, this.val.trainable = e;
   }
 };
-function EL(e, t) {
+function hz(e, t) {
   if (e.shape.toString() !== t.shape.toString())
     throw new Error("Shape mismatch: " + JSON.stringify(e.shape) + " vs. " + JSON.stringify(t.shape));
 }
-function vm(e) {
+function wm(e) {
   return e.map((t) => t.read());
 }
-function Pb(e) {
+function Db(e) {
   e.forEach((t) => {
     t[0].write(t[1]);
   });
@@ -11126,13 +10445,13 @@ var Dt = class {
 };
 var $s = class {
   constructor(e, t, n, s, r, a, i) {
-    this.dtype = e, this.shape = t, this.sourceLayer = n, this.inputs = s, this.callArgs = r, this.outputTensorIndex = i, this.id = YI(), a != null && (this.originalName = HI(a), this.name = qI(this.originalName)), this.rank = t.length;
+    this.dtype = e, this.shape = t, this.sourceLayer = n, this.inputs = s, this.callArgs = r, this.outputTensorIndex = i, this.id = jI(), a != null && (this.originalName = XI(a), this.name = YI(this.originalName)), this.rank = t.length;
   }
 };
-var RL = 0;
-var Fp = class {
+var fz = 0;
+var Pp = class {
   constructor(e, t) {
-    this.callArgs = t, this.id = RL++, this.outboundLayer = e.outboundLayer, this.inboundLayers = e.inboundLayers, this.nodeIndices = e.nodeIndices, this.tensorIndices = e.tensorIndices, this.inputTensors = e.inputTensors, this.outputTensors = e.outputTensors, this.inputMasks = e.inputMasks, this.outputMasks = e.outputMasks, this.inputShapes = e.inputShapes, this.outputShapes = e.outputShapes;
+    this.callArgs = t, this.id = fz++, this.outboundLayer = e.outboundLayer, this.inboundLayers = e.inboundLayers, this.nodeIndices = e.nodeIndices, this.tensorIndices = e.tensorIndices, this.inputTensors = e.inputTensors, this.outputTensors = e.outputTensors, this.inputMasks = e.inputMasks, this.outputMasks = e.outputMasks, this.inputShapes = e.inputShapes, this.outputShapes = e.outputShapes;
     for (let n of e.inboundLayers)
       n != null && n.outboundNodes.push(this);
     e.outboundLayer.inboundNodes.push(this);
@@ -11144,14 +10463,14 @@ var Fp = class {
     return { outboundLayer: this.outboundLayer ? this.outboundLayer.name : null, inboundLayers: e, nodeIndices: this.nodeIndices, tensorIndices: this.tensorIndices };
   }
 };
-var DL = 0;
-var Ge = class extends ae.Serializable {
+var mz = 0;
+var He = class extends ae.Serializable {
   constructor(e = {}) {
-    super(), this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = DL++, this.activityRegularizer = null, this.inputSpec = null, this.supportsMasking = false, this._trainableWeights = [], this._nonTrainableWeights = [], this._losses = [], this._updates = [], this._built = false, this.inboundNodes = [], this.outboundNodes = [];
+    super(), this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = mz++, this.activityRegularizer = null, this.inputSpec = null, this.supportsMasking = false, this._trainableWeights = [], this._nonTrainableWeights = [], this._losses = [], this._updates = [], this._built = false, this.inboundNodes = [], this.outboundNodes = [];
     let t = e.name;
     if (!t) {
       let n = this.getClassName();
-      t = Ws(n) + "_" + Dp(n);
+      t = Ws(n) + "_" + Np(n);
     }
     if (this.name = t, this.trainable_ = e.trainable == null ? true : e.trainable, e.inputShape != null || e.batchInputShape != null) {
       let n;
@@ -11303,7 +10622,7 @@ var Ge = class extends ae.Serializable {
       }
     if (s === r)
       throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");
-    return ea(this.name, () => {
+    return ta(this.name, () => {
       if (!this.built) {
         this.assertInputCompatibility(e);
         let a = [];
@@ -11319,7 +10638,7 @@ var Ge = class extends ae.Serializable {
           throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
         return a;
       } else {
-        let a = FL(e), i = this.computeOutputShape(a), o, u = OL(e);
+        let a = gz(e), i = this.computeOutputShape(a), o, u = bz(e);
         if (this.warnOnIncompatibleInputShape(Array.isArray(e) ? a[0] : a), i != null && i.length > 0 && Array.isArray(i[0]) ? o = i.map((l, c) => new $s(u, l, this, dt(e), t, this.name, c)) : o = new $s(u, i, this, dt(e), t, this.name), this.addInboundNode(e, o, null, null, a, i, t), this._refCount++, this.activityRegularizer != null)
           throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
         return o;
@@ -11360,7 +10679,7 @@ var Ge = class extends ae.Serializable {
     this.built = true;
   }
   getWeights(e = false) {
-    return vm(e ? this.trainableWeights : this.weights);
+    return wm(e ? this.trainableWeights : this.weights);
   }
   setWeights(e) {
     j(() => {
@@ -11369,21 +10688,21 @@ var Ge = class extends ae.Serializable {
         throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);
       if (t.length === 0)
         return;
-      let n = [], s = vm(t);
+      let n = [], s = wm(t);
       for (let r = 0; r < s.length; ++r) {
         let a = s[r], i = t[r], o = e[r];
         if (!w.arraysEqual(a.shape, o.shape))
           throw new U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);
         n.push([i, o]);
       }
-      Pb(n);
+      Db(n);
     });
   }
   addWeight(e, t, n, s, r, a, i, o) {
     if (this._addedWeightNames.indexOf(e) !== -1)
       throw new U(`Duplicate weight name ${e} for layer ${this.name}`);
     this._addedWeightNames.push(e), n == null && (n = "float32"), this.fastWeightInitDuringBuild && (s = o != null ? o() : ht("zeros"));
-    let u = s.apply(t, n), l = new AL(u, n, e, a, i);
+    let u = s.apply(t, n), l = new pz(u, n, e, a, i);
     return u.dispose(), r != null && this.addLoss(() => r.apply(l.read())), a == null && (a = true), a ? this._trainableWeights.push(l) : this._nonTrainableWeights.push(l), l;
   }
   setFastWeightInitDuringBuild(e) {
@@ -11415,7 +10734,7 @@ var Ge = class extends ae.Serializable {
     let u = [], l = [], c = [];
     for (let p of o)
       u.push(p.sourceLayer), l.push(p.nodeIndex), c.push(p.tensorIndex);
-    new Fp({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
+    new Pp({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
     for (let p = 0; p < t.length; p++)
       t[p].sourceLayer = this, t[p].nodeIndex = this.inboundNodes.length - 1, t[p].tensorIndex = p;
   }
@@ -11440,17 +10759,17 @@ var Ge = class extends ae.Serializable {
     return --this._refCount === 0 && (e = this.disposeWeights()), { refCountAfterDispose: this._refCount, numDisposedVariables: e };
   }
 };
-function FL(e) {
+function gz(e) {
   e = dt(e);
   let t = [];
   for (let n of e)
     t.push(n.shape);
   return bn(t);
 }
-function OL(e) {
+function bz(e) {
   return "float32";
 }
-function QI(e, t, n) {
+function eS(e, t, n) {
   if ((t == null || n != null && n > 0) && (t = e.sourceLayer, n = e.nodeIndex), t.inboundNodes.length === 0)
     return [e];
   {
@@ -11460,7 +10779,7 @@ function QI(e, t, n) {
     {
       let r = [];
       for (let a = 0; a < s.inboundLayers.length; a++) {
-        let i = s.inputTensors[a], o = s.inboundLayers[a], u = s.nodeIndices[a], l = QI(i, o, u);
+        let i = s.inputTensors[a], o = s.inboundLayers[a], u = s.nodeIndices[a], l = eS(i, o, u);
         for (let c of l)
           r.indexOf(c) === -1 && r.push(c);
       }
@@ -11468,9 +10787,9 @@ function QI(e, t, n) {
     }
   }
 }
-var Qo = class extends Ge {
+var Zo = class extends He {
   constructor(e) {
-    if (super({ dtype: e.dtype, name: e.name != null ? e.name : Dp("input").toString() }), e.batchSize == null && (e.batchSize = null), e.sparse == null && (e.sparse = false), this.trainable = false, this.built = true, this.sparse = e.sparse, e.inputShape != null && e.batchInputShape != null)
+    if (super({ dtype: e.dtype, name: e.name != null ? e.name : Np("input").toString() }), e.batchSize == null && (e.batchSize = null), e.sparse == null && (e.sparse = false), this.trainable = false, this.built = true, this.sparse = e.sparse, e.inputShape != null && e.batchInputShape != null)
       throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
     let t = e.batchInputShape;
     if (t == null) {
@@ -11482,7 +10801,7 @@ var Qo = class extends Ge {
     let n = e.dtype || "float32";
     this.batchInputShape = t, this.dtype = n, this.inputSpec = [{ shape: t }];
     let s = new $s(this.dtype, this.batchInputShape, this, [], {}, this.name);
-    s.nodeIndex = 0, s.tensorIndex = 0, new Fp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: [s], outputTensors: [s], inputMasks: [null], outputMasks: [null], inputShapes: [t], outputShapes: [t] });
+    s.nodeIndex = 0, s.tensorIndex = 0, new Pp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: [s], outputTensors: [s], inputMasks: [null], outputMasks: [null], inputShapes: [t], outputShapes: [t] });
   }
   apply(e, t) {
     throw new U(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);
@@ -11494,9 +10813,9 @@ var Qo = class extends Ge {
     return { batchInputShape: this.batchInputShape, dtype: this.dtype, sparse: this.sparse, name: this.name };
   }
 };
-Qo.className = "InputLayer";
-ae.registerClass(Qo);
-function ZI(e) {
+Zo.className = "InputLayer";
+ae.registerClass(Zo);
+function tS(e) {
   if (e.batchShape == null && e.shape == null)
     throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
   if (e.batchShape != null && e.shape != null)
@@ -11504,9 +10823,915 @@ function ZI(e) {
   let t = e.batchShape;
   e.shape != null && t == null && (t = [null].concat(e.shape));
   let n = e.dtype;
-  return n == null && (n = "float32"), new Qo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
+  return n == null && (n = "float32"), new Zo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
 }
-async function rr(e) {
+function yz(e, t) {
+  if (e.dtype == null || e.dtype === t.dtype)
+    return t;
+  try {
+    return ce(t, e.dtype);
+  } catch (n) {
+    throw new U(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`);
+  }
+}
+var Zr = class {
+  constructor(e) {
+    if (this.id2Value = {}, this.id2Mask = {}, this.name2Id = {}, e instanceof Zr)
+      for (let t in e.id2Value)
+        this.id2Value[t] = e.id2Value[t], t in e.id2Mask && (this.id2Mask[t] = e.id2Mask[t]);
+    else {
+      if (e == null)
+        return;
+      for (let t of e)
+        this.add(t.key, t.value);
+    }
+  }
+  add(e, t, n) {
+    if (this.id2Value[e.id] == null)
+      this.id2Value[e.id] = yz(e, t), this.name2Id[e.name] = e.id, n != null && (this.id2Mask[e.id] = n);
+    else
+      throw new U(`Duplicate key: name=${e.name}, id=${e.id}`);
+    return this;
+  }
+  addFeed(e) {
+    this.add(e.key, e.value);
+  }
+  hasKey(e) {
+    return this.id2Value[e.id] != null;
+  }
+  names() {
+    return Object.keys(this.name2Id);
+  }
+  getValue(e) {
+    if (e instanceof $s) {
+      if (this.id2Value[e.id] == null)
+        throw new U(`Nonexistent key: ${e.name}`);
+      return this.id2Value[e.id];
+    } else {
+      let t = this.name2Id[e];
+      if (t == null)
+        throw new U(`Feed dict has no SymbolicTensor name: ${e}`);
+      return this.id2Value[t];
+    }
+  }
+  getMask(e) {
+    if (e instanceof $s) {
+      if (this.id2Value[e.id] == null)
+        throw new U(`Nonexistent key: ${e.name}`);
+      return this.id2Mask[e.id];
+    } else {
+      let t = this.name2Id[e];
+      if (t == null)
+        throw new U(`Feed dict has no SymbolicTensor name: ${e}`);
+      return this.id2Mask[t];
+    }
+  }
+  disposeMasks() {
+    this.id2Mask != null && Re(this.id2Mask);
+  }
+};
+var Nd = new GI();
+var Td = new GI();
+function vz(e) {
+  Nd != null && Nd.setMaxEntries(e), Td != null && Td.setMaxEntries(e);
+}
+function Eu(e, t, n, s) {
+  let r = n == null ? false : n.training, a = Array.isArray(e), i = a ? e : [e], o = i.map((f) => f.name), u = [], l = t.names();
+  for (let f of o)
+    l.indexOf(f) !== -1 ? u.push(t.getValue(f)) : u.push(null);
+  s != null && (s.maxNumTensors = -1 / 0, s.minNumTensors = 1 / 0);
+  let c = o.join(",") + "|" + t.names().join(","), p = Nd.get(c), d;
+  if (p == null) {
+    let f = xz(i, t);
+    p = f.sorted, d = f.recipientCounts, Nd.put(c, p), Td.put(c, d);
+  }
+  d = {}, r || Object.assign(d, Td.get(c));
+  let h = new Zr(t);
+  for (let f = 0; f < p.length; ++f) {
+    if (s != null) {
+      let A = pm().numTensors;
+      A > s.maxNumTensors && (s.maxNumTensors = A), A < s.minNumTensors && (s.minNumTensors = A);
+    }
+    let m = p[f], g = m.sourceLayer;
+    if (g instanceof Zo)
+      continue;
+    let b = [], y = [], v = [], x = false;
+    for (let A of m.inputs) {
+      let P = h.getValue(A), R = h.getMask(A);
+      b.push(P), y.push(R), R != null && (x = true), r || (d[A.name]--, d[A.name] === 0 && !t.hasKey(A) && o.indexOf(A.name) === -1 && !P.isDisposed && A.sourceLayer.stateful !== true && v.push(P));
+    }
+    x && (n = n || {}, n.mask = y[0]);
+    let k = dt(g.apply(b, n)), T = null;
+    g.supportsMasking && (T = g.computeMask(b, y));
+    let N = kz(m), E = Array.isArray(N) ? N : [N];
+    for (let A = 0; A < E.length; ++A) {
+      h.hasKey(E[A]) || h.add(E[A], k[A], Array.isArray(T) ? T[0] : T);
+      let P = o.indexOf(E[A].name);
+      P !== -1 && (u[P] = k[A]);
+    }
+    r || Re(v);
+  }
+  return h.disposeMasks(), a ? u : u[0];
+}
+function xz(e, t) {
+  w.assert(e != null && e.length > 0, () => "Expected at least one fetch, got none");
+  let n = [], s = {};
+  if (e.length === 1) {
+    let r = Nx(e[0], t);
+    n = r.sorted, s = r.recipientMap;
+  } else {
+    let r = /* @__PURE__ */ new Set();
+    for (let a of e) {
+      let { sorted: i, recipientMap: o } = Nx(a, t);
+      for (let u of i)
+        r.has(u.name) || (n.push(u), r.add(u.name));
+      for (let u in o)
+        s[u] == null && (s[u] = /* @__PURE__ */ new Set()), o[u].forEach((l) => s[u].add(l));
+    }
+  }
+  return { sorted: n, recipientCounts: wz(s) };
+}
+function wz(e) {
+  let t = {};
+  for (let n in e)
+    t[n] = e[n].size;
+  return t;
+}
+function Nx(e, t) {
+  let n = /* @__PURE__ */ new Set(), s = [], r = {};
+  for (let o of t.names())
+    n.add(o);
+  let a = [], i = [];
+  for (a.push(e); a.length > 0; ) {
+    let o = a[a.length - 1];
+    if (n.has(o.name)) {
+      a.pop();
+      continue;
+    }
+    let u = i[i.length - 1] === a.length - 1;
+    if (o.inputs.length === 0 || u)
+      a.pop(), s.push(o), n.add(o.name), u && i.pop();
+    else {
+      i.push(a.length - 1);
+      for (let l of o.inputs)
+        r[l.name] == null && (r[l.name] = /* @__PURE__ */ new Set()), r[l.name].add(o.name), !n.has(l.name) && a.push(l);
+    }
+  }
+  return { sorted: s, recipientMap: r };
+}
+function kz(e) {
+  let t;
+  if (e.sourceLayer.inboundNodes.length === 1)
+    t = e.sourceLayer.output;
+  else {
+    let n = null;
+    for (let s = 0; s < e.sourceLayer.inboundNodes.length; ++s)
+      for (let r of e.sourceLayer.inboundNodes[s].outputTensors)
+        if (r.id === e.id) {
+          n = s;
+          break;
+        }
+    t = e.sourceLayer.getOutputAt(n);
+  }
+  return t;
+}
+var Iz = X();
+Iz.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, vz);
+var nS = { kernelName: uo, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(e, xp(ce(n, "float32"), -1)) };
+} };
+var Sz = { kernelName: rl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => {
+    let s = ct(ce(n, "float32")), r = dn(ge(we(1), s));
+    return kt(xe(e, r));
+  } };
+} };
+var Cz = { kernelName: al, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => {
+    let s = dn(ge(ct(ce(n, "float32")), 1));
+    return xe(e, s);
+  } };
+} };
+var Nz = { kernelName: Sr, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = ot(n.shape, s.shape);
+  return { a: () => {
+    let o = e, u = _t(n.shape, r);
+    return u.length > 0 && (o = ve(o, u)), G(o, n.shape);
+  }, b: () => {
+    let o = e, u = _t(s.shape, r);
+    return u.length > 0 && (o = ve(o, u)), G(o, s.shape);
+  } };
+} };
+var Tz = { kernelName: Ia, saveAllInputs: true, gradFunc: (e, t) => {
+  let n = {};
+  return t.forEach((s, r) => {
+    n[r] = () => e.clone();
+  }), n;
+} };
+var $z = { kernelName: Sa, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => je(n) };
+} };
+var _z = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => je(n) };
+} };
+var Az = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, dn(ge(we(1), ct(ce(n, "float32"))))) };
+} };
+var Ez = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => {
+    let s = dn(ie(we(1), ct(ce(n, "float32"))));
+    return xe(e, s);
+  } };
+} };
+var Rz = { kernelName: hl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = ot(n.shape, s.shape);
+  return { a: () => {
+    let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = _t(n.shape, r);
+    return l.length > 0 && (u = ve(u, l)), G(u, n.shape);
+  }, b: () => {
+    let o = ie(ct(n), ct(s)), u = kt(V(e, xe(n, o))), l = _t(s.shape, r);
+    return l.length > 0 && (u = ve(u, l)), G(u, s.shape);
+  } };
+} };
+var Dz = { kernelName: dl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, ie(ct(ce(n, "float32")), 1)) };
+} };
+var Fz = { kernelName: pl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, ge(we(1), ct(ce(n, "float32")))) };
+} };
+function Oz(e, t, n, s, r, a) {
+  let i = _(e, "dy", "avgPool3dGrad"), o = _(t, "input", "avgPool3dGrad"), u = i, l = o, c = false;
+  o.rank === 4 && (c = true, u = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]]), l = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), O(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), hn("avgPool3dGrad", r, a);
+  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = M.runKernel(lg, p, d);
+  return c ? G(h, [h.shape[1], h.shape[2], h.shape[3], h.shape[4]]) : h;
+}
+var Pz = L({ avgPool3dGrad_: Oz });
+var zz = { kernelName: Kd, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { filterSize: r, strides: a, pad: i, dimRoundingMode: o } = n;
+  return { x: () => Pz(e, s, r, a, i, o) };
+} };
+function Mz(e, t, n, s, r) {
+  let a = _(e, "dy", "avgPoolGrad"), i = _(t, "input", "avgPoolGrad");
+  O(i.rank === a.rank, () => `Rank of input (${i.rank}) does not match rank of dy (${a.rank})`);
+  let o = i, u = a, l = false;
+  i.rank === 3 && (l = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2]]), u = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), O(o.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);
+  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = M.runKernel(ug, c, p);
+  return l ? G(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
+}
+var Lz = L({ avgPoolGrad_: Mz });
+var Bz = { kernelName: Ca, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { filterSize: r, strides: a, pad: i } = n;
+  return { x: () => Lz(e, s, r, a, i) };
+} };
+var Vz = { kernelName: Na, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { transposeA: a, transposeB: i } = n;
+  return !a && !i ? { a: () => We(e, r, false, true), b: () => We(s, e, true, false) } : !a && i ? { a: () => We(e, r, false, false), b: () => We(e, s, true, false) } : a && !i ? { a: () => We(r, e, false, true), b: () => We(s, e, false, false) } : { a: () => We(r, e, true, true), b: () => We(e, s, true, true) };
+} };
+var Wz = { kernelName: lo, gradFunc: (e, t, n) => {
+  let { blockShape: s, crops: r } = n;
+  return { x: () => rb(e, s, r) };
+} };
+var Uz = { kernelName: P$, gradFunc: (e, t, n) => {
+  let s = n, r = s.inputShape, a = s.shape, i = Array.from(a);
+  for (let u = r.length - 1; u >= 0; u--)
+    if (r[u] === a[u])
+      i[u] = 1;
+    else if (r[u] !== 1)
+      throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${a}].`);
+  let o = [];
+  for (let u = 0; u < i.length; u++)
+    i[u] > 1 && o.push(u);
+  return { x: () => ve(e, o, true) };
+} };
+var Gz = { kernelName: Ta, gradFunc: (e) => ({ x: () => e.clone() }) };
+var Hz = { kernelName: $a, gradFunc: (e) => ({ x: () => je(e) }) };
+var qz = { kernelName: Cr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
+  return { x: () => vn(Ds(Xo(s, r), Yo(s, a)), e, je(e)) };
+} };
+var jz = { kernelName: Yd, inputsToSave: ["x"], gradFunc: nS.gradFunc };
+var Kz = { kernelName: co, saveAllInputs: true, gradFunc: (e, t, n) => {
+  let s = t.map((u) => u.shape), { axis: r } = n, a = ts(r, t[0].shape)[0], i = s.map((u) => u[a]);
+  return Bn(e, i, a).map((u) => () => u);
+} };
+var Xz = { kernelName: _a, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { dilations: a, strides: i, pad: o, dataFormat: u } = n;
+  return O(mr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => Kg(s.shape, e, r, i, o, u), filter: () => fb(s, e, r.shape, i, o, u) };
+} };
+var Yz = { kernelName: Aa, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { strides: a, pad: i, dataFormat: o, dimRoundingMode: u } = n;
+  return { dy: () => da(e, r, a, i, o, 1, u), filter: () => fb(e, s, r.shape, a, i, o, u) };
+} };
+function Qz(e, t, n, s, r) {
+  let a = e;
+  e.rank === 4 && (a = G(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]));
+  let i = t;
+  i.rank === 4 && (i = G(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]])), O(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), O(i.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`), O(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), O(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), O(i.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);
+  let o = { x: a, dy: i }, u = { strides: s, pad: r, filterShape: n };
+  return M.runKernel(hg, o, u);
+}
+var Zz = L({ conv3DBackpropFilter_: Qz });
+var Jz = { kernelName: Qd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+  let { dilations: s, strides: r, pad: a } = n;
+  O(mr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
+  let [i, o] = t;
+  return { x: () => aI(i.shape, e, o, r, a), filter: () => Zz(i, e, o.shape, r, a) };
+} };
+var eM = { kernelName: Ea, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(kt(wI(ce(n, "float32"))), e) };
+} };
+var tM = { kernelName: Ra, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(kI(ce(n, "float32")), e) };
+} };
+var nM = { kernelName: Da, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
+  return { x: () => {
+    let o = hI([r], s.rank), u = oI(e, r, a, !i);
+    return o != null && (u = Ge(u, o)), u;
+  } };
+} };
+var sM = { kernelName: Fa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+  let { dilations: s, strides: r, pad: a, dimRoundingMode: i } = n, o = s == null ? [1, 1] : s;
+  O(mr(o), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);
+  let [u, l] = t;
+  return O(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), O(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), O(Ps(r, o), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${o}'.`), hn("depthwiseConv2d", a, i), { x: () => DI(u.shape, e, l, r, a, o, i), filter: () => RI(u, e, l.shape, r, a, o, i) };
+} };
+var rM = { kernelName: Zd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+  let [s, r] = t, a = { x: s, filter: r, dy: e }, i = { x: s, filter: r, dy: e };
+  return { x: () => M.runKernel(Qf, a, n), filter: () => M.runKernel(Zf, i, n) };
+} };
+var aM = { kernelName: Pa, outputsToSave: [true], gradFunc: (e, t) => {
+  let [n] = t, s = { dy: e, y: n };
+  return { x: () => M.runKernel(vg, s) };
+} };
+var iM = { kernelName: fl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t, s = V(Yn(kt(ct(n))), 2 / Math.sqrt(Math.PI));
+  return { x: () => V(e, s) };
+} };
+var oM = { kernelName: za, outputsToSave: [true], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(e, n) };
+} };
+var uM = { kernelName: go, inputsToSave: ["input"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { input: () => G(e, n.shape) };
+} };
+var lM = { kernelName: bo, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(e, Yn(n)) };
+} };
+var cM = { kernelName: Ma, gradFunc: (e) => ({ x: () => je(e) }) };
+var dM = { kernelName: La, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = ot(n.shape, s.shape);
+  return { a: () => {
+    let o = xe(e, ce(s, "float32")), u = _t(n.shape, r);
+    return u.length > 0 ? G(ve(o, u), n.shape) : o;
+  }, b: () => {
+    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
+    u.length > 0 && (o = G(ve(o, u), s.shape));
+    let l = ct(s);
+    return kt(xe(o, ce(l, "float32")));
+  } };
+} };
+var pM = { kernelName: Ba, inputsToSave: ["x", "mean", "variance", "scale"], gradFunc: (e, t, n) => {
+  let { varianceEpsilon: s } = n, [r, a, i, o] = t, u = o == null ? we(1) : o, l = _t(a.shape, r.shape), c = [];
+  if (a.rank === 1) {
+    for (let x = 0; x < r.shape.length - 1; ++x)
+      c.push(r.shape[x]);
+    c.push(1);
+  }
+  let p = ge(r, a), d = V(e, u), h = vI(ie(i, we(s))), f = V(V(V(h, h), h), we(-0.5));
+  return { x: () => a.rank === 1 ? G(V(V(e, ps(G(h, [1, 1, 1, a.shape[0]]), c)), u), r.shape) : G(V(V(e, h), u), r.shape), mean: () => {
+    let x = V(V(h, we(-1)), d);
+    return a.rank === 1 && (x = ve(x, l)), G(x, a.shape);
+  }, variance: () => {
+    let x = V(V(f, p), d);
+    return a.rank === 1 && (x = ve(x, l)), G(x, a.shape);
+  }, scale: () => {
+    let x = V(p, h), k = V(e, x);
+    return a.rank === 1 && (k = ve(k, l)), G(k, a.shape);
+  }, offset: () => {
+    let x = e;
+    return a.rank === 1 && (x = ve(x, l)), G(x, a.shape);
+  } };
+} };
+var hM = { kernelName: vo, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
+  let [s, r] = t, { axis: a } = n, i = ts(a, s.shape)[0];
+  return { x: () => {
+    let u = s.shape, l = r.size, c = u.slice(0, i), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = Tx(0, p), m = Tx(p + 1, p + 1 + h), g = $x([c, [l], d]), b = G(e, g), y = G(r, [l]), v = $x([[p], f, m]), x = Ge(b, v), k = q3(x, y, s.shape[i]), T = eb(v);
+    return k = Ge(k, T), k;
+  }, indices: () => r };
+} };
+function Tx(e, t) {
+  let n = [];
+  for (let s = e; s < t; ++s)
+    n.push(s);
+  return n;
+}
+function $x(e) {
+  let t = [];
+  for (let n = 0; n < e.length; ++n)
+    for (let s = 0; s < e[n].length; ++s)
+      t.push(e[n][s]);
+  return t;
+}
+var fM = { kernelName: Va, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t;
+  return { a: () => je(n), b: () => je(s) };
+} };
+var mM = { kernelName: Wa, gradFunc: (e) => ({ x: () => ce(e, "float32") }) };
+var gM = { kernelName: gl, gradFunc: (e) => ({ x: () => je(e) }) };
+var bM = { kernelName: bl, gradFunc: (e) => ({ x: () => je(e) }) };
+var yM = { kernelName: yl, gradFunc: (e) => ({ x: () => je(e) }) };
+var vM = { kernelName: Ua, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { alpha: r } = n, a = Un(s, 0);
+  return { x: () => vn(a, e, V(e, r)) };
+} };
+var xM = { kernelName: vl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, ie(n, 1)) };
+} };
+var wM = { kernelName: Ga, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, ce(n, "float32")) };
+} };
+var kM = { kernelName: z$, inputsToSave: [], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s] = t, { axis: r } = n;
+  return { logits: () => {
+    let i = Yn(s);
+    return ge(e, V(ve(e, r, true), i));
+  } };
+} };
+function IM(e, t, n, s = 5, r = 1, a = 1, i = 0.5) {
+  let o = { x: e, y: t, dy: n }, u = { depthRadius: s, bias: r, alpha: a, beta: i };
+  return M.runKernel(Ig, o, u);
+}
+var SM = L({ localResponseNormalizationBackprop_: IM });
+var CM = { kernelName: np, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s, r] = t, { depthRadius: a, bias: i, alpha: o, beta: u } = n;
+  return { x: () => SM(s, r, e, a, i, o, u) };
+} };
+function sS(e, t, n, s) {
+  return t.rank < n.rank && (t = G(t, pa(t.shape, s))), e.rank < n.rank && (e = G(e, pa(e.shape, s))), { x: () => V(e, ce(Xn(n, t), e.dtype)) };
+}
+var _x = { kernelName: Ha, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let s = n, { reductionIndices: r } = s, a = t[0], i = t[1], o = ts(r, a.shape), u = sS(e, i, a, o);
+  return { x: () => u.x() };
+} };
+var NM = { kernelName: qa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t;
+  return { a: () => V(e, ce(Xo(n, s), "float32")), b: () => V(e, ce(lI(n, s), "float32")) };
+} };
+function TM(e, t, n, s, r, a, i) {
+  let o = _(e, "dy", "maxPool3dGrad"), u = _(t, "input", "maxPool3dGrad"), l = _(n, "output", "maxPool3dGrad"), c = o, p = u, d = l, h = false;
+  u.rank === 4 && (h = true, c = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]]), p = G(u, [1, u.shape[0], u.shape[1], u.shape[2], u.shape[3]]), d = G(l, [1, l.shape[0], l.shape[1], l.shape[2], l.shape[3]])), O(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), O(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), O(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), hn("maxPool3dGrad", a, i);
+  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = M.runKernel(Cg, f, m);
+  return h ? G(g, [g.shape[1], g.shape[2], g.shape[3], g.shape[4]]) : g;
+}
+var $M = L({ maxPool3dGrad_: TM });
+var _M = { kernelName: sp, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s, r] = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = n;
+  return { x: () => $M(e, s, r, a, i, o, u) };
+} };
+function AM(e, t, n, s, r, a, i) {
+  let o = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
+  O(u.rank === o.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${o.rank})`), O(o.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`), O(u.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`), hn("maxPoolGrad", a, i);
+  let c = { dy: o, input: u, output: l }, p = { filterSize: s, strides: r, pad: a, dimRoundingMode: i };
+  return M.runKernel(Sg, c, p);
+}
+var EM = L({ maxPoolGrad_: AM });
+var RM = { kernelName: ja, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s, r] = t, { filterSize: a, strides: i, pad: o } = n;
+  return { x: () => EM(e, s, r, a, i, o) };
+} };
+var DM = { kernelName: Ka, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { axis: r } = n, a = ts(r, s.shape), o = pI(s.shape, a)[1], u = pt(o);
+  return { x: () => {
+    let c = s.shape.slice();
+    a.forEach((h) => {
+      c[h] = 1;
+    });
+    let p = G(e, c);
+    return xe(V(p, Mn(s.shape, "float32")), u);
+  } };
+} };
+var FM = { kernelName: Xa, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+  let s = n, { axis: r } = s, [a, i] = t, o = ts(r, a.shape), u = sS(e, i, a, o);
+  return { x: () => u.x() };
+} };
+var OM = { kernelName: Ya, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t;
+  return { a: () => V(e, ce(Yo(n, s), "float32")), b: () => V(e, ce(Un(n, s), "float32")) };
+} };
+var PM = { kernelName: Qa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
+  return { x: () => qe(e, a, s.shape) };
+} };
+var zM = { kernelName: wl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = ot(n.shape, s.shape);
+  return { a: () => {
+    let o = _t(n.shape, r);
+    return o.length > 0 ? G(ve(e, o), n.shape) : e;
+  }, b: () => {
+    let o = V(e, kt(bp(xe(n, s)))), u = _t(s.shape, r);
+    return u.length > 0 ? G(ve(o, u), s.shape) : o;
+  } };
+} };
+var MM = { kernelName: Za, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = ot(n.shape, s.shape);
+  return { a: () => {
+    let o = V(e, ce(s, "float32")), u = _t(n.shape, r);
+    return u.length > 0 ? G(ve(o, u), n.shape) : o;
+  }, b: () => {
+    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
+    return u.length > 0 ? G(ve(o, u), s.shape) : o;
+  } };
+} };
+var LM = { kernelName: Co, gradFunc: (e) => ({ x: () => kt(e) }) };
+var BM = { kernelName: Ao, inputsToSave: ["indices"], gradFunc: (e, t) => {
+  let n = t[0];
+  return { indices: () => $t(n.shape, "float32") };
+} };
+var VM = { kernelName: _o, gradFunc: (e) => ({ x: () => je(e) }) };
+var WM = { kernelName: Eo, saveAllInputs: true, gradFunc: (e, t, n) => {
+  let { axis: s } = n;
+  return Fs(e, s).map((a) => () => a);
+} };
+var Ax = { kernelName: Ja, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
+  return { x: () => qe(e, a, s.shape) };
+} };
+var UM = { kernelName: ei, inputsToSave: ["a", "b"], outputsToSave: [true], gradFunc: (e, t) => {
+  let [n, s, r] = t, a = n, i = s, o = ot(a.shape, i.shape);
+  return { a: () => {
+    let c = ce(i, "float32"), p = V(e, V(c, ha(a, ge(c, we(1))))), d = _t(a.shape, o);
+    return d.length > 0 && (p = ve(p, d)), G(p, a.shape);
+  }, b: () => {
+    let c = Un(a, 0), p = vn(c, Qn(a), je(a)), d = V(e, V(r, p)), h = _t(i.shape, o);
+    return h.length > 0 && (d = ve(d, h)), G(d, i.shape);
+  } };
+} };
+var GM = { kernelName: ti, inputsToSave: ["x", "alpha"], gradFunc: (e, t) => {
+  let [n, s] = t, r = Un(n, 0);
+  return { x: () => vn(r, e, V(e, s)), alpha: () => {
+    let a = vn(r, je(e), V(e, n)), i = _t(s.shape, e.shape);
+    return i.length > 0 && (a = ve(a, i)), G(a, s.shape);
+  } };
+} };
+function HM(e, t, n) {
+  let s = e.shape.slice();
+  s[n] = 1;
+  let r = G(t, s), a = mm(e, n, true, false), i = mm(e, n, true, true), o = V(a, i);
+  return V(r, o);
+}
+function qM(e, t, n) {
+  let s = e.shape.length, r = s - n.length, a = S.getAxesPermutation(n, s), i = e;
+  a != null && (i = Ge(e, a));
+  let o = i.shape.slice(), l = o.splice(s - n.length, n.length).reduce((d, h) => d * h, 1);
+  o.push(l);
+  let c = i.reshape(o), p = HM(c, t, r);
+  if (p = p.reshape(i.shape), a != null) {
+    let d = S.getUndoAxesPermutation(a);
+    p = Ge(p, d);
+  }
+  return p;
+}
+var jM = { kernelName: ni, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { axis: r } = n, a = [];
+  return r == null ? a = s.shape.map((i, o) => o) : typeof r == "number" ? a = [r] : a = r, { x: () => qM(s, e, a) };
+} };
+var KM = { kernelName: Oa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = ot(n.shape, s.shape);
+  return { a: () => {
+    let o = xe(e, ce(s, "float32")), u = _t(n.shape, r);
+    return u.length > 0 ? G(ve(o, u), n.shape) : o;
+  }, b: () => {
+    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
+    u.length > 0 && (o = G(ve(o, u), s.shape));
+    let l = ct(s);
+    return kt(xe(o, ce(l, "float32")));
+  } };
+} };
+var XM = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, kt(ct(n))) };
+} };
+var YM = { kernelName: ai, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t, s = V(Yo(n, 6), xp(n));
+  return { x: () => V(e, ce(s, "float32")) };
+} };
+var QM = { kernelName: si, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(e, ce(xp(n), "float32")) };
+} };
+var ZM = { kernelName: Ro, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => G(e, n.shape) };
+} };
+var JM = { kernelName: ri, inputsToSave: ["images"], gradFunc: (e, t, n) => {
+  let [s] = t, r = { dy: e, images: s };
+  return { images: () => M.runKernel(_g, r, n) };
+} };
+var eL = { kernelName: Cl, inputsToSave: ["images"], gradFunc: (e, t, n) => {
+  let [s] = t, r = { dy: e, images: s };
+  return { images: () => M.runKernel($g, r, n) };
+} };
+var tL = { kernelName: Do, gradFunc: (e, t, n) => {
+  let { dims: s } = n, r = ts(s, e.shape);
+  return { x: () => Jn(e, r) };
+} };
+var nL = { kernelName: Fo, gradFunc: (e) => ({ x: () => je(e) }) };
+var sL = { kernelName: ii, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => kt(xe(e, V(ha(n, 1.5), 2))) };
+} };
+var rL = { kernelName: Po, inputsToSave: ["condition"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { condition: () => ce(je(n), "float32"), t: () => V(e, ce(n, e.dtype)), e: () => V(e, ce(tb(n), e.dtype)) };
+} };
+var aL = { kernelName: Nl, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => {
+    let s = Un(n, we(0)), r = we(BI), a = we(VI), i = V(e, a), o = V(V(e, r), Yn(ce(n, "float32")));
+    return vn(s, i, o);
+  } };
+} };
+var iL = { kernelName: ui, outputsToSave: [true], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(e, V(n, ge(we(1), n))) };
+} };
+var oL = { kernelName: Tl, gradFunc: (e) => ({ x: () => je(e) }) };
+var uL = { kernelName: oi, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(Xg(ce(n, "float32")), e) };
+} };
+var lL = { kernelName: Mo, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(iI(ce(n, "float32")), e) };
+} };
+var cL = { kernelName: zo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { begin: r, size: a } = n, i = s.shape, [o, u] = Gk(s, r, a), l = [];
+  for (let c = 0; c < e.rank; c++)
+    l.push([o[c], i[c] - o[c] - u[c]]);
+  return { x: () => bi(e, l) };
+} };
+var dL = { kernelName: di, outputsToSave: [true], gradFunc: (e, t, n) => {
+  let [s] = t, { dim: r } = n, a = true, i = V(e, s);
+  return { logits: () => ge(i, V(ve(i, [r], a), s)) };
+} };
+var pL = { kernelName: $l, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(e, qs(n)) };
+} };
+var Ex = { kernelName: Lo, gradFunc: (e, t, n) => {
+  let { blockShape: s, paddings: r } = n;
+  return { x: () => jg(e, s, r) };
+} };
+var Rx = { kernelName: Bo, gradFunc: (e, t, n) => {
+  let { axis: s } = n;
+  return { x: () => Ft(e, s) };
+} };
+var hL = { kernelName: li, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, V(dn(ce(n, "float32")), 2)) };
+} };
+var fL = { kernelName: Al, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(e, V(ce(n, "float32"), 2)) };
+} };
+var mL = { kernelName: pi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = we(2);
+  return { a: () => V(e, V(r, ge(n, s))), b: () => V(e, V(r, ge(s, n))) };
+} };
+var gL = { kernelName: gi, gradFunc: (e) => ({ x: () => je(e) }) };
+var bL = { kernelName: hi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+  let [n, s] = t, r = ot(n.shape, s.shape);
+  return { a: () => {
+    let o = e, u = _t(n.shape, r);
+    return u.length > 0 && (o = ve(o, u)), G(o, n.shape);
+  }, b: () => {
+    let o = e, u = _t(s.shape, r);
+    return u.length > 0 && (o = ve(o, u)), G(kt(o), s.shape);
+  } };
+} };
+var yL = { kernelName: ci, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, r = s.shape.slice(), { axis: a } = n;
+  ts(a, s.shape).forEach((l) => {
+    r[l] = 1;
+  });
+  let o = G(e, r), u = V(o, Mn(s.shape, "float32"));
+  return { x: () => u };
+} };
+var vL = { kernelName: Wo, inputsToSave: ["x"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => xe(e, ct(Xg(n))) };
+} };
+var xL = { kernelName: fi, outputsToSave: [true], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => V(ge(we(1), ct(n)), e) };
+} };
+var wL = { kernelName: Nr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+  let [s] = t, { reps: r } = n;
+  return { x: () => {
+    let i = je(s);
+    if (s.rank === 1)
+      for (let o = 0; o < r[0]; ++o)
+        i = ie(i, qe(e, [o * s.shape[0]], [s.shape[0]]));
+    else if (s.rank === 2)
+      for (let o = 0; o < r[0]; ++o)
+        for (let u = 0; u < r[1]; ++u)
+          i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1]], [s.shape[0], s.shape[1]]));
+    else if (s.rank === 3)
+      for (let o = 0; o < r[0]; ++o)
+        for (let u = 0; u < r[1]; ++u)
+          for (let l = 0; l < r[2]; ++l)
+            i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1], l * s.shape[2]], [s.shape[0], s.shape[1], s.shape[2]]));
+    else if (s.rank === 4)
+      for (let o = 0; o < r[0]; ++o)
+        for (let u = 0; u < r[1]; ++u)
+          for (let l = 0; l < r[2]; ++l)
+            for (let c = 0; c < r[3]; ++c)
+              i = ie(i, qe(e, [o * s.shape[0], u * s.shape[1], l * s.shape[2], c * s.shape[3]], [s.shape[0], s.shape[1], s.shape[2], s.shape[3]]));
+    else
+      throw new Error(`Gradient for tile operation is not implemented for rank-${s.rank} tensors yet.`);
+    return i;
+  } };
+} };
+var kL = { kernelName: mi, gradFunc: (e, t, n) => {
+  let s = n, { perm: r } = s, a = eb(r);
+  return { x: () => Ge(e, a) };
+} };
+var IL = { kernelName: Ho, gradFunc: (e, t, n) => {
+  let s = n, { axis: r } = s;
+  return { value: () => es(e, r) };
+} };
+var SL = { kernelName: cp, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
+  let [n] = t;
+  return { x: () => CL(e, n) };
+} };
+function CL(e, t) {
+  let n = _r(t, je(t)), s = Xu(e, n), r = Xo(t, we(0, "int32")), a = s.rank - r.rank;
+  for (let o = 0; o < a; ++o)
+    r = Pn(r, o + 1);
+  r = Ds(r, Mn(s.shape, "bool"));
+  let i = je(s);
+  return vn(r, s, i);
+}
+var NL = { kernelName: qo, gradFunc: (e) => ({ x: () => je(e) }) };
+var TL = [nS, Sz, Cz, Nz, Tz, $z, _z, Az, Ez, Rz, Dz, Fz, zz, Bz, Vz, Wz, Uz, Gz, Hz, qz, jz, Kz, Yz, Xz, Jz, eM, tM, nM, sM, rM, KM, aM, iM, oM, uM, lM, dM, cM, pM, hM, fM, mM, gM, bM, yM, vM, xM, wM, kM, CM, _x, _x, NM, _M, RM, DM, FM, OM, PM, zM, MM, LM, BM, VM, WM, Ax, Ax, UM, GM, jM, XM, YM, QM, ZM, JM, eL, tL, nL, sL, rL, aL, iL, oL, uL, lL, cL, dL, pL, Ex, Ex, Rx, Rx, hL, mL, fL, gL, bL, yL, vL, xL, wL, kL, IL, SL, NL];
+for (let e of TL)
+  L$(e);
+var $L = {};
+Ae($L, { maxNorm: () => _L, minMaxNorm: () => RL, nonNeg: () => EL, unitNorm: () => AL });
+function Fb(e, t) {
+  return j(() => dn(ve(V(e, e), t, true)));
+}
+var Ul = class extends ae.Serializable {
+  getConfig() {
+    return {};
+  }
+};
+var Ob = class extends Ul {
+  constructor(e) {
+    super(), this.defaultMaxValue = 2, this.defaultAxis = 0, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.axis = e.axis != null ? e.axis : this.defaultAxis;
+  }
+  apply(e) {
+    return j(() => {
+      let t = Fb(e, this.axis), n = Vn(t, 0, this.maxValue);
+      return V(e, xe(n, ie(Rt(), t)));
+    });
+  }
+  getConfig() {
+    return { maxValue: this.maxValue, axis: this.axis };
+  }
+};
+Ob.className = "MaxNorm";
+ae.registerClass(Ob);
+var Pb = class extends Ul {
+  constructor(e) {
+    super(), this.defaultAxis = 0, this.axis = e.axis != null ? e.axis : this.defaultAxis;
+  }
+  apply(e) {
+    return j(() => xe(e, ie(Rt(), Fb(e, this.axis))));
+  }
+  getConfig() {
+    return { axis: this.axis };
+  }
+};
+Pb.className = "UnitNorm";
+ae.registerClass(Pb);
+var zb = class extends Ul {
+  apply(e) {
+    return Ys(e);
+  }
+};
+zb.className = "NonNeg";
+ae.registerClass(zb);
+var Mb = class extends Ul {
+  constructor(e) {
+    super(), this.defaultMinValue = 0, this.defaultMaxValue = 1, this.defaultRate = 1, this.defaultAxis = 0, this.minValue = e.minValue != null ? e.minValue : this.defaultMinValue, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.rate = e.rate != null ? e.rate : this.defaultRate, this.axis = e.axis != null ? e.axis : this.defaultAxis;
+  }
+  apply(e) {
+    return j(() => {
+      let t = Fb(e, this.axis), n = ie(V(this.rate, Vn(t, this.minValue, this.maxValue)), V(1 - this.rate, t));
+      return V(e, xe(n, ie(Rt(), t)));
+    });
+  }
+  getConfig() {
+    return { minValue: this.minValue, maxValue: this.maxValue, rate: this.rate, axis: this.axis };
+  }
+};
+Mb.className = "MinMaxNorm";
+ae.registerClass(Mb);
+var Dx = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
+function Ot(e) {
+  return Ib(e);
+}
+function Fx(e, t = {}) {
+  return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "constraint");
+}
+function Pt(e) {
+  if (e == null)
+    return null;
+  if (typeof e == "string") {
+    let n = { className: e in Dx ? Dx[e] : e, config: {} };
+    return Fx(n);
+  } else
+    return e instanceof Ul ? e : Fx(e);
+}
+function _L(e) {
+  return new Ob(e);
+}
+function AL(e) {
+  return new Pb(e);
+}
+function EL() {
+  return new zb();
+}
+function RL(e) {
+  return new Mb(e);
+}
+var DL = {};
+Ae(DL, { constant: () => PL, glorotNormal: () => UL, glorotUniform: () => WL, heNormal: () => GL, heUniform: () => HL, identity: () => BL, leCunNormal: () => qL, leCunUniform: () => jL, ones: () => OL, orthogonal: () => KL, randomNormal: () => ML, randomUniform: () => zL, truncatedNormal: () => LL, varianceScaling: () => VL, zeros: () => FL });
+function FL() {
+  return new Nb();
+}
+function OL() {
+  return new _p();
+}
+function PL(e) {
+  return new Tb(e);
+}
+function zL(e) {
+  return new $b(e);
+}
+function ML(e) {
+  return new _b(e);
+}
+function LL(e) {
+  return new Ab(e);
+}
+function BL(e) {
+  return new Eb(e);
+}
+function VL(e) {
+  return new xn(e);
+}
+function WL(e) {
+  return new Ap(e);
+}
+function UL(e) {
+  return new Ep(e);
+}
+function GL(e) {
+  return new Rp(e);
+}
+function HL(e) {
+  return new Dp(e);
+}
+function qL(e) {
+  return new Fp(e);
+}
+function jL(e) {
+  return new Op(e);
+}
+function KL(e) {
+  return new Rb(e);
+}
+var XL = {};
+Ae(XL, { Layer: () => He, RNN: () => Er, RNNCell: () => ql, activation: () => xV, add: () => _V, alphaDropout: () => pW, average: () => AV, averagePooling1d: () => jy, averagePooling2d: () => Ky, averagePooling3d: () => Xy, avgPool1d: () => LV, avgPool2d: () => VV, avgPool3d: () => UV, avgPooling1d: () => BV, avgPooling2d: () => WV, avgPooling3d: () => GV, batchNormalization: () => PV, bidirectional: () => rW, concatenate: () => EV, conv1d: () => dV, conv2d: () => pV, conv2dTranspose: () => hV, conv3d: () => fV, conv3dTranspose: () => mV, convLstm2d: () => eW, convLstm2dCell: () => tW, cropping2D: () => bV, dense: () => wV, depthwiseConv2d: () => vV, dot: () => OV, dropout: () => kV, elu: () => aV, embedding: () => $V, flatten: () => SV, gaussianDropout: () => dW, gaussianNoise: () => cW, globalAveragePooling1d: () => HV, globalAveragePooling2d: () => qV, globalMaxPool1d: () => iW, globalMaxPool2d: () => oW, globalMaxPooling1d: () => XS, globalMaxPooling2d: () => YS, gru: () => KV, gruCell: () => XV, input: () => HB, inputLayer: () => rV, layerNormalization: () => zV, leakyReLU: () => oV, lstm: () => YV, lstmCell: () => QV, masking: () => hW, maxPool1d: () => uW, maxPool2d: () => lW, maxPooling1d: () => QS, maxPooling2d: () => ZS, maxPooling3d: () => jV, maximum: () => RV, minimum: () => DV, multiply: () => FV, permute: () => TV, prelu: () => uV, reLU: () => iV, repeatVector: () => CV, reshape: () => NV, rnn: () => nW, separableConv2d: () => gV, simpleRNN: () => ZV, simpleRNNCell: () => JV, softmax: () => lV, spatialDropout1d: () => IV, stackedRNNCells: () => sW, thresholdedReLU: () => cV, timeDistributed: () => aW, upSampling2d: () => yV, zeroPadding2d: () => MV });
+async function ar(e) {
   if (e == null)
     return;
   let t = [], n = [], s = [];
@@ -11524,15 +11749,15 @@ async function rr(e) {
     Re(s);
   }
 }
-function JI(e) {
+function rS(e) {
   if (e != null)
     for (let t in e) {
       let n = e[t];
       typeof n != "number" && n.dispose();
     }
 }
-var PL = 125;
-var Ji = class {
+var YL = 125;
+var to = class {
   constructor() {
     this.validationData = null;
   }
@@ -11554,7 +11779,7 @@ var Ji = class {
   setModel(e) {
   }
 };
-var zL = class {
+var QL = class {
   constructor(e, t = 10) {
     e == null && (e = []), this.callbacks = e, this.queueLength = t;
   }
@@ -11600,7 +11825,7 @@ var zL = class {
       await t.onTrainEnd(e);
   }
 };
-var ML = class extends Ji {
+var ZL = class extends to {
   constructor() {
     super();
   }
@@ -11632,7 +11857,7 @@ var ML = class extends Ji {
         }));
   }
 };
-var LL = class extends Ji {
+var JL = class extends to {
   async onTrainBegin(e) {
     this.epoch = [], this.history = {};
   }
@@ -11656,39 +11881,39 @@ var LL = class extends Ji {
       this.history[t[r]][n[r]].dispose(), this.history[t[r]][n[r]] = s[r][0];
   }
 };
-var BL = class extends Ji {
+var eB = class extends to {
   constructor(e, t) {
-    if (super(), this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || OI, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = PL), this.yieldEvery === "never" && e.onYield != null)
+    if (super(), this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || LI, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = YL), this.yieldEvery === "never" && e.onYield != null)
       throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
-    w.isNumber(this.yieldEvery) && (this.maybeWait = VM(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc)), this.trainBegin = e.onTrainBegin, this.trainEnd = e.onTrainEnd, this.epochBegin = e.onEpochBegin, this.epochEnd = e.onEpochEnd, this.batchBegin = e.onBatchBegin, this.batchEnd = e.onBatchEnd, this.yield = e.onYield;
+    w.isNumber(this.yieldEvery) && (this.maybeWait = GP(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc)), this.trainBegin = e.onTrainBegin, this.trainEnd = e.onTrainEnd, this.epochBegin = e.onEpochBegin, this.epochEnd = e.onEpochEnd, this.batchBegin = e.onBatchBegin, this.batchEnd = e.onBatchEnd, this.yield = e.onYield;
   }
   async maybeWait(e, t, n) {
     let s = [];
-    this.yield != null && (await rr(n), s.push(this.yield(e, t, n))), s.push(this.nextFrameFunc()), await Promise.all(s);
+    this.yield != null && (await ar(n), s.push(this.yield(e, t, n))), s.push(this.nextFrameFunc()), await Promise.all(s);
   }
   async onEpochBegin(e, t) {
-    this.currentEpoch = e, this.epochBegin != null && (await rr(t), await this.epochBegin(e, t));
+    this.currentEpoch = e, this.epochBegin != null && (await ar(t), await this.epochBegin(e, t));
   }
   async onEpochEnd(e, t) {
     let n = [];
-    this.epochEnd != null && (await rr(t), n.push(this.epochEnd(e, t))), this.yieldEvery === "epoch" && n.push(this.nextFrameFunc()), await Promise.all(n);
+    this.epochEnd != null && (await ar(t), n.push(this.epochEnd(e, t))), this.yieldEvery === "epoch" && n.push(this.nextFrameFunc()), await Promise.all(n);
   }
   async onBatchBegin(e, t) {
-    this.batchBegin != null && (await rr(t), await this.batchBegin(e, t));
+    this.batchBegin != null && (await ar(t), await this.batchBegin(e, t));
   }
   async onBatchEnd(e, t) {
     let n = [];
-    this.batchEnd != null && (await rr(t), n.push(this.batchEnd(e, t))), this.yieldEvery === "batch" ? n.push(this.nextFrameFunc()) : w.isNumber(this.yieldEvery) && n.push(this.maybeWait(this.currentEpoch, e, t)), await Promise.all(n);
+    this.batchEnd != null && (await ar(t), n.push(this.batchEnd(e, t))), this.yieldEvery === "batch" ? n.push(this.nextFrameFunc()) : w.isNumber(this.yieldEvery) && n.push(this.maybeWait(this.currentEpoch, e, t)), await Promise.all(n);
   }
   async onTrainBegin(e) {
-    this.trainBegin != null && (await rr(e), await this.trainBegin(e));
+    this.trainBegin != null && (await ar(e), await this.trainBegin(e));
   }
   async onTrainEnd(e) {
-    this.trainEnd != null && (await rr(e), await this.trainEnd(e));
+    this.trainEnd != null && (await ar(e), await this.trainEnd(e));
   }
 };
-function eS(e, t) {
-  return e == null && (e = {}), e instanceof Ji ? [e] : Array.isArray(e) && e[0] instanceof Ji ? e : dt(e).map((s) => new BL(s, t));
+function aS(e, t) {
+  return e == null && (e = {}), e instanceof to ? [e] : Array.isArray(e) && e[0] instanceof to ? e : dt(e).map((s) => new eB(s, t));
 }
 var ks = class {
   constructor() {
@@ -11715,176 +11940,176 @@ var ks = class {
     return t.map((n) => new n());
   }
 };
-var zb = ks;
-zb.constructors = {};
-function tS(e, t, n, s, r, a, i, o, u) {
-  let l = new LL(), c = [new ML(), ...zb.createCallbacks(t)];
+var Lb = ks;
+Lb.constructors = {};
+function iS(e, t, n, s, r, a, i, o, u) {
+  let l = new JL(), c = [new ZL(), ...Lb.createCallbacks(t)];
   e != null && c.push(...e), c.push(l);
-  let p = new zL(c);
+  let p = new QL(c);
   return p.setParams({ epochs: n, initialEpoch: s, samples: r, steps: a, batchSize: i, verbose: t, doValidation: o, metrics: u }), { callbackList: p, history: l };
 }
 function ms(e, t = {}, n = false) {
   return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "layer", n);
 }
-function Nd(e, t) {
+function $d(e, t) {
   return j(() => {
     e.dtype !== "float32" && (e = ce(e, "float32"));
-    let n = ve(Wl(e), t, true), s = Pl(n.shape, Rt()), r = dn($r(n, s));
+    let n = ve(Vl(e), t, true), s = Pl(n.shape, Rt()), r = dn(_r(n, s));
     return xe(e, r);
   });
 }
-function gi(e, t) {
-  return j(() => St(Wl(ge(t, e)), -1));
+function vi(e, t) {
+  return j(() => St(Vl(ge(t, e)), -1));
 }
-function Op(e, t) {
+function zp(e, t) {
   return j(() => St(Mt(ge(t, e)), -1));
 }
-function Zo(e, t) {
+function Jo(e, t) {
   return j(() => {
     let n = ge(e, t), s = Vn(Mt(e), Rt(), Number.MAX_VALUE), r = Mt(xe(n, s));
     return V(100, St(r, -1));
   });
 }
-function VL(e, t) {
+function tB(e, t) {
   return j(() => {
-    let n = Vn(t, Rt(), Number.MAX_VALUE), s = Yn(ie(1, n)), r = Vn(e, Rt(), Number.MAX_VALUE), a = Yn(ie(1, r));
-    return St(Wl(ge(s, a)), -1);
+    let n = Vn(t, Rt(), Number.MAX_VALUE), s = Qn(ie(1, n)), r = Vn(e, Rt(), Number.MAX_VALUE), a = Qn(ie(1, r));
+    return St(Vl(ge(s, a)), -1);
   });
 }
-function WL(e, t) {
+function nB(e, t) {
   return j(() => {
-    let n = $r(0, ge(1, V(e, t)));
-    return St(Wl(n), -1);
+    let n = _r(0, ge(1, V(e, t)));
+    return St(Vl(n), -1);
   });
 }
-function UL(e, t) {
+function sB(e, t) {
   return j(() => {
-    let n = $r(0, ge(1, V(e, t)));
+    let n = _r(0, ge(1, V(e, t)));
     return St(n, -1);
   });
 }
-function GL(e, t) {
+function rB(e, t) {
   return j(() => {
     let n = ve(V(e, t), -1), s = As(V(ge(1, e), t), -1);
-    return $r(0, ie(1, ge(s, n)));
+    return _r(0, ie(1, ge(s, n)));
   });
 }
-function HL(e, t) {
+function aB(e, t) {
   return j(() => {
     let n = Math.log(2), s = ge(t, e), r = ge(ie(s, zl(V(-2, s))), n);
     return St(r, -1);
   });
 }
-function Qu(e, t, n = false) {
+function Zu(e, t, n = false) {
   return j(() => {
     if (n)
-      t = ub(t);
+      t = cb(t);
     else {
       let s = ve(t, t.shape.length - 1, true);
       t = xe(t, s);
     }
-    return t = Vn(t, Rt(), 1 - Rt()), kt(ve(V(ce(e, "float32"), Yn(t)), t.shape.length - 1));
+    return t = Vn(t, Rt(), 1 - Rt()), kt(ve(V(ce(e, "float32"), Qn(t)), t.shape.length - 1));
   });
 }
-function Td(e, t, n = false) {
+function _d(e, t, n = false) {
   return j(() => {
-    let s = ce(mp(sL(e)), "int32");
+    let s = ce(bp(nz(e)), "int32");
     t = Vn(t, Rt(), 1 - Rt());
     let r = t.shape, a = G(vd(s, r[r.length - 1]), r);
-    return Qu(a, t, n);
+    return Zu(a, t, n);
   });
 }
-function qL(e, t) {
+function iB(e, t) {
   if (!w.arraysEqual(e.shape, t.shape))
     throw new U(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);
   return j(() => {
-    let n = Xs(t), s = kt(Mt(t));
-    return ie(ge(n, V(t, e)), Yg(Xn(s)));
+    let n = Ys(t), s = kt(Mt(t));
+    return ie(ge(n, V(t, e)), Zg(Yn(s)));
   });
 }
-function Pp(e, t) {
+function Mp(e, t) {
   return j(() => {
     let n;
-    return n = Vn(t, Rt(), 1 - Rt()), n = Yn(xe(n, ge(1, n))), St(qL(e, n), -1);
+    return n = Vn(t, Rt(), 1 - Rt()), n = Qn(xe(n, ge(1, n))), St(iB(e, n), -1);
   });
 }
-function jL(e, t) {
+function oB(e, t) {
   return j(() => {
     let n = Vn(e, Rt(), 1), s = Vn(t, Rt(), 1);
-    return ve(V(e, Yn(xe(n, s))), -1);
+    return ve(V(e, Qn(xe(n, s))), -1);
   });
 }
-function KL(e, t) {
+function uB(e, t) {
   return j(() => {
-    let n = Yn(ie(Rt(), t));
+    let n = Qn(ie(Rt(), t));
     return St(ge(t, V(e, n)), -1);
   });
 }
-function Mb(e, t) {
+function Bb(e, t) {
   return j(() => {
-    let n = Nd(e, -1), s = Nd(t, -1), r = V(n, s);
+    let n = $d(e, -1), s = $d(t, -1), r = V(n, s);
     return kt(ve(r, -1));
   });
 }
-var $d = { meanSquaredError: gi, meanAbsoluteError: Op, meanAbsolutePercentageError: Zo, meanSquaredLogarithmicError: VL, squaredHinge: WL, hinge: UL, categoricalHinge: GL, logcosh: HL, categoricalCrossentropy: Qu, sparseCategoricalCrossentropy: Td, binaryCrossentropy: Pp, kullbackLeiblerDivergence: jL, poisson: KL, cosineProximity: Mb };
-function Wf(e) {
+var Ad = { meanSquaredError: vi, meanAbsoluteError: zp, meanAbsolutePercentageError: Jo, meanSquaredLogarithmicError: tB, squaredHinge: nB, hinge: sB, categoricalHinge: rB, logcosh: aB, categoricalCrossentropy: Zu, sparseCategoricalCrossentropy: _d, binaryCrossentropy: Mp, kullbackLeiblerDivergence: oB, poisson: uB, cosineProximity: Bb };
+function Gf(e) {
   if (typeof e == "string") {
-    if (e in $d)
-      return $d[e];
+    if (e in Ad)
+      return Ad[e];
     let t = `Unknown loss ${e}`;
     throw e.toLowerCase().includes("softmaxcrossentropy") && (t = `Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`), new U(t);
   } else
     return e;
 }
-function Lb(e, t) {
+function Vb(e, t) {
   return j(() => {
-    let n = V(0.5, Qn(t)), s = Sp(Wn(t, n), e.dtype);
-    return St(Kn(e, s), -1);
+    let n = V(0.5, Zn(t)), s = Tp(Un(t, n), e.dtype);
+    return St(Xn(e, s), -1);
   });
 }
-function Bb(e, t) {
-  return j(() => Sp(Kn(Hu(e, -1), Hu(t, -1)), "float32"));
+function Wb(e, t) {
+  return j(() => Tp(Xn(qu(e, -1), qu(t, -1)), "float32"));
 }
-function nS(e, t) {
-  return j(() => ce(ve(Ds(Kn(e, 1), Kn(t, 1))), "float32"));
+function oS(e, t) {
+  return j(() => ce(ve(Ds(Xn(e, 1), Xn(t, 1))), "float32"));
 }
-function XL(e, t) {
-  return j(() => ce(ve(Ds(Kn(e, 1), Kn(t, 0))), "float32"));
+function lB(e, t) {
+  return j(() => ce(ve(Ds(Xn(e, 1), Xn(t, 0))), "float32"));
 }
-function YL(e, t) {
-  return j(() => ce(ve(Ds(Kn(e, 0), Kn(t, 1))), "float32"));
+function cB(e, t) {
+  return j(() => ce(ve(Ds(Xn(e, 0), Xn(t, 1))), "float32"));
 }
-function sS(e, t) {
+function uS(e, t) {
   return j(() => {
-    let n = nS(e, t), s = YL(e, t), r = ie(n, s);
-    return ce(vn(Wn(r, 0), xe(n, r), 0), "float32");
+    let n = oS(e, t), s = cB(e, t), r = ie(n, s);
+    return ce(vn(Un(r, 0), xe(n, r), 0), "float32");
   });
 }
-function QL(e, t) {
+function dB(e, t) {
   return j(() => {
-    let n = nS(e, t), s = XL(e, t), r = ie(n, s);
-    return ce(vn(Wn(r, 0), xe(n, r), 0), "float32");
+    let n = oS(e, t), s = lB(e, t), r = ie(n, s);
+    return ce(vn(Un(r, 0), xe(n, r), 0), "float32");
   });
 }
-function rS(e, t) {
-  return Pp(e, t);
+function lS(e, t) {
+  return Mp(e, t);
 }
-function aS(e, t) {
-  return e.rank === t.rank && (e = mr(e, [e.rank - 1])), t = Hu(t, -1), t.dtype !== e.dtype && (t = ce(t, e.dtype)), ce(Kn(e, t), "float32");
+function cS(e, t) {
+  return e.rank === t.rank && (e = gr(e, [e.rank - 1])), t = qu(t, -1), t.dtype !== e.dtype && (t = ce(t, e.dtype)), ce(Xn(e, t), "float32");
 }
-var ZL = gi;
-var JL = gi;
-var eB = Op;
-var tB = Op;
-var nB = Zo;
-var sB = Zo;
-var Vb = Qu;
-var rB = Mb;
-var iS = Td;
-var _d = { binaryAccuracy: Lb, categoricalAccuracy: Bb, precision: sS, categoricalCrossentropy: Vb, sparseCategoricalCrossentropy: iS, mse: ZL, MSE: JL, mae: eB, MAE: tB, mape: nB, MAPE: sB, cosine: rB };
-function aB(e) {
-  if (typeof e == "string" && e in _d)
-    return _d[e];
+var pB = vi;
+var hB = vi;
+var fB = zp;
+var mB = zp;
+var gB = Jo;
+var bB = Jo;
+var Ub = Zu;
+var yB = Bb;
+var dS = _d;
+var Ed = { binaryAccuracy: Vb, categoricalAccuracy: Wb, precision: uS, categoricalCrossentropy: Ub, sparseCategoricalCrossentropy: dS, mse: pB, MSE: hB, mae: fB, MAE: mB, mape: gB, MAPE: bB, cosine: yB };
+function vB(e) {
+  if (typeof e == "string" && e in Ed)
+    return Ed[e];
   if (typeof e != "string" && e != null)
     return e;
   throw new U(`Unknown metric ${e}`);
@@ -11894,49 +12119,49 @@ function qc(e) {
     return e;
   {
     let t;
-    for (let n of Object.keys($d))
-      if ($d[n] === e) {
+    for (let n of Object.keys(Ad))
+      if (Ad[n] === e) {
         t = n;
         break;
       }
     if (t !== void 0)
       return t;
-    for (let n of Object.keys(_d))
-      if (_d[n] === e) {
+    for (let n of Object.keys(Ed))
+      if (Ed[n] === e) {
         t = n;
         break;
       }
     return t !== void 0 ? t : e.name;
   }
 }
-function iB(e) {
-  let t = { Adagrad: () => Pi.adagrad(0.01), Adadelta: () => Pi.adadelta(1, 0.95, Rt()), Adam: () => Pi.adam(1e-3, 0.9, 0.999, Rt()), Adamax: () => Pi.adamax(2e-3, 0.9, 0.999, Rt(), 0), RMSProp: () => Pi.rmsprop(1e-3, 0.9, 0, Rt()), SGD: () => Pi.sgd(0.01) };
+function xB(e) {
+  let t = { Adagrad: () => Mi.adagrad(0.01), Adadelta: () => Mi.adadelta(1, 0.95, Rt()), Adam: () => Mi.adam(1e-3, 0.9, 0.999, Rt()), Adamax: () => Mi.adamax(2e-3, 0.9, 0.999, Rt(), 0), RMSProp: () => Mi.rmsprop(1e-3, 0.9, 0, Rt()), SGD: () => Mi.sgd(0.01) };
   if (t.adagrad = t.Adagrad, t.adadelta = t.Adadelta, t.adam = t.Adam, t.adamax = t.Adamax, t.rmsprop = t.RMSProp, t.sgd = t.SGD, e in t)
     return t[e]();
   throw new U(`Unknown Optimizer ${e}`);
 }
-var Rx = 1 * 1024 * 1024;
-function Dx(e, t, n = false) {
-  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !xm(e))
+var Ox = 1 * 1024 * 1024;
+function Px(e, t, n = false) {
+  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !km(e))
     throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
   if (n) {
     let s = JSON.stringify(e);
-    s.length > Rx && console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${Rx}.`);
+    s.length > Ox && console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${Ox}.`);
   }
 }
-function xm(e) {
+function km(e) {
   if (e === null)
     return true;
   if (typeof e == "object")
     if (Object.getPrototypeOf(e) === Object.prototype) {
       let t = Object.keys(e);
       for (let n of t)
-        if (typeof n != "string" || !xm(e[n]))
+        if (typeof n != "string" || !km(e[n]))
           return false;
       return true;
     } else if (Array.isArray(e)) {
       for (let t of e)
-        if (!xm(t))
+        if (!km(t))
           return false;
       return true;
     } else
@@ -11946,8 +12171,8 @@ function xm(e) {
     return t === "string" || t === "number" || t === "boolean";
   }
 }
-function oB(e, t, n, s = console.log) {
-  let r = lB(e), a = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
+function wB(e, t, n, s = console.log) {
+  let r = IB(e), a = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
   r ? (t = t || 90, n = n || [0.32, 0.61, 0.89, 1]) : (t = t || 115, n = n || [0.24, 0.48, 0.7, 0.8, 1]), n[n.length - 1] <= 1 && (n = n.map((c) => Math.floor(t * c)));
   let i;
   if (!r) {
@@ -11955,19 +12180,19 @@ function oB(e, t, n, s = console.log) {
     for (let c in e.nodesByDepth)
       i.push(...e.nodesByDepth[c]);
   }
-  s("_".repeat(t)), Ad(a, n, s), s("=".repeat(t));
+  s("_".repeat(t)), Rd(a, n, s), s("=".repeat(t));
   let o = e.layers;
   for (let c = 0; c < o.length; ++c)
-    r ? cB(o[c], n, s) : dB(o[c], n, i, s), s((c === o.length - 1 ? "=" : "_").repeat(t));
+    r ? SB(o[c], n, s) : CB(o[c], n, i, s), s((c === o.length - 1 ? "=" : "_").repeat(t));
   e.checkTrainableWeightsConsistency();
-  let u = uB(e), l = Cd(e.nonTrainableWeights);
+  let u = kB(e), l = Cd(e.nonTrainableWeights);
   s(`Total params: ${u + l}`), s(`Trainable params: ${u}`), s(`Non-trainable params: ${l}`), s("_".repeat(t));
 }
-function uB(e) {
+function kB(e) {
   let t;
   return e.collectedTrainableWeights != null ? t = Cd(e.collectedTrainableWeights) : t = Cd(e.trainableWeights), t;
 }
-function lB(e) {
+function IB(e) {
   let t = true, n = [], s = [];
   for (let r in e.nodesByDepth)
     n.push(e.nodesByDepth[r]);
@@ -11993,13 +12218,13 @@ function lB(e) {
     }
   return t;
 }
-function Ad(e, t, n = console.log) {
+function Rd(e, t, n = console.log) {
   let s = "";
   for (let r = 0; r < e.length; ++r)
     r > 0 && (s = s.slice(0, s.length - 1) + " "), s += e[r], s = s.slice(0, t[r]), s += " ".repeat(t[r] - s.length);
   n(s);
 }
-function cB(e, t, n) {
+function SB(e, t, n) {
   let s, r;
   try {
     r = e.inboundNodes.map((u) => JSON.stringify(u.inputShapes)).join(",");
@@ -12012,9 +12237,9 @@ function cB(e, t, n) {
     s = "multiple";
   }
   let a = e.name, i = e.getClassName(), o = [`${a} (${i})`, r, s, e.countParams().toString()];
-  Ad(o, t, n);
+  Rd(o, t, n);
 }
-function dB(e, t, n, s) {
+function CB(e, t, n, s) {
   let r, a;
   try {
     a = e.inboundNodes.map((p) => JSON.stringify(p.inputShapes)).join(",");
@@ -12034,25 +12259,25 @@ function dB(e, t, n, s) {
         i.push(`${h}[${f}][${m}]`);
       }
   let o = e.name, u = e.getClassName(), l = i.length === 0 ? "" : i[0], c = [`${o} (${u})`, a, r, e.countParams().toString(), l];
-  Ad(c, t, s);
+  Rd(c, t, s);
   for (let p = 1; p < i.length; ++p)
-    Ad(["", "", "", "", i[p]], t, s);
+    Rd(["", "", "", "", i[p]], t, s);
 }
-function oS(e, t, n) {
+function pS(e, t, n) {
   return (e === "inboundNodes" || e === "outputLayers" || e === "inputLayers") && t === 0 && typeof n == "string";
 }
-function Zu(e, t) {
+function Ju(e, t) {
   if (e === null)
     return null;
   if (typeof e == "string")
-    return Kr(e);
+    return Xr(e);
   if (typeof e == "number" || typeof e == "boolean")
     return e;
   if (e instanceof Array) {
     let n = [], s = e.length;
     for (let r = 0; r < s; ++r) {
       let a = e[r];
-      oS(t, r, a) ? n.push(a) : n.push(Zu(a, t));
+      pS(t, r, a) ? n.push(a) : n.push(Ju(a, t));
     }
     return n;
   } else {
@@ -12062,14 +12287,14 @@ function Zu(e, t) {
       if (s === "name" && typeof r == "string")
         n[s] = r;
       else {
-        let a = Kr(s);
-        n[a] = Zu(r, a);
+        let a = Xr(s);
+        n[a] = Ju(r, a);
       }
     }
     return n;
   }
 }
-function wm(e, t) {
+function Im(e, t) {
   if (e == null)
     return null;
   if (typeof e == "string")
@@ -12080,195 +12305,28 @@ function wm(e, t) {
     let n = [], s = e.length;
     for (let r = 0; r < s; ++r) {
       let a = e[r];
-      oS(t, r, a) ? n.push(a) : n.push(wm(a, t));
+      pS(t, r, a) ? n.push(a) : n.push(Im(a, t));
     }
     return n;
   } else {
     let n = {};
     for (let s of Object.keys(e)) {
       let r = e[s], a = Ws(s);
-      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = wm(r, s);
+      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = Im(r, s);
     }
     return n;
   }
 }
-var uS = "0.0.0";
-function pB(e, t) {
-  if (e.dtype == null || e.dtype === t.dtype)
-    return t;
-  try {
-    return ce(t, e.dtype);
-  } catch (n) {
-    throw new U(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`);
-  }
-}
-var Qr = class {
-  constructor(e) {
-    if (this.id2Value = {}, this.id2Mask = {}, this.name2Id = {}, e instanceof Qr)
-      for (let t in e.id2Value)
-        this.id2Value[t] = e.id2Value[t], t in e.id2Mask && (this.id2Mask[t] = e.id2Mask[t]);
-    else {
-      if (e == null)
-        return;
-      for (let t of e)
-        this.add(t.key, t.value);
-    }
-  }
-  add(e, t, n) {
-    if (this.id2Value[e.id] == null)
-      this.id2Value[e.id] = pB(e, t), this.name2Id[e.name] = e.id, n != null && (this.id2Mask[e.id] = n);
-    else
-      throw new U(`Duplicate key: name=${e.name}, id=${e.id}`);
-    return this;
-  }
-  addFeed(e) {
-    this.add(e.key, e.value);
-  }
-  hasKey(e) {
-    return this.id2Value[e.id] != null;
-  }
-  names() {
-    return Object.keys(this.name2Id);
-  }
-  getValue(e) {
-    if (e instanceof $s) {
-      if (this.id2Value[e.id] == null)
-        throw new U(`Nonexistent key: ${e.name}`);
-      return this.id2Value[e.id];
-    } else {
-      let t = this.name2Id[e];
-      if (t == null)
-        throw new U(`Feed dict has no SymbolicTensor name: ${e}`);
-      return this.id2Value[t];
-    }
-  }
-  getMask(e) {
-    if (e instanceof $s) {
-      if (this.id2Value[e.id] == null)
-        throw new U(`Nonexistent key: ${e.name}`);
-      return this.id2Mask[e.id];
-    } else {
-      let t = this.name2Id[e];
-      if (t == null)
-        throw new U(`Feed dict has no SymbolicTensor name: ${e}`);
-      return this.id2Mask[t];
-    }
-  }
-  disposeMasks() {
-    this.id2Mask != null && Re(this.id2Mask);
-  }
-};
-var Uf = {};
-var Fx = {};
-function Au(e, t, n, s) {
-  let r = n == null ? false : n.training, a = Array.isArray(e), i = a ? e : [e], o = i.map((f) => f.name), u = [], l = t.names();
-  for (let f of o)
-    l.indexOf(f) !== -1 ? u.push(t.getValue(f)) : u.push(null);
-  s != null && (s.maxNumTensors = -1 / 0, s.minNumTensors = 1 / 0);
-  let c = o.join(",") + "|" + t.names().join(","), p, d;
-  if (Uf[c] == null) {
-    let f = hB(i, t);
-    p = f.sorted, d = f.recipientCounts, Uf[c] = p, Fx[c] = d;
-  }
-  p = Uf[c], d = {}, r || Object.assign(d, Fx[c]);
-  let h = new Qr(t);
-  for (let f = 0; f < p.length; ++f) {
-    if (s != null) {
-      let A = dm().numTensors;
-      A > s.maxNumTensors && (s.maxNumTensors = A), A < s.minNumTensors && (s.minNumTensors = A);
-    }
-    let m = p[f], g = m.sourceLayer;
-    if (g instanceof Qo)
-      continue;
-    let b = [], y = [], v = [], x = false;
-    for (let A of m.inputs) {
-      let P = h.getValue(A), R = h.getMask(A);
-      b.push(P), y.push(R), R != null && (x = true), r || (d[A.name]--, d[A.name] === 0 && !t.hasKey(A) && o.indexOf(A.name) === -1 && !P.isDisposed && A.sourceLayer.stateful !== true && v.push(P));
-    }
-    x && (n = n || {}, n.mask = y[0]);
-    let k = dt(g.apply(b, n)), T = null;
-    g.supportsMasking && (T = g.computeMask(b, y));
-    let N = mB(m), E = Array.isArray(N) ? N : [N];
-    for (let A = 0; A < E.length; ++A) {
-      h.hasKey(E[A]) || h.add(E[A], k[A], Array.isArray(T) ? T[0] : T);
-      let P = o.indexOf(E[A].name);
-      P !== -1 && (u[P] = k[A]);
-    }
-    r || Re(v);
-  }
-  return h.disposeMasks(), a ? u : u[0];
-}
-function hB(e, t) {
-  w.assert(e != null && e.length > 0, () => "Expected at least one fetch, got none");
-  let n = [], s = {};
-  if (e.length === 1) {
-    let r = Ox(e[0], t);
-    n = r.sorted, s = r.recipientMap;
-  } else {
-    let r = /* @__PURE__ */ new Set();
-    for (let a of e) {
-      let { sorted: i, recipientMap: o } = Ox(a, t);
-      for (let u of i)
-        r.has(u.name) || (n.push(u), r.add(u.name));
-      for (let u in o)
-        s[u] == null && (s[u] = /* @__PURE__ */ new Set()), o[u].forEach((l) => s[u].add(l));
-    }
-  }
-  return { sorted: n, recipientCounts: fB(s) };
-}
-function fB(e) {
-  let t = {};
-  for (let n in e)
-    t[n] = e[n].size;
-  return t;
-}
-function Ox(e, t) {
-  let n = /* @__PURE__ */ new Set(), s = [], r = {};
-  for (let o of t.names())
-    n.add(o);
-  let a = [], i = [];
-  for (a.push(e); a.length > 0; ) {
-    let o = a[a.length - 1];
-    if (n.has(o.name)) {
-      a.pop();
-      continue;
-    }
-    let u = i[i.length - 1] === a.length - 1;
-    if (o.inputs.length === 0 || u)
-      a.pop(), s.push(o), n.add(o.name), u && i.pop();
-    else {
-      i.push(a.length - 1);
-      for (let l of o.inputs)
-        r[l.name] == null && (r[l.name] = /* @__PURE__ */ new Set()), r[l.name].add(o.name), !n.has(l.name) && a.push(l);
-    }
-  }
-  return { sorted: s, recipientMap: r };
-}
-function mB(e) {
-  let t;
-  if (e.sourceLayer.inboundNodes.length === 1)
-    t = e.sourceLayer.output;
-  else {
-    let n = null;
-    for (let s = 0; s < e.sourceLayer.inboundNodes.length; ++s)
-      for (let r of e.sourceLayer.inboundNodes[s].outputTensors)
-        if (r.id === e.id) {
-          n = s;
-          break;
-        }
-    t = e.sourceLayer.getOutputAt(n);
-  }
-  return t;
-}
-var Is = class extends Ge {
+var hS = "0.0.0";
+var Is = class extends He {
   constructor(e) {
     if (super({}), this.containerNodes = /* @__PURE__ */ new Set(), this.name = e.name, this.name == null) {
       let b = this.getClassName().toLowerCase();
-      this.name = Dp(b);
+      this.name = Np(b);
     }
-    if (this.supportsMasking = false, this.trainable_ = true, Array.isArray(e.inputs) ? this.inputs = e.inputs.slice() : this.inputs = [e.inputs], Array.isArray(e.outputs) ? this.outputs = e.outputs.slice() : this.outputs = [e.outputs], cr(this.inputs).length !== this.inputs.length)
+    if (this.supportsMasking = false, this.trainable_ = true, Array.isArray(e.inputs) ? this.inputs = e.inputs.slice() : this.inputs = [e.inputs], Array.isArray(e.outputs) ? this.outputs = e.outputs.slice() : this.outputs = [e.outputs], dr(this.inputs).length !== this.inputs.length)
       throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((b) => b.name)}`);
-    cr(this.outputs).length !== this.outputs.length && console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((b) => b.name)}`), this.inputLayers = [], this.inputLayersNodeIndices = [], this.inputLayersTensorIndices = [], this.outputLayers = [], this.outputLayersNodeIndices = [], this.outputLayersTensorIndices = [], this.layers = [], this.internalContainerRefs = [];
+    dr(this.outputs).length !== this.outputs.length && console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((b) => b.name)}`), this.inputLayers = [], this.inputLayersNodeIndices = [], this.inputLayersTensorIndices = [], this.outputLayers = [], this.outputLayersNodeIndices = [], this.outputLayersTensorIndices = [], this.layers = [], this.internalContainerRefs = [];
     for (let b of this.outputs) {
       let y = b.sourceLayer, v = b.nodeIndex, x = b.tensorIndex;
       this.outputLayers.push(y), this.outputLayersNodeIndices.push(v), this.outputLayersTensorIndices.push(x);
@@ -12280,7 +12338,7 @@ var Is = class extends Ge {
     this.inputNames = [], this.outputNames = [], this.feedInputShapes = [], this.feedInputNames = [], this.feedOutputNames = [];
     for (let b = 0; b < this.inputLayers.length; b++) {
       let y = this.inputLayers[b];
-      if (!(y instanceof Qo))
+      if (!(y instanceof Zo))
         throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);
       this.inputNames.push(y.name), this.feedInputShapes.push(y.batchInputShape), this.feedInputNames.push(y.name);
     }
@@ -12358,7 +12416,7 @@ var Is = class extends Ge {
       if (y !== 1)
         throw new hs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(g));
     }
-    this.outboundNodes = [], this.inboundNodes = [], new Fp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: this.inputs.map((b) => null), outputMasks: this.outputs.map((b) => null), inputShapes: this.inputs.map((b) => b.shape), outputShapes: this.outputs.map((b) => b.shape) }), this.built = true, this._refCount = 1;
+    this.outboundNodes = [], this.inboundNodes = [], new Pp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: this.inputs.map((b) => null), outputMasks: this.outputs.map((b) => null), inputShapes: this.inputs.map((b) => b.shape), outputShapes: this.outputs.map((b) => b.shape) }), this.built = true, this._refCount = 1;
   }
   assertNotDisposed() {
     if (this._refCount === 0)
@@ -12436,30 +12494,30 @@ var Is = class extends Ge {
       if (a.length > 0)
         throw new U(`${a.length} of ${s} weights are not set: ${a}`);
     }
-    Pb(r);
+    Db(r);
   }
   updatedConfig() {
     let e = this.getConfig(), t = {};
-    return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${uS}`, t.backend = "TensorFlow.js", t;
+    return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${hS}`, t.backend = "TensorFlow.js", t;
   }
   toJSON(e, t = true) {
-    let n = wm(this.updatedConfig());
+    let n = Im(this.updatedConfig());
     return t ? JSON.stringify(n) : n;
   }
   call(e, t) {
     return j(() => {
       e = dt(e);
-      let n = new Qr();
+      let n = new Zr();
       for (let s = 0; s < this.inputs.length; ++s)
         n.add(this.inputs[s], e[s]);
-      return Au(this.outputs, n, t);
+      return Eu(this.outputs, n, t);
     });
   }
   computeMask(e, t) {
     return j(() => {
       e = dt(e);
       let n;
-      return t == null ? n = fa(null, e.length) : n = dt(t), this.runInternalGraph(e, n)[1];
+      return t == null ? n = ma(null, e.length) : n = dt(t), this.runInternalGraph(e, n)[1];
     });
   }
   computeOutputShape(e) {
@@ -12503,7 +12561,7 @@ var Is = class extends Ge {
     return bn(r);
   }
   runInternalGraph(e, t) {
-    t == null && (t = fa(null, e.length));
+    t == null && (t = ma(null, e.length));
     let n = {};
     for (let o = 0; o < this.inputs.length; ++o) {
       let u = this.inputs[o], l = e[o], c = t[o];
@@ -12658,7 +12716,7 @@ var Is = class extends Ge {
     let l = t.name, c = t.layers;
     for (let m of c)
       u(m);
-    for (; !BM(a); )
+    for (; !UP(a); )
       for (let m of c) {
         let g = r[m.name];
         if (g.name in a) {
@@ -12700,7 +12758,7 @@ var Is = class extends Ge {
     });
   }
 };
-function gB(e, t, n) {
+function NB(e, t, n) {
   let s = t.length;
   if (e == null || Array.isArray(e) && e.length === 0)
     return t.map((r) => null);
@@ -12718,19 +12776,19 @@ function gB(e, t, n) {
   } else
     throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`);
 }
-function lS(e, t) {
-  return gB(e, t, "classWeight");
+function fS(e, t) {
+  return NB(e, t, "classWeight");
 }
-async function cS(e, t, n, s) {
+async function mS(e, t, n, s) {
   if (t != null || s != null)
     throw new Error("Support sampleWeight is not implemented yet");
   if (n != null) {
     let r = j(() => {
       if (e.shape.length === 1)
-        return lr(e);
+        return cr(e);
       if (e.shape.length === 2) {
         if (e.shape[1] > 1)
-          return Hu(e, 1);
+          return qu(e, 1);
         if (e.shape[1] === 1)
           return G(e, [e.shape[0]]);
         throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
@@ -12747,14 +12805,14 @@ async function cS(e, t, n, s) {
   } else
     return null;
 }
-function bB(e, t) {
+function TB(e, t) {
   return V(e, t);
 }
-var yB = 32;
-function dS(e, t) {
+var $B = 32;
+function gS(e, t) {
   let n, s, r = t;
   n = r.xs, s = r.ys, w.assert(n != null && s != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);
-  let a = Px("input", e.inputNames, n), i = Px("output", e.outputNames, s), o = a[0].shape[0];
+  let a = zx("input", e.inputNames, n), i = zx("output", e.outputNames, s), o = a[0].shape[0];
   w.assert(a.length === e.inputs.length, () => `LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`), w.assert(i.length === e.outputs.length, () => `LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);
   for (let u = 0; u < a.length; u++)
     w.assert(a[u].shape[0] === o, () => `Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
@@ -12762,7 +12820,7 @@ function dS(e, t) {
     w.assert(i[u].shape[0] === o, () => `Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
   return { xs: a, ys: i };
 }
-function Px(e, t, n) {
+function zx(e, t, n) {
   if (n instanceof et)
     return [n];
   if (Array.isArray(n))
@@ -12777,12 +12835,12 @@ function Px(e, t, n) {
     return s;
   }
 }
-function vB(e) {
+function _B(e) {
   if (e.length === 3)
     throw new Fe("Validation with sample weights is not implemented yet.");
   return { xs: e[0], ys: e[1] };
 }
-async function xB(e, t, n) {
+async function AB(e, t, n) {
   let s = n.batchesPerEpoch != null;
   if (w.assert(e.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."), w.assert(n != null, () => "For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."), w.assert(n.epochs != null && n.epochs > 0 && Number.isInteger(n.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`), w.assert(!s || n.batchesPerEpoch > 0 && Number.isInteger(n.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`), w.assert(n.validationSplit == null, () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead."), e.isTraining)
     throw new Error("Cannot start training because another fit() call is ongoing.");
@@ -12790,15 +12848,15 @@ async function xB(e, t, n) {
   try {
     let r = n.validationData != null, a, i;
     if (r)
-      if (zx(n.validationData))
+      if (Mx(n.validationData))
         w.assert(n.validationBatches == null || n.validationBatches > 0 && Number.isInteger(n.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);
       else {
-        let g = vB(n.validationData);
+        let g = _B(n.validationData);
         a = g.xs, i = g.ys;
       }
     let o = e.makeTrainFunction(), u = e.getDedupedMetricsNames(), l;
     r ? l = u.slice().concat(u.map((g) => "val_" + g)) : l = u.slice();
-    let c = eS(n.callbacks, n.yieldEvery), p = n.verbose == null ? 1 : n.verbose, { callbackList: d, history: h } = tS(c, p, n.epochs, null, null, wB(t, n), null, r, l);
+    let c = aS(n.callbacks, n.yieldEvery), p = n.verbose == null ? 1 : n.verbose, { callbackList: d, history: h } = iS(c, p, n.epochs, null, null, EB(t, n), null, r, l);
     d.setModel(e), e.history = h, await d.onTrainBegin(), e.stopTraining_ = false;
     let f = n.initialEpoch == null ? 0 : n.initialEpoch, m = await t.iterator();
     for (; f < n.epochs; ) {
@@ -12812,13 +12870,13 @@ async function xB(e, t, n) {
           break;
         }
         if (v.value != null) {
-          let { xs: x, ys: k } = dS(e, v.value), T = {};
+          let { xs: x, ys: k } = gS(e, v.value), T = {};
           T.batch = y, T.size = x[0].shape[0], await d.onBatchBegin(y, T);
           let N = [];
           if (n.classWeight != null) {
-            let P = lS(n.classWeight, e.outputNames);
+            let P = fS(n.classWeight, e.outputNames);
             for (let R = 0; R < P.length; ++R)
-              N.push(await cS(k[R], null, P[R]));
+              N.push(await mS(k[R], null, P[R]));
           }
           let E = x.concat(k).concat(N), A = o(E);
           Re(E);
@@ -12826,12 +12884,12 @@ async function xB(e, t, n) {
             let R = u[P], F = A[P];
             T[R] = F, Ht(F);
           }
-          await d.onBatchEnd(y, T), JI(T), y++, b++;
+          await d.onBatchEnd(y, T), rS(T), y++, b++;
         }
         if (s ? b >= n.batchesPerEpoch : v.done) {
           if (r) {
             let x;
-            zx(n.validationData) ? x = dt(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = dt(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? yB : n.validationBatchSize, verbose: 0 }));
+            Mx(n.validationData) ? x = dt(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = dt(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? $B : n.validationBatchSize, verbose: 0 }));
             for (let k = 0; k < e.metricsNames.length; ++k)
               g[`val_${e.metricsNames[k]}`] = x[k];
           }
@@ -12848,28 +12906,28 @@ async function xB(e, t, n) {
     e.isTraining = false;
   }
 }
-function wB(e, t) {
+function EB(e, t) {
   let n = null;
   return t.batchesPerEpoch != null ? n = t.batchesPerEpoch : Number.isFinite(e.size) && (n = e.size), n;
 }
-function zx(e) {
+function Mx(e) {
   return typeof e.iterator == "function";
 }
-function kB(e) {
+function RB(e) {
   return typeof e.next == "function";
 }
-async function IB(e, t, n) {
+async function DB(e, t, n) {
   n = n || {};
   let s = n.batches != null, r = e.testFunction, a = [];
   if (n.verbose > 0)
     throw new Fe("Verbose mode is not implemented yet.");
   w.assert(!s || n.batches > 0 && Number.isInteger(n.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);
-  let i = kB(t) ? t : await t.iterator(), o = 0, u = 0;
+  let i = RB(t) ? t : await t.iterator(), o = 0, u = 0;
   for (; !s || u < n.batches; ) {
     let l = await i.next();
     if (a = j(() => {
       if (l.value) {
-        let { xs: c, ys: p } = dS(e, l.value), d = c.concat(p), h = j(() => r(d));
+        let { xs: c, ys: p } = gS(e, l.value), d = c.concat(p), h = j(() => r(d));
         if (Re(d), u === 0)
           for (let m = 0; m < h.length; ++m)
             a.push(we(0));
@@ -12892,29 +12950,29 @@ async function IB(e, t, n) {
   }
   return bn(a);
 }
-function km(e) {
+function Sm(e) {
   w.assert(e > 0 && Number.isInteger(e), () => `batchSize is required to be a positive integer, but got ${e}`);
 }
-function Eu(e, t, n) {
-  return e == null ? [null] : Array.isArray(e) ? e.map((s) => ta(s, t, n - t)) : ta(e, t, n - t);
+function Ru(e, t, n) {
+  return e == null ? [null] : Array.isArray(e) ? e.map((s) => na(s, t, n - t)) : na(e, t, n - t);
 }
-function Wb(e, t) {
-  return j(() => e == null ? null : Array.isArray(e) ? e.map((n) => Wb(n, t)) : KI(e, t.dtype === "int32" ? t : ce(t, "int32")));
+function Gb(e, t) {
+  return j(() => e == null ? null : Array.isArray(e) ? e.map((n) => Gb(n, t)) : ZI(e, t.dtype === "int32" ? t : ce(t, "int32")));
 }
-function Im(e, t) {
+function Cm(e, t) {
   let n = [], s = 0, r = null;
   for (; s < e; )
     r = s + t, r >= e && (r = e), n.push([s, r]), s = r;
   return n;
 }
-async function SB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
+async function FB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
   r == null && (r = 32), a == null && (a = 1), c == null && (c = true), d == null && (d = 0);
   let m = false;
   if (u != null && l != null && (m = true), f != null && (m = true, h == null))
     throw new U("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
   let g = e.checkNumSamples(n, r, h, "steps_per_epoch"), b;
-  g != null && (b = ys(0, g)), i == null && (i = 1);
-  let { callbackList: y, history: v } = tS(o, i, a, d, g, h, r, m, p);
+  g != null && (b = bs(0, g)), i == null && (i = 1);
+  let { callbackList: y, history: v } = iS(o, i, a, d, g, h, r, m, p);
   y.setModel(e), e.history = v, await y.onTrainBegin(), e.stopTraining_ = false;
   for (let x = d; x < a; ++x) {
     await y.onEpochBegin(x);
@@ -12925,13 +12983,13 @@ async function SB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
       if (c === "batch")
         throw new Fe("batch shuffling is not implemneted yet");
       c && w.shuffle(b);
-      let T = Zt(b), N = Im(g, r);
+      let T = Zt(b), N = Cm(g, r);
       for (let E = 0; E < N.length; ++E) {
         let A = {};
         if (await y.onBatchBegin(E, A), j(() => {
-          let P = N[E][0], R = N[E][1], F = ta(T, P, R - P);
+          let P = N[E][0], R = N[E][1], F = na(T, P, R - P);
           A.batch = E, A.size = R - P;
-          let $ = Wb(n, F), z = t($);
+          let $ = Gb(n, F), z = t($);
           for (let W = 0; W < s.length; ++W) {
             let q = s[W], K = z[W];
             A[q] = K, Ht(K);
@@ -12943,7 +13001,7 @@ async function SB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
               Ht(Y), k["val_" + K] = Y;
             }
           }
-        }), await y.onBatchEnd(E, A), JI(A), e.stopTraining_)
+        }), await y.onBatchEnd(E, A), rS(A), e.stopTraining_)
           break;
       }
       T.dispose();
@@ -12953,14 +13011,14 @@ async function SB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
   }
   return await y.onTrainEnd(), await e.history.syncData(), e.history;
 }
-async function CB(e, t, n, s = {}) {
+async function OB(e, t, n, s = {}) {
   if (e.isTraining)
     throw new Error("Cannot start training because another fit() call is ongoing.");
   e.isTraining = true;
   let r, a, i, o, u, l, c, p, d;
   try {
     let h = s.batchSize == null ? 32 : s.batchSize;
-    km(h);
+    Sm(h);
     let f = false, m = await e.standardizeUserData(t, n, s.sampleWeight, s.classWeight, f, h);
     r = m[0], a = m[1], d = m[2];
     let g = false, b;
@@ -12974,26 +13032,26 @@ async function CB(e, t, n, s = {}) {
     } else if (s.validationSplit != null && s.validationSplit > 0 && s.validationSplit < 1) {
       g = true;
       let A = Math.floor(r[0].shape[0] * (1 - s.validationSplit)), P = r[0].shape[0];
-      c = Eu(r, A, P), i = r, r = Eu(r, 0, A), p = Eu(a, A, P), o = a, a = Eu(a, 0, A), b = c.concat(p);
+      c = Ru(r, A, P), i = r, r = Ru(r, 0, A), p = Ru(a, A, P), o = a, a = Ru(a, 0, A), b = c.concat(p);
     } else
       s.validationSteps != null && (g = true);
     let y = r.concat(a).concat(d);
     e.checkTrainableWeightsConsistency();
     let v = e.makeTrainFunction(), x = e.getDedupedMetricsNames(), k, T;
     g ? (e.makeTestFunction(), k = e.testFunction, T = x.slice().concat(x.map((A) => "val_" + A))) : (k = null, b = [], T = x.slice());
-    let N = eS(s.callbacks, s.yieldEvery);
-    return await SB(e, v, y, x, h, s.epochs, s.verbose, N, k, b, s.shuffle, T, s.initialEpoch, null, null);
+    let N = aS(s.callbacks, s.yieldEvery);
+    return await FB(e, v, y, x, h, s.epochs, s.verbose, N, k, b, s.shuffle, T, s.initialEpoch, null, null);
   } finally {
     e.isTraining = false, ds(r, t), ds(a, n), ds(i, t), ds(o, n), ds(c, u), ds(p, l), d != null && Re(d);
   }
 }
-function pS(e) {
+function bS(e) {
   let t = [];
   e instanceof et && (e = [e]);
   for (let n = 0; n < e.length; ++n) {
     let s = e[n];
     if (s.rank === 1)
-      t.push(Vl(s, 1));
+      t.push(Bl(s, 1));
     else {
       if (s.rank === 0)
         throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
@@ -13031,22 +13089,22 @@ function ds(e, t) {
     r.isDisposed || r.dispose();
   });
 }
-function NB(e) {
+function PB(e) {
   return e instanceof et;
 }
-function Sm(e) {
+function Nm(e) {
   return Array.isArray(e);
 }
-function Mx(e) {
-  return !NB(e) && !Sm(e);
+function Lx(e) {
+  return !PB(e) && !Nm(e);
 }
-function Lx(e, t, n, s = true, r = "") {
+function Bx(e, t, n, s = true, r = "") {
   if (t == null || t.length === 0) {
     if (e != null) {
       let i = false;
-      if (Sm(e) && e.length > 0)
+      if (Nm(e) && e.length > 0)
         i = true;
-      else if (Mx(e)) {
+      else if (Lx(e)) {
         for (let o in e)
           if (e.hasOwnProperty(o)) {
             i = true;
@@ -13062,14 +13120,14 @@ function Lx(e, t, n, s = true, r = "") {
   if (e == null)
     return t.map((i) => null);
   let a;
-  if (Mx(e)) {
+  if (Lx(e)) {
     e = e, a = [];
     for (let i of t) {
       if (e[i] == null)
         throw new U(`No data provided for "${i}". Need data for each key in: ${t}`);
       a.push(e[i]);
     }
-  } else if (Sm(e)) {
+  } else if (Nm(e)) {
     if (e = e, e.length !== t.length)
       throw new U(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);
     a = e;
@@ -13078,7 +13136,7 @@ function Lx(e, t, n, s = true, r = "") {
       throw new U(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);
     a = [e];
   }
-  if (a = pS(a), n != null)
+  if (a = bS(a), n != null)
     for (let i = 0; i < t.length; ++i) {
       if (n[i] == null)
         continue;
@@ -13095,10 +13153,10 @@ function Lx(e, t, n, s = true, r = "") {
     }
   return a;
 }
-function TB(e, t, n) {
-  let s = cr(e.map((a) => a.shape[0]));
+function zB(e, t, n) {
+  let s = dr(e.map((a) => a.shape[0]));
   s.sort();
-  let r = cr(t.map((a) => a.shape[0]));
+  let r = dr(t.map((a) => a.shape[0]));
   if (r.sort(), s.length > 1)
     throw new U(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(e.map((a) => a.shape))}`);
   if (r.length > 1)
@@ -13106,12 +13164,12 @@ function TB(e, t, n) {
   if (s.length > 0 && r.length > 0 && !w.arraysEqual(s, r))
     throw new U(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`);
 }
-function $B(e, t, n) {
-  let s = [gi, Pp, Qu];
+function MB(e, t, n) {
+  let s = [vi, Mp, Zu];
   for (let r = 0; r < e.length; ++r) {
     let a = e[r], i = t[r], o = n[r];
     if (i != null) {
-      if (i === Qu && a.shape[a.shape.length - 1] === 1)
+      if (i === Zu && a.shape[a.shape.length - 1] === 1)
         throw new U(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
       if (s.indexOf(i) !== -1) {
         let u = a.shape.slice(1), l = o.slice(1);
@@ -13124,7 +13182,7 @@ function $B(e, t, n) {
     }
   }
 }
-function Bx(e, t, n, s = true, r = "") {
+function Vx(e, t, n, s = true, r = "") {
   let a;
   if (Array.isArray(e)) {
     if (e.length !== t.length)
@@ -13151,7 +13209,7 @@ function Bx(e, t, n, s = true, r = "") {
       }
     }
 }
-function _B(e, t) {
+function LB(e, t) {
   if (e == null || Array.isArray(e) && e.length === 0)
     return t.map((s) => []);
   let n;
@@ -13172,21 +13230,21 @@ function _B(e, t) {
     return s;
   }
 }
-var AB = "layers-model";
-var pr = class extends Is {
+var BB = "layers-model";
+var hr = class extends Is {
   constructor(e) {
     super(e), this.isTraining = false;
   }
   summary(e, t, n = console.log) {
     if (!this.built)
       throw new U("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");
-    oB(this, e, t, n);
+    wB(this, e, t, n);
   }
   compile(e) {
     if (e.loss == null && (e.loss = []), this.loss = e.loss, typeof e.optimizer == "string")
-      this.optimizer_ = iB(e.optimizer), this.isOptimizerOwned = true;
+      this.optimizer_ = xB(e.optimizer), this.isOptimizerOwned = true;
     else {
-      if (!(e.optimizer instanceof _r))
+      if (!(e.optimizer instanceof Ar))
         throw new U("User-defined optimizer must be an instance of tf.Optimizer.");
       this.optimizer_ = e.optimizer, this.isOptimizerOwned = false;
     }
@@ -13197,13 +13255,13 @@ var pr = class extends Is {
         if (this.outputNames.indexOf(a) === -1)
           throw new U(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);
       for (let a of this.outputNames)
-        e.loss[a] == null && console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`), t.push(Wf(e.loss[a]));
+        e.loss[a] == null && console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`), t.push(Gf(e.loss[a]));
     } else if (Array.isArray(e.loss)) {
       if (e.loss.length !== this.outputs.length)
         throw new U(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);
-      t = e.loss.map((i) => Wf(i));
+      t = e.loss.map((i) => Gf(i));
     } else {
-      let a = Wf(e.loss);
+      let a = Gf(e.loss);
       this.outputs.forEach((i) => {
         t.push(a);
       });
@@ -13214,7 +13272,7 @@ var pr = class extends Is {
       this.feedOutputNames.push(o), this.feedOutputShapes.push(i), this.feedLossFns.push(this.lossFunctions[a]);
     }
     let n = [];
-    this.metrics = e.metrics, this.metricsNames = ["loss"], this.metricsTensors = [], ea("loss", () => {
+    this.metrics = e.metrics, this.metricsNames = ["loss"], this.metricsTensors = [], ta("loss", () => {
       for (let a = 0; a < this.outputs.length; ++a) {
         if (n.indexOf(a) !== -1)
           continue;
@@ -13222,10 +13280,10 @@ var pr = class extends Is {
         this.outputs.length > 1 && (this.metricsTensors.push([i, a]), this.metricsNames.push(this.outputNames[a] + "_loss"));
       }
     });
-    let s = _B(e.metrics, this.outputNames), r = (a, i, o) => {
+    let s = LB(e.metrics, this.outputNames), r = (a, i, o) => {
       this.outputNames.length > 1 && (i = this.outputNames[a] + "_" + i), this.metricsNames.push(i), this.metricsTensors.push([o, a]);
     };
-    ea("metric", () => {
+    ta("metric", () => {
       for (let a = 0; a < this.outputs.length; ++a) {
         if (n.indexOf(a) !== -1)
           continue;
@@ -13235,13 +13293,13 @@ var pr = class extends Is {
           for (let h of u) {
             if (typeof h == "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(h) !== -1) {
               let m = this.internalOutputShapes[a];
-              m[m.length - 1] === 1 || this.lossFunctions[a] === Pp ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = Lb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = rS) : this.lossFunctions[a] === Td ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = aS : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = iS) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Bb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Vb);
+              m[m.length - 1] === 1 || this.lossFunctions[a] === Mp ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = Vb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = lS) : this.lossFunctions[a] === _d ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = cS : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = dS) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Wb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Ub);
               let g;
               ["accuracy", "acc"].indexOf(h) !== -1 ? g = "acc" : ["crossentropy", "ce"].indexOf(h) !== -1 && (g = "ce"), d = p, c = l + g;
             } else
-              d = aB(h), c = l + qc(h);
+              d = vB(h), c = l + qc(h);
             let f;
-            ea(c, () => {
+            ta(c, () => {
               f = d;
             }), r(a, c, f);
           }
@@ -13254,7 +13312,7 @@ var pr = class extends Is {
   }
   evaluate(e, t, n = {}) {
     let s = n.batchSize == null ? 32 : n.batchSize;
-    km(s);
+    Sm(s);
     let r = true, a = this.standardizeUserDataXY(e, t, r, s);
     try {
       let i = a[0].concat(a[1]);
@@ -13266,7 +13324,7 @@ var pr = class extends Is {
     }
   }
   async evaluateDataset(e, t) {
-    return this.makeTestFunction(), IB(this, e, t);
+    return this.makeTestFunction(), DB(this, e, t);
   }
   checkNumSamples(e, t, n, s = "steps") {
     let r;
@@ -13282,7 +13340,7 @@ var pr = class extends Is {
   execute(e, t) {
     if (Array.isArray(t) && t.length === 0)
       throw new U("`outputs` is an empty Array, which is not allowed.");
-    let n = Array.isArray(t), s = n ? t : [t], r = this.retrieveSymbolicTensors(s), a = new Qr();
+    let n = Array.isArray(t), s = n ? t : [t], r = this.retrieveSymbolicTensors(s), a = new Zr();
     if (e instanceof et && (e = [e]), Array.isArray(e)) {
       if (e.length !== this.inputs.length)
         throw new U(`The number of inputs provided (${e.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
@@ -13295,11 +13353,11 @@ var pr = class extends Is {
           throw new U(`No value is provided for the model's input ${o.name}`);
         a.add(o, u);
       }
-    let i = Au(r, a);
+    let i = Eu(r, a);
     return n ? i : i[0];
   }
   retrieveSymbolicTensors(e) {
-    let t = fa(null, e.length), n = e.length;
+    let t = ma(null, e.length), n = e.length;
     for (let s of this.layers) {
       let r = Array.isArray(s.output) ? s.output : [s.output], a = r.map((i) => i.name);
       for (let i = 0; i < e.length; ++i) {
@@ -13323,33 +13381,33 @@ var pr = class extends Is {
       let s = this.checkNumSamples(e);
       if (n)
         throw new Fe("Verbose predictLoop() is not implemented yet.");
-      let r = Im(s, t), a = this.outputs.map((i) => []);
+      let r = Cm(s, t), a = this.outputs.map((i) => []);
       for (let i = 0; i < r.length; ++i)
         j(() => {
-          let u = r[i][0], l = r[i][1], c = Eu(e, u, l), p = [];
+          let u = r[i][0], l = r[i][1], c = Ru(e, u, l), p = [];
           if (Array.isArray(c))
             for (let h = 0; h < c.length; ++h)
               p.push({ key: this.inputs[h], value: c[h] });
           else
             p.push({ key: this.inputs[0], value: c });
-          let d = new Qr(p);
-          return Au(this.outputs, d);
+          let d = new Zr(p);
+          return Eu(this.outputs, d);
         }).forEach((u, l) => a[l].push(u));
       return bn(a.map((i) => Ft(i, 0)));
     });
   }
   predict(e, t = {}) {
-    let n = pS(e);
-    Bx(n, this.inputNames, this.feedInputShapes, false);
+    let n = bS(e);
+    Vx(n, this.inputNames, this.feedInputShapes, false);
     try {
       let s = t.batchSize == null ? 32 : t.batchSize;
-      return km(s), this.predictLoop(n, s);
+      return Sm(s), this.predictLoop(n, s);
     } finally {
       ds(n, e);
     }
   }
   predictOnBatch(e) {
-    Bx(e, this.inputNames, this.feedInputShapes, true);
+    Vx(e, this.inputNames, this.feedInputShapes, true);
     let t = (Array.isArray(e) ? e[0] : e).shape[0];
     return this.predictLoop(e, t);
   }
@@ -13359,9 +13417,9 @@ var pr = class extends Is {
     let r = [];
     for (let a = 0; a < this.feedOutputShapes.length; ++a) {
       let i = this.feedOutputShapes[a];
-      this.feedLossFns[a] === Td ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
+      this.feedLossFns[a] === _d ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
     }
-    if (e = Lx(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = Lx(t, this.feedOutputNames, r, false, "target"), TB(e, t, null), $B(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
+    if (e = Bx(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = Bx(t, this.feedOutputNames, r, false, "target"), zB(e, t, null), MB(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
       throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);
     return [e, t];
   }
@@ -13371,10 +13429,10 @@ var pr = class extends Is {
       throw new Error("sample weight is not supported yet.");
     let u = null;
     if (s != null) {
-      let l = lS(s, this.outputNames);
+      let l = fS(s, this.outputNames);
       u = [];
       for (let c = 0; c < l.length; ++c)
-        u.push(await cS(o[c], null, l[c]));
+        u.push(await mS(o[c], null, l[c]));
     }
     return [i, o, u];
   }
@@ -13386,9 +13444,9 @@ var pr = class extends Is {
       if (r != null)
         throw new Fe("steps mode in testLoop() is not implemented yet");
       {
-        let o = Im(a, n), u = Zt(ys(0, a));
+        let o = Cm(a, n), u = Zt(bs(0, a));
         for (let l = 0; l < o.length; ++l) {
-          let c = o[l][0], p = o[l][1], d = ta(u, c, p - c), h = Wb(t, d), f = e(h);
+          let c = o[l][0], p = o[l][1], d = na(u, c, p - c), h = Gb(t, d), f = e(h);
           if (l === 0)
             for (let m = 0; m < f.length; ++m)
               i.push(we(0));
@@ -13407,7 +13465,7 @@ var pr = class extends Is {
     let e = this.metricsNames, t = [];
     for (let n = 0; n < e.length; ++n) {
       let s = e[n], r = s;
-      Sx(e, s) > 1 && (r += `_${Sx(e.slice(0, n), s)}`), t.push(r);
+      xx(e, s) > 1 && (r += `_${xx(e.slice(0, n), s)}`), t.push(r);
     }
     return t;
   }
@@ -13417,10 +13475,10 @@ var pr = class extends Is {
         let c = [];
         for (let f = 0; f < this.inputs.length; ++f)
           c.push({ key: this.inputs[f], value: n[f] });
-        let p = new Qr(c), d = Au(this.outputs, p, { training: true }), h;
+        let p = new Zr(c), d = Eu(this.outputs, p, { training: true }), h;
         for (let f = 0; f < this.lossFunctions.length; ++f) {
           let g = this.lossFunctions[f](s[f], d[f]);
-          r[f] != null && (g = bB(g, r[f]));
+          r[f] != null && (g = TB(g, r[f]));
           let b = St(g);
           t.push(b), f === 0 ? h = g : h = ie(h, g);
         }
@@ -13446,7 +13504,7 @@ var pr = class extends Is {
       let t = [], n, s = e.slice(0, this.inputs.length), r = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), a = [];
       for (let u = 0; u < this.inputs.length; ++u)
         a.push({ key: this.inputs[u], value: s[u] });
-      let i = new Qr(a), o = Au(this.outputs, i);
+      let i = new Zr(a), o = Eu(this.outputs, i);
       for (let u = 0; u < this.lossFunctions.length; ++u) {
         let l = this.lossFunctions[u], c = St(l(r[u], o[u]));
         u === 0 ? n = c : n = ie(n, c), t.push(n);
@@ -13459,10 +13517,10 @@ var pr = class extends Is {
     });
   }
   async fit(e, t, n = {}) {
-    return CB(this, e, t, n);
+    return OB(this, e, t, n);
   }
   async fitDataset(e, t) {
-    return xB(this, e, t);
+    return AB(this, e, t);
   }
   async trainOnBatch(e, t) {
     let n = await this.standardizeUserData(e, t), s = n[0], r = n[1], i = this.makeTrainFunction()(s.concat(r)), o = [];
@@ -13493,8 +13551,8 @@ var pr = class extends Is {
   dispose() {
     let e = super.dispose();
     if (e.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
-      let t = dm().numTensors;
-      this.optimizer_.dispose(), e.numDisposedVariables += t - dm().numTensors;
+      let t = pm().numTensors;
+      this.optimizer_.dispose(), e.numDisposedVariables += t - pm().numTensors;
     }
     return e;
   }
@@ -13541,23 +13599,23 @@ var pr = class extends Is {
       throw new Error("Loading loss_weights is not supported yet.");
     if (e.sample_weight_mode != null)
       throw new Error("Loading sample_weight_mode is not supported yet.");
-    let t = Zu(e.optimizer_config), n = ms(t), s;
+    let t = Ju(e.optimizer_config), n = ms(t), s;
     if (typeof e.loss == "string")
-      s = Kr(e.loss);
+      s = Xr(e.loss);
     else if (Array.isArray(e.loss))
-      s = e.loss.map((a) => Kr(a));
+      s = e.loss.map((a) => Xr(a));
     else if (e.loss != null) {
       s = {};
       for (let a in e.loss)
-        s[a] = Kr(e.loss[a]);
+        s[a] = Xr(e.loss[a]);
     }
     let r;
     if (Array.isArray(e.metrics))
-      r = e.metrics.map((a) => Kr(a));
+      r = e.metrics.map((a) => Xr(a));
     else if (e.metrics != null) {
       r = {};
       for (let a in e.metrics)
-        r[a] = Kr(e.metrics[a]);
+        r[a] = Xr(e.metrics[a]);
     }
     this.compile({ loss: s, metrics: r, optimizer: n });
   }
@@ -13572,32 +13630,32 @@ var pr = class extends Is {
     }
     if (e.save == null)
       throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
-    let n = await En.encodeWeights(this.getNamedWeights(t)), s = false, r = null, i = { modelTopology: this.toJSON(r, s), format: AB, generatedBy: `TensorFlow.js tfjs-layers v${uS}`, convertedBy: null };
+    let n = await En.encodeWeights(this.getNamedWeights(t)), s = false, r = null, i = { modelTopology: this.toJSON(r, s), format: BB, generatedBy: `TensorFlow.js tfjs-layers v${hS}`, convertedBy: null };
     if ((t == null ? false : t.includeOptimizer) && this.optimizer != null) {
       i.trainingConfig = this.getTrainingConfig();
       let u = "optimizer", { data: l, specs: c } = await En.encodeWeights(await this.optimizer.getWeights(), u);
       n.specs.push(...c), n.data = En.concatenateArrayBuffers([n.data, l]);
     }
-    return this.userDefinedMetadata != null && (Dx(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
+    return this.userDefinedMetadata != null && (Px(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
   }
   setUserDefinedMetadata(e) {
-    Dx(e, this.name), this.userDefinedMetadata = e;
+    Px(e, this.name), this.userDefinedMetadata = e;
   }
   getUserDefinedMetadata() {
     return this.userDefinedMetadata;
   }
 };
-pr.className = "Model";
-ae.registerClass(pr);
-var hS = class extends pr {
+hr.className = "Model";
+ae.registerClass(hr);
+var yS = class extends hr {
 };
-hS.className = "Functional";
-ae.registerClass(hS);
-async function EB(e, t) {
+yS.className = "Functional";
+ae.registerClass(yS);
+async function VB(e, t) {
   "modelTopology" in e || (e = { modelTopology: e }), e = e;
   let n = e.modelTopology;
   n.model_config != null && (n = n.model_config);
-  let s = Zu(n), r = ms(s, t);
+  let s = Ju(n), r = ms(s, t);
   if (e.weightsManifest != null) {
     let a = await En.loadWeights(e.weightsManifest, e.pathPrefix, r.weights.map((o) => o.originalName)), i = {};
     for (let o of r.weights)
@@ -13606,7 +13664,7 @@ async function EB(e, t) {
   }
   return r;
 }
-async function RB(e, t) {
+async function WB(e, t) {
   if (t == null && (t = {}), typeof e == "string") {
     let n = En.getLoadHandlers(e, t);
     if (n.length === 0)
@@ -13615,31 +13673,31 @@ async function RB(e, t) {
       throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);
     e = n[0];
   }
-  return DB(e, void 0, t);
+  return UB(e, void 0, t);
 }
-async function DB(e, t, n) {
+async function UB(e, t, n) {
   if (n == null && (n = {}), e.load == null)
     throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
   let s = await e.load(), r = s.modelTopology;
   r.model_config != null && (r = r.model_config);
-  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = ms(Zu(r), t, i), u = s.trainingConfig;
+  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = ms(Ju(r), t, i), u = s.trainingConfig;
   if (u != null && o.loadTrainingConfig(u), s.userDefinedMetadata != null && o.setUserDefinedMetadata(s.userDefinedMetadata), s.weightData != null) {
     if (s.weightSpecs == null)
       throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
-    let { modelWeights: l, optimizerWeights: c } = FB(s.weightData, s.weightSpecs);
+    let { modelWeights: l, optimizerWeights: c } = GB(s.weightData, s.weightSpecs);
     o.loadWeights(l, a), o.optimizer != null && c.length > 0 && await o.optimizer.setWeights(c), Re(l), Re(c.map((p) => p.tensor));
   }
   return o;
 }
-function FB(e, t) {
+function GB(e, t) {
   let n = En.decodeWeights(e, t), s = {}, r = [];
   return t.forEach((a) => {
     a.group === "optimizer" ? r.push({ name: a.name, tensor: n[a.name] }) : s[a.name] = n[a.name];
   }), { modelWeights: s, optimizerWeights: r };
 }
-var Cm = class extends pr {
+var Tm = class extends hr {
   constructor(e) {
-    if (super({ inputs: [], outputs: [] }), e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Dp("sequential_"), e.layers != null)
+    if (super({ inputs: [], outputs: [] }), e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Np("sequential_"), e.layers != null)
       for (let t of e.layers)
         this.add(t);
   }
@@ -13648,7 +13706,7 @@ var Cm = class extends pr {
       throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`);
   }
   add(e) {
-    let t = e instanceof Cm || e instanceof pr, n;
+    let t = e instanceof Tm || e instanceof hr, n;
     if (t) {
       if (n = e, n.outputs.length !== 1)
         throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
@@ -13659,7 +13717,7 @@ var Cm = class extends pr {
       if (e.inboundNodes.length === 0) {
         if (e.batchInputShape == null)
           throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
-        let s = ZI({ batchShape: e.batchInputShape, dtype: e.dtype, name: e.name + "_input" });
+        let s = tS({ batchShape: e.batchInputShape, dtype: e.dtype, name: e.name + "_input" });
         e.apply(s);
       }
       if (t)
@@ -13669,9 +13727,9 @@ var Cm = class extends pr {
           throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);
         if (e.inboundNodes[0].outputTensors.length !== 1)
           throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
-        this.checkShape(e), this.outputs = [e.inboundNodes[0].outputTensors[0]], this.inputs = QI(this.outputs[0]);
+        this.checkShape(e), this.outputs = [e.inboundNodes[0].outputTensors[0]], this.inputs = eS(this.outputs[0]);
       }
-      this.inboundNodes = [], new Fp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: fa(null, this.inputs.length), outputMasks: [null], inputShapes: this.inputs.map((s) => s.shape), outputShapes: this.outputs[0].shape });
+      this.inboundNodes = [], new Pp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: ma(null, this.inputs.length), outputMasks: [null], inputShapes: this.inputs.map((s) => s.shape), outputShapes: this.outputs[0].shape });
     } else {
       let s = e.apply(this.outputs[0]);
       if (Array.isArray(s))
@@ -13696,7 +13754,7 @@ var Cm = class extends pr {
   build(e) {
     if (nt(e), this.inputs.length === 0 || this.outputs.length === 0)
       throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");
-    this.model = new pr({ inputs: this.inputs, outputs: this.outputs[0], name: this.name + "_model" }), this.model.trainable = this.trainable, this.supportsMasking = this.model.supportsMasking, this.inputLayers = this.model.inputLayers, this.inputLayersNodeIndices = this.model.inputLayersNodeIndices, this.inputLayersTensorIndices = this.model.inputLayersTensorIndices, this.outputLayers = this.model.outputLayers, this.outputLayersNodeIndices = this.model.outputLayersNodeIndices, this.outputLayersTensorIndices = this.model.outputLayersTensorIndices, this.nodesByDepth = this.model.nodesByDepth, this.containerNodes = this.model.containerNodes, this.outputNames = this.model.outputNames, this.inputNames = this.model.inputNames, this.built = true;
+    this.model = new hr({ inputs: this.inputs, outputs: this.outputs[0], name: this.name + "_model" }), this.model.trainable = this.trainable, this.supportsMasking = this.model.supportsMasking, this.inputLayers = this.model.inputLayers, this.inputLayersNodeIndices = this.model.inputLayersNodeIndices, this.inputLayersTensorIndices = this.model.inputLayersTensorIndices, this.outputLayers = this.model.outputLayers, this.outputLayersNodeIndices = this.model.outputLayersNodeIndices, this.outputLayersTensorIndices = this.model.outputLayersTensorIndices, this.nodesByDepth = this.model.nodesByDepth, this.containerNodes = this.model.containerNodes, this.outputNames = this.model.outputNames, this.inputNames = this.model.inputNames, this.built = true;
   }
   countParams() {
     return this.built || this.build(), super.countParams();
@@ -13754,7 +13812,7 @@ var Cm = class extends pr {
     } else
       w.assert(t.layers != null, () => "When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."), r = t.layers, delete t.layers, a = t;
     let i = new e(a);
-    if (!(i instanceof Cm))
+    if (!(i instanceof Tm))
       throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);
     for (let o of r) {
       let l = ms(o, void 0, s);
@@ -13781,158 +13839,158 @@ var Cm = class extends pr {
     return { name: this.name, layers: e };
   }
 };
-var Ub = Cm;
-Ub.className = "Sequential";
-ae.registerClass(Ub);
-function Npe(e) {
-  return new pr(e);
+var Hb = Tm;
+Hb.className = "Sequential";
+ae.registerClass(Hb);
+function Bpe(e) {
+  return new hr(e);
 }
-function Tpe(e) {
-  return new Ub(e);
+function Vpe(e) {
+  return new Hb(e);
 }
-function $pe(e, t) {
-  return t == null && (t = {}), RB(e, t);
+function Wpe(e, t) {
+  return t == null && (t = {}), WB(e, t);
 }
-function OB(e) {
-  return ZI(e);
+function HB(e) {
+  return tS(e);
 }
-function _pe(e, t) {
-  zb.registerCallbackConstructor(e, t);
+function Upe(e, t) {
+  Lb.registerCallbackConstructor(e, t);
 }
 var In = class extends ae.Serializable {
   getConfig() {
     return {};
   }
 };
-var fS = class extends In {
+var vS = class extends In {
   apply(e, t = 1) {
-    return aL(e, t);
+    return rz(e, t);
   }
 };
-fS.className = "elu";
-ae.registerClass(fS);
-var mS = class extends In {
+vS.className = "elu";
+ae.registerClass(vS);
+var xS = class extends In {
   apply(e) {
-    return gI(e);
+    return xI(e);
   }
 };
-mS.className = "selu";
-ae.registerClass(mS);
-var gS = class extends In {
+xS.className = "selu";
+ae.registerClass(xS);
+var wS = class extends In {
   apply(e) {
-    return Xs(e);
+    return Ys(e);
   }
 };
-gS.className = "relu";
-ae.registerClass(gS);
-var bS = class extends In {
+wS.className = "relu";
+ae.registerClass(wS);
+var kS = class extends In {
   apply(e) {
-    return j(() => bp(6, Xs(e)));
+    return j(() => vp(6, Ys(e)));
   }
 };
-bS.className = "relu6";
-ae.registerClass(bS);
-var yS = class extends In {
+kS.className = "relu6";
+ae.registerClass(kS);
+var IS = class extends In {
   apply(e) {
     return e;
   }
 };
-yS.className = "linear";
-ae.registerClass(yS);
-var vS = class extends In {
+IS.className = "linear";
+ae.registerClass(IS);
+var SS = class extends In {
   apply(e) {
     return qs(e);
   }
 };
-vS.className = "sigmoid";
-ae.registerClass(vS);
-var xS = class extends In {
+SS.className = "sigmoid";
+ae.registerClass(SS);
+var CS = class extends In {
   apply(e) {
-    return oL(e);
+    return iz(e);
   }
 };
-xS.className = "hardSigmoid";
-ae.registerClass(xS);
-var wS = class extends In {
+CS.className = "hardSigmoid";
+ae.registerClass(CS);
+var NS = class extends In {
   apply(e) {
     return zl(e);
   }
 };
-wS.className = "softplus";
-ae.registerClass(wS);
-var kS = class extends In {
+NS.className = "softplus";
+ae.registerClass(NS);
+var TS = class extends In {
   apply(e) {
-    return iL(e);
+    return az(e);
   }
 };
-kS.className = "softsign";
-ae.registerClass(kS);
-var IS = class extends In {
+TS.className = "softsign";
+ae.registerClass(TS);
+var $S = class extends In {
   apply(e) {
-    return qu(e);
+    return ju(e);
   }
 };
-IS.className = "tanh";
-ae.registerClass(IS);
-var Gb = class extends In {
+$S.className = "tanh";
+ae.registerClass($S);
+var qb = class extends In {
   apply(e, t = -1) {
-    return ub(e, t);
+    return cb(e, t);
   }
 };
-Gb.className = "softmax";
-ae.registerClass(Gb);
-var SS = class extends In {
+qb.className = "softmax";
+ae.registerClass(qb);
+var _S = class extends In {
   apply(e, t = -1) {
-    return iI(e, t);
+    return cI(e, t);
   }
 };
-SS.className = "logSoftmax";
-ae.registerClass(SS);
-var CS = class extends In {
+_S.className = "logSoftmax";
+ae.registerClass(_S);
+var AS = class extends In {
   apply(e, t = 1) {
     return j(() => V(qs(V(e, t)), e));
   }
 };
-CS.className = "swish";
-ae.registerClass(CS);
-var NS = class extends In {
+AS.className = "swish";
+ae.registerClass(AS);
+var ES = class extends In {
   apply(e) {
-    return j(() => V(e, qu(zl(e))));
+    return j(() => V(e, ju(zl(e))));
   }
 };
-NS.className = "mish";
-ae.registerClass(NS);
-function br(e) {
+ES.className = "mish";
+ae.registerClass(ES);
+function yr(e) {
   return e.getClassName();
 }
-function Gf(e, t = {}) {
+function Hf(e, t = {}) {
   return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "activation");
 }
-function yr(e) {
+function vr(e) {
   if (e == null) {
     let t = {};
-    return t.className = "linear", t.config = {}, Gf(t);
+    return t.className = "linear", t.config = {}, Hf(t);
   }
   if (typeof e == "string") {
     let t = {};
-    return t.className = e, t.config = {}, Gf(t);
+    return t.className = e, t.config = {}, Hf(t);
   } else
-    return e instanceof In ? e : Gf(e);
+    return e instanceof In ? e : Hf(e);
 }
-function Hb(e) {
+function jb(e) {
   if (e != null && typeof e != "object")
     throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`);
 }
-var TS = class extends ae.Serializable {
+var RS = class extends ae.Serializable {
 };
-var Gl = class extends TS {
+var Gl = class extends RS {
   constructor(e) {
-    super(), Hb(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
+    super(), jb(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
   }
   apply(e) {
     return j(() => {
       let t = $t([1]);
-      return this.hasL1 && (t = ie(t, ve(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ve(V(this.l2, Wl(e))))), G(t, []);
+      return this.hasL1 && (t = ie(t, ve(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ve(V(this.l2, Vl(e))))), G(t, []);
     });
   }
   getConfig() {
@@ -13944,35 +14002,35 @@ var Gl = class extends TS {
 };
 Gl.className = "L1L2";
 ae.registerClass(Gl);
-function PB(e) {
-  return Hb(e), new Gl({ l1: e != null ? e.l1 : null, l2: 0 });
+function qB(e) {
+  return jb(e), new Gl({ l1: e != null ? e.l1 : null, l2: 0 });
 }
-function zB(e) {
-  return Hb(e), new Gl({ l2: e != null ? e.l2 : null, l1: 0 });
+function jB(e) {
+  return jb(e), new Gl({ l2: e != null ? e.l2 : null, l1: 0 });
 }
-var Vx = { l1l2: "L1L2" };
+var Wx = { l1l2: "L1L2" };
 function it(e) {
-  return wb(e);
+  return Ib(e);
 }
-function Wx(e, t = {}) {
+function Ux(e, t = {}) {
   return Ll(e, ae.SerializationMap.getMap().classNameMap, t, "regularizer");
 }
 function ft(e) {
   if (e == null)
     return null;
   if (typeof e == "string") {
-    let n = { className: e in Vx ? Vx[e] : e, config: {} };
-    return Wx(n);
+    let n = { className: e in Wx ? Wx[e] : e, config: {} };
+    return Ux(n);
   } else
-    return e instanceof TS ? e : Wx(e);
+    return e instanceof RS ? e : Ux(e);
 }
-var qb = class extends Ge {
+var Kb = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.supportsMasking = true, e != null && (this.maxValue = e.maxValue);
   }
   call(e, t) {
     e = Oe(e);
-    let n = Xs(e);
+    let n = Ys(e);
     return this.maxValue != null && (n = Vn(n, 0, this.maxValue)), n;
   }
   computeOutputShape(e) {
@@ -13983,15 +14041,15 @@ var qb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-qb.className = "ReLU";
-ae.registerClass(qb);
-var jb = class extends Ge {
+Kb.className = "ReLU";
+ae.registerClass(Kb);
+var Xb = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.DEFAULT_ALPHA = 0.3, e == null && (e = {}), this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
   }
   call(e, t) {
     let n = Oe(e);
-    return Xg(n, this.alpha);
+    return Qg(n, this.alpha);
   }
   computeOutputShape(e) {
     return e;
@@ -14001,9 +14059,9 @@ var jb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-jb.className = "LeakyReLU";
-ae.registerClass(jb);
-var Kb = class extends Ge {
+Xb.className = "LeakyReLU";
+ae.registerClass(Xb);
+var Yb = class extends He {
   constructor(e) {
     if (super(e == null ? {} : e), this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ht(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = ft(e.alphaRegularizer), this.alphaConstraint = Pt(e.alphaConstraint), e.sharedAxes == null)
       this.sharedAxes = null;
@@ -14028,16 +14086,16 @@ var Kb = class extends Ge {
     this.inputSpec = [new Dt({ ndim: e.length, axes: n })], this.built = true;
   }
   call(e, t) {
-    return e = Oe(e), sb(e, this.alpha.read());
+    return e = Oe(e), ab(e, this.alpha.read());
   }
   getConfig() {
     let e = { alphaInitializer: yt(this.alphaInitializer), alphaRegularizer: it(this.alphaRegularizer), alphaConstraint: Ot(this.alphaConstraint), sharedAxes: this.sharedAxes }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-Kb.className = "PReLU";
-ae.registerClass(Kb);
-var Xb = class extends Ge {
+Yb.className = "PReLU";
+ae.registerClass(Yb);
+var Qb = class extends He {
   constructor(e) {
     if (super(e == null ? {} : e), this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
       throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);
@@ -14045,7 +14103,7 @@ var Xb = class extends Ge {
   }
   call(e, t) {
     let n = Oe(e);
-    return fp(n);
+    return gp(n);
   }
   computeOutputShape(e) {
     return e;
@@ -14055,15 +14113,15 @@ var Xb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Xb.className = "ELU";
-ae.registerClass(Xb);
-var Yb = class extends Ge {
+Qb.className = "ELU";
+ae.registerClass(Qb);
+var Zb = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.DEFAULT_THETA = 1, e == null && (e = {}), this.theta = e.theta == null ? this.DEFAULT_THETA : e.theta;
   }
   call(e, t) {
     let n = Oe(e);
-    return V(n, ce(Wn(n, this.theta), "float32"));
+    return V(n, ce(Un(n, this.theta), "float32"));
   }
   computeOutputShape(e) {
     return e;
@@ -14073,11 +14131,11 @@ var Yb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Yb.className = "ThresholdedReLU";
-ae.registerClass(Yb);
-var Qb = class extends Ge {
+Zb.className = "ThresholdedReLU";
+ae.registerClass(Zb);
+var Jb = class extends He {
   constructor(e) {
-    super(e == null ? {} : e), this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Gb().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
+    super(e == null ? {} : e), this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new qb().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
   }
   call(e, t) {
     let n = Oe(e);
@@ -14091,16 +14149,16 @@ var Qb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Qb.className = "Softmax";
-ae.registerClass(Qb);
-function Xi(e, t, n) {
+Jb.className = "Softmax";
+ae.registerClass(Jb);
+function Qi(e, t, n) {
   if (typeof e == "number")
-    return fa(e, t);
+    return ma(e, t);
   if (e.length !== t)
     throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);
   for (let s = 0; s < t; ++s) {
     let r = e[s];
-    if (!tL(r))
+    if (!ez(r))
       throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`);
   }
   return e;
@@ -14115,62 +14173,62 @@ function Ns(e, t, n, s) {
   if (e == null)
     return null;
   if (s === "valid")
-    e = e * t + gr([n - t, 0]);
+    e = e * t + br([n - t, 0]);
   else if (s === "same")
     e = e * t;
   else
     throw new U(`Unsupport padding mode: ${s}.`);
   return e;
 }
-function Zb(e, t) {
-  return j(() => (Ct(t), t === "channelsFirst" ? qe(e, [0, 2, 3, 1]) : e));
+function ey(e, t) {
+  return j(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 1]) : e));
 }
-function $S(e, t) {
-  return j(() => (Ct(t), t === "channelsFirst" ? qe(e, [0, 2, 3, 4, 1]) : e));
+function DS(e, t) {
+  return j(() => (Ct(t), t === "channelsFirst" ? Ge(e, [0, 2, 3, 4, 1]) : e));
 }
-function MB(e, t, n, s = 1, r = "valid", a, i = 1) {
+function KB(e, t, n, s = 1, r = "valid", a, i = 1) {
   return j(() => {
-    if (a == null && (a = bs()), Ct(a), e.shape.length !== 3)
+    if (a == null && (a = ys()), Ct(a), e.shape.length !== 3)
       throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);
     if (t.shape.length !== 3)
       throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);
     if (n != null && n.shape.length !== 1)
       throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);
-    if (a === "channelsFirst" && (e = qe(e, [0, 2, 1])), r === "causal")
+    if (a === "channelsFirst" && (e = Ge(e, [0, 2, 1])), r === "causal")
       throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
-    let o = Zk(e, t, s, r === "same" ? "same" : "valid", "NWC", i);
+    let o = nI(e, t, s, r === "same" ? "same" : "valid", "NWC", i);
     return n != null && (o = ws(o, n)), o;
   });
 }
-function Ux(e, t, n, s = [1, 1], r = "valid", a, i, o = null) {
+function Gx(e, t, n, s = [1, 1], r = "valid", a, i, o = null) {
   return j(() => {
-    if (a == null && (a = bs()), Ct(a), e.rank !== 3 && e.rank !== 4)
+    if (a == null && (a = ys()), Ct(a), e.rank !== 3 && e.rank !== 4)
       throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);
     if (t.rank !== 3 && t.rank !== 4)
       throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);
-    let u = Zb(e, a);
+    let u = ey(e, a);
     if (r === "causal")
       throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
-    return u = ha.conv2d({ x: u, filter: t, strides: s, pad: r === "same" ? "same" : "valid", dilations: i, dataFormat: "NHWC", bias: n, activation: o }), a === "channelsFirst" && (u = qe(u, [0, 3, 1, 2])), u;
+    return u = fa.conv2d({ x: u, filter: t, strides: s, pad: r === "same" ? "same" : "valid", dilations: i, dataFormat: "NHWC", bias: n, activation: o }), a === "channelsFirst" && (u = Ge(u, [0, 3, 1, 2])), u;
   });
 }
-function LB(e, t, n, s = [1, 1, 1], r = "valid", a, i) {
+function XB(e, t, n, s = [1, 1, 1], r = "valid", a, i) {
   return j(() => {
-    if (a == null && (a = bs()), Ct(a), e.rank !== 4 && e.rank !== 5)
+    if (a == null && (a = ys()), Ct(a), e.rank !== 4 && e.rank !== 5)
       throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);
     if (t.rank !== 4 && t.rank !== 5)
       throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);
-    let o = $S(e, a);
+    let o = DS(e, a);
     if (r === "causal")
       throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
-    return o = eI(o, t, s, r === "same" ? "same" : "valid", "NDHWC", i), n != null && (o = ws(o, n)), a === "channelsFirst" && (o = qe(o, [0, 4, 1, 2, 3])), o;
+    return o = rI(o, t, s, r === "same" ? "same" : "valid", "NDHWC", i), n != null && (o = ws(o, n)), a === "channelsFirst" && (o = Ge(o, [0, 4, 1, 2, 3])), o;
   });
 }
-var Jb = class extends Ge {
+var ty = class extends He {
   constructor(e, t) {
-    if (super(t), this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", Jb.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
+    if (super(t), this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", ty.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
       throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
-    if (this.kernelSize = Xi(t.kernelSize, e, "kernelSize"), this.strides = Xi(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Un(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, Ct(this.dataFormat), this.activation = yr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ht(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = ft(t.biasRegularizer), this.activityRegularizer = ft(t.activityRegularizer), this.dilationRate = Xi(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
+    if (this.kernelSize = Qi(t.kernelSize, e, "kernelSize"), this.strides = Qi(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Gn(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, Ct(this.dataFormat), this.activation = vr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ht(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = ft(t.biasRegularizer), this.activityRegularizer = ft(t.activityRegularizer), this.dilationRate = Qi(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
       throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
     if (this.rank === 2) {
       if (typeof this.dilationRate == "number")
@@ -14185,15 +14243,15 @@ var Jb = class extends Ge {
     }
   }
   static verifyArgs(e) {
-    if (Cs("kernelSize" in e, "required key 'kernelSize' not in config"), typeof e.kernelSize != "number" && !kb(e.kernelSize, "number", 1, 3))
+    if (Cs("kernelSize" in e, "required key 'kernelSize' not in config"), typeof e.kernelSize != "number" && !Sb(e.kernelSize, "number", 1, 3))
       throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`);
   }
   getConfig() {
-    let e = { kernelSize: this.kernelSize, strides: this.strides, padding: this.padding, dataFormat: this.dataFormat, dilationRate: this.dilationRate, activation: br(this.activation), useBias: this.useBias, biasInitializer: yt(this.biasInitializer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
+    let e = { kernelSize: this.kernelSize, strides: this.strides, padding: this.padding, dataFormat: this.dataFormat, dilationRate: this.dilationRate, activation: yr(this.activation), useBias: this.useBias, biasInitializer: yt(this.biasInitializer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-var Hl = class extends Jb {
+var Hl = class extends ty {
   constructor(e, t) {
     super(e, t), this.kernel = null, Hl.verifyArgs(t), this.filters = t.filters, Bt(this.filters, "filters"), this.kernelInitializer = ht(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = ft(t.kernelRegularizer);
   }
@@ -14208,16 +14266,16 @@ var Hl = class extends Jb {
   call(e, t) {
     return j(() => {
       e = Oe(e);
-      let n, s = this.bias == null ? null : this.bias.read(), r = UI(this.activation.getClassName());
+      let n, s = this.bias == null ? null : this.bias.read(), r = qI(this.activation.getClassName());
       if (r != null && this.rank === 2)
-        n = Ux(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
+        n = Gx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
       else {
         if (this.rank === 1)
-          n = MB(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
+          n = KB(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
         else if (this.rank === 2)
-          n = Ux(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
+          n = Gx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
         else if (this.rank === 3)
-          n = LB(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
+          n = XB(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
         else
           throw new Fe("convolutions greater than 3D are not implemented yet.");
         this.activation != null && (n = this.activation.apply(n));
@@ -14244,25 +14302,25 @@ var Hl = class extends Jb {
       throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`);
   }
 };
-var _S = class extends Hl {
+var FS = class extends Hl {
   constructor(e) {
-    super(2, e), _S.verifyArgs(e);
+    super(2, e), FS.verifyArgs(e);
   }
   getConfig() {
     let e = super.getConfig();
     return delete e.rank, e;
   }
   static verifyArgs(e) {
-    if (typeof e.kernelSize != "number" && !kb(e.kernelSize, "number", 1, 2))
+    if (typeof e.kernelSize != "number" && !Sb(e.kernelSize, "number", 1, 2))
       throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var zp = _S;
-zp.className = "Conv2D";
-ae.registerClass(zp);
-var AS = class extends Hl {
+var Lp = FS;
+Lp.className = "Conv2D";
+ae.registerClass(Lp);
+var OS = class extends Hl {
   constructor(e) {
-    super(3, e), AS.verifyArgs(e);
+    super(3, e), OS.verifyArgs(e);
   }
   getConfig() {
     let e = super.getConfig();
@@ -14273,10 +14331,10 @@ var AS = class extends Hl {
       throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var Mp = AS;
-Mp.className = "Conv3D";
-ae.registerClass(Mp);
-var ey = class extends zp {
+var Bp = OS;
+Bp.className = "Conv3D";
+ae.registerClass(Bp);
+var ny = class extends Lp {
   constructor(e) {
     if (super(e), this.inputSpec = [new Dt({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
       throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
@@ -14298,9 +14356,9 @@ var ey = class extends zp {
       let s = n.shape, r = s[0], a, i;
       this.dataFormat === "channelsFirst" ? (a = 2, i = 3) : (a = 1, i = 2);
       let o = s[a], u = s[i], l = this.kernelSize[0], c = this.kernelSize[1], p = this.strides[0], d = this.strides[1], h = Ns(o, p, l, this.padding), f = Ns(u, d, c, this.padding), m = [r, h, f, this.filters];
-      this.dataFormat !== "channelsLast" && (n = qe(n, [0, 2, 3, 1]));
-      let g = Jk(n, this.kernel.read(), m, this.strides, this.padding);
-      return this.dataFormat !== "channelsLast" && (g = qe(g, [0, 3, 1, 2])), this.bias != null && (g = ws(g, this.bias.read(), this.dataFormat)), this.activation != null && (g = this.activation.apply(g)), g;
+      this.dataFormat !== "channelsLast" && (n = Ge(n, [0, 2, 3, 1]));
+      let g = sI(n, this.kernel.read(), m, this.strides, this.padding);
+      return this.dataFormat !== "channelsLast" && (g = Ge(g, [0, 3, 1, 2])), this.bias != null && (g = ws(g, this.bias.read(), this.dataFormat)), this.activation != null && (g = this.activation.apply(g)), g;
     });
   }
   computeOutputShape(e) {
@@ -14315,9 +14373,9 @@ var ey = class extends zp {
     return delete e.dilationRate, e;
   }
 };
-ey.className = "Conv2DTranspose";
-ae.registerClass(ey);
-var ty = class extends Mp {
+ny.className = "Conv2DTranspose";
+ae.registerClass(ny);
+var sy = class extends Bp {
   constructor(e) {
     if (super(e), this.inputSpec = [new Dt({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
       throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
@@ -14339,9 +14397,9 @@ var ty = class extends Mp {
       let s = n.shape, r = s[0], a, i, o;
       this.dataFormat === "channelsFirst" ? (o = 2, a = 3, i = 4) : (o = 1, a = 2, i = 3);
       let u = s[o], l = s[a], c = s[i], p = this.kernelSize[0], d = this.kernelSize[1], h = this.kernelSize[2], f = this.strides[0], m = this.strides[1], g = this.strides[2], b = Ns(u, f, p, this.padding), y = Ns(l, m, d, this.padding), v = Ns(c, g, h, this.padding), x = [r, b, y, v, this.filters];
-      this.dataFormat !== "channelsLast" && (n = qe(n, [0, 2, 3, 4, 1]));
-      let k = tR(n, this.kernel.read(), x, this.strides, this.padding);
-      return this.dataFormat !== "channelsLast" && (k = qe(k, [0, 4, 1, 2, 3])), this.bias !== null && (k = ws(k, this.bias.read(), this.dataFormat)), this.activation !== null && (k = this.activation.apply(k)), k;
+      this.dataFormat !== "channelsLast" && (n = Ge(n, [0, 2, 3, 4, 1]));
+      let k = oR(n, this.kernel.read(), x, this.strides, this.padding);
+      return this.dataFormat !== "channelsLast" && (k = Ge(k, [0, 4, 1, 2, 3])), this.bias !== null && (k = ws(k, this.bias.read(), this.dataFormat)), this.activation !== null && (k = this.activation.apply(k)), k;
     });
   }
   computeOutputShape(e) {
@@ -14356,9 +14414,9 @@ var ty = class extends Mp {
     return delete e.dilationRate, e;
   }
 };
-ty.className = "Conv3DTranspose";
-ae.registerClass(ty);
-var ES = class extends Hl {
+sy.className = "Conv3DTranspose";
+ae.registerClass(sy);
+var PS = class extends Hl {
   constructor(e, t) {
     if (super(e, t), this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", this.depthwiseKernel = null, this.pointwiseKernel = null, t.filters == null)
       throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");
@@ -14387,7 +14445,7 @@ var ES = class extends Hl {
       let n;
       if (this.rank === 1)
         throw new Fe("1D separable convolution is not implemented yet.");
-      return this.rank === 2 && (this.dataFormat === "channelsFirst" && (e = qe(e, [0, 2, 3, 1])), n = c3(e, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC")), this.useBias && (n = ws(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), this.dataFormat === "channelsFirst" && (n = qe(n, [0, 3, 1, 2])), n;
+      return this.rank === 2 && (this.dataFormat === "channelsFirst" && (e = Ge(e, [0, 2, 3, 1])), n = m3(e, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC")), this.useBias && (n = ws(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), this.dataFormat === "channelsFirst" && (n = Ge(n, [0, 3, 1, 2])), n;
     });
   }
   getConfig() {
@@ -14395,31 +14453,31 @@ var ES = class extends Hl {
     return delete e.rank, delete e.kernelInitializer, delete e.kernelRegularizer, delete e.kernelConstraint, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.pointwiseInitializer = yt(this.pointwiseInitializer), e.depthwiseRegularizer = it(this.depthwiseRegularizer), e.pointwiseRegularizer = it(this.pointwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseConstraint), e.pointwiseConstraint = Ot(this.pointwiseConstraint), e;
   }
 };
-ES.className = "SeparableConv";
-var ny = class extends ES {
+PS.className = "SeparableConv";
+var ry = class extends PS {
   constructor(e) {
     super(2, e);
   }
 };
-ny.className = "SeparableConv2D";
-ae.registerClass(ny);
-var RS = class extends Hl {
+ry.className = "SeparableConv2D";
+ae.registerClass(ry);
+var zS = class extends Hl {
   constructor(e) {
-    super(1, e), RS.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
+    super(1, e), zS.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
   }
   getConfig() {
     let e = super.getConfig();
     return delete e.rank, delete e.dataFormat, e;
   }
   static verifyArgs(e) {
-    if (typeof e.kernelSize != "number" && !kb(e.kernelSize, "number", 1, 1))
+    if (typeof e.kernelSize != "number" && !Sb(e.kernelSize, "number", 1, 1))
       throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var sy = RS;
-sy.className = "Conv1D";
-ae.registerClass(sy);
-var ry = class extends Ge {
+var ay = zS;
+ay.className = "Conv1D";
+ae.registerClass(ay);
+var iy = class extends He {
   constructor(e) {
     super(e), typeof e.cropping == "number" ? this.cropping = [[e.cropping, e.cropping], [e.cropping, e.cropping]] : typeof e.cropping[0] == "number" ? this.cropping = [[e.cropping[0], e.cropping[0]], [e.cropping[1], e.cropping[1]]] : this.cropping = e.cropping, this.dataFormat = e.dataFormat === void 0 ? "channelsLast" : e.dataFormat, this.inputSpec = [{ ndim: 4 }];
   }
@@ -14429,11 +14487,11 @@ var ry = class extends Ge {
   call(e, t) {
     return j(() => {
       if (e = Oe(e), this.dataFormat === "channelsLast") {
-        let n = Gc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
-        return Gc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
+        let n = Hc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
+        return Hc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
       } else {
-        let n = Gc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
-        return Gc(n, this.cropping[1][0], e.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
+        let n = Hc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
+        return Hc(n, this.cropping[1][0], e.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
       }
     });
   }
@@ -14442,11 +14500,11 @@ var ry = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-ry.className = "Cropping2D";
-ae.registerClass(ry);
-var ay = class extends Ge {
+iy.className = "Cropping2D";
+ae.registerClass(iy);
+var oy = class extends He {
   constructor(e) {
-    super(e), this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, ZM(this.interpolation);
+    super(e), this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, QP(this.interpolation);
   }
   computeOutputShape(e) {
     if (this.dataFormat === "channelsFirst") {
@@ -14461,12 +14519,12 @@ var ay = class extends Ge {
     return j(() => {
       let n = Oe(e), s = n.shape;
       if (this.dataFormat === "channelsFirst") {
-        n = qe(n, [0, 2, 3, 1]);
-        let r = this.size[0] * s[2], a = this.size[1] * s[3], i = this.interpolation === "nearest" ? qn.resizeNearestNeighbor(n, [r, a]) : qn.resizeBilinear(n, [r, a]);
-        return qe(i, [0, 3, 1, 2]);
+        n = Ge(n, [0, 2, 3, 1]);
+        let r = this.size[0] * s[2], a = this.size[1] * s[3], i = this.interpolation === "nearest" ? jn.resizeNearestNeighbor(n, [r, a]) : jn.resizeBilinear(n, [r, a]);
+        return Ge(i, [0, 3, 1, 2]);
       } else {
         let r = this.size[0] * s[1], a = this.size[1] * s[2];
-        return this.interpolation === "nearest" ? qn.resizeNearestNeighbor(n, [r, a]) : qn.resizeBilinear(n, [r, a]);
+        return this.interpolation === "nearest" ? jn.resizeNearestNeighbor(n, [r, a]) : jn.resizeBilinear(n, [r, a]);
       }
     });
   }
@@ -14475,20 +14533,20 @@ var ay = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-ay.className = "UpSampling2D";
-ae.registerClass(ay);
-function BB(e, t, n = [1, 1], s = "valid", r, a) {
+oy.className = "UpSampling2D";
+ae.registerClass(oy);
+function YB(e, t, n = [1, 1], s = "valid", r, a) {
   return j(() => {
-    r == null && (r = bs()), Ct(r);
-    let i = Zb(e, r);
+    r == null && (r = ys()), Ct(r);
+    let i = ey(e, r);
     if (e.rank !== 4)
       throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);
     if (t.rank !== 4)
       throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);
-    return i = hp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
+    return i = mp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
   });
 }
-var iy = class extends Jb {
+var uy = class extends ty {
   constructor(e) {
     super(2, e), this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ht(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = Pt(e.depthwiseConstraint), this.depthwiseRegularizer = ft(e.depthwiseRegularizer);
   }
@@ -14504,7 +14562,7 @@ var iy = class extends Jb {
   call(e, t) {
     return j(() => {
       e = Oe(e);
-      let n = BB(e, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
+      let n = YB(e, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
       return this.useBias && (n = ws(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), n;
     });
   }
@@ -14518,9 +14576,9 @@ var iy = class extends Jb {
     return e.depthMultiplier = this.depthMultiplier, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.depthwiseRegularizer = it(this.depthwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseRegularizer), e;
   }
 };
-iy.className = "DepthwiseConv2D";
-ae.registerClass(iy);
-function DS(e, t, n, s) {
+uy.className = "DepthwiseConv2D";
+ae.registerClass(uy);
+function MS(e, t, n, s) {
   if (Array.isArray(e)) {
     if (t != null || n != null)
       throw new U("When inputs is an array, neither initialState or constants should be provided");
@@ -14531,15 +14589,15 @@ function DS(e, t, n, s) {
   }
   return t = r(t), n = r(n), { inputs: e, initialState: t, constants: n };
 }
-function FS(e, t, n, s = false, r, a, i = false, o = false) {
+function LS(e, t, n, s = false, r, a, i = false, o = false) {
   return j(() => {
     let u = t.shape.length;
     if (u < 3)
       throw new U(`Input should be at least 3D, but is ${u}D.`);
-    let l = [1, 0].concat(ys(2, u));
-    if (t = qe(t, l), a != null)
+    let l = [1, 0].concat(bs(2, u));
+    if (t = Ge(t, l), a != null)
       throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
-    i && console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."), r != null && (r = ce(ce(r, "bool"), "float32"), r.rank === u - 1 && (r = Pn(r, -1)), r = qe(r, l)), s && (t = Zn(t, 0), r != null && (r = Zn(r, 0)));
+    i && console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."), r != null && (r = ce(ce(r, "bool"), "float32"), r.rank === u - 1 && (r = Pn(r, -1)), r = Ge(r, l)), s && (t = Jn(t, 0), r != null && (r = Jn(r, 0)));
     let c = [], p, d = n, h = t.shape[0], f = Fs(t), m;
     r != null && (m = Fs(r));
     for (let b = 0; b < h; ++b) {
@@ -14548,7 +14606,7 @@ function FS(e, t, n, s = false, r, a, i = false, o = false) {
         p = v[0], d = v[1];
       else {
         let x = j(() => {
-          let k = m[b], T = ge(Qn(k), k), N = ie(V(v[0], k), V(d[0], T)), E = d.map((A, P) => ie(V(v[1][P], k), V(A, T)));
+          let k = m[b], T = ge(Zn(k), k), N = ie(V(v[0], k), V(d[0], T)), E = d.map((A, P) => ie(V(v[1][P], k), V(A, T)));
           return { output: N, newStates: E };
         });
         p = x.output, d = x.newStates;
@@ -14556,23 +14614,23 @@ function FS(e, t, n, s = false, r, a, i = false, o = false) {
       o && c.push(p);
     }
     let g;
-    return o && (g = Jn(c, 1)), [p, g, d];
+    return o && (g = es(c, 1)), [p, g, d];
   });
 }
-var OS = class extends Ge {
+var BS = class extends He {
   constructor(e) {
     super(e);
     let t;
     if (e.cell == null)
       throw new U("cell property is missing for the constructor of RNN.");
-    if (Array.isArray(e.cell) ? t = new Vp({ cells: e.cell }) : t = e.cell, t.stateSize == null)
+    if (Array.isArray(e.cell) ? t = new Up({ cells: e.cell }) : t = e.cell, t.stateSize == null)
       throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
     this.cell = t, this.returnSequences = e.returnSequences == null ? false : e.returnSequences, this.returnState = e.returnState == null ? false : e.returnState, this.goBackwards = e.goBackwards == null ? false : e.goBackwards, this._stateful = e.stateful == null ? false : e.stateful, this.unroll = e.unroll == null ? false : e.unroll, this.supportsMasking = true, this.inputSpec = [new Dt({ ndim: 3 })], this.stateSpec = null, this.states_ = null, this.numConstants = null, this.keptStates = [];
   }
   getStates() {
     if (this.states_ == null) {
       let e = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
-      return ys(0, e).map((t) => null);
+      return bs(0, e).map((t) => null);
     } else
       return this.states_;
   }
@@ -14580,7 +14638,7 @@ var OS = class extends Ge {
     this.states_ = e;
   }
   computeOutputShape(e) {
-    ym(e) && (e = e[0]), e = e;
+    xm(e) && (e = e[0]), e = e;
     let t = this.cell.stateSize;
     Array.isArray(t) || (t = [t]);
     let n = t[0], s;
@@ -14618,7 +14676,7 @@ var OS = class extends Ge {
   build(e) {
     if (this.numConstants != null)
       throw new Fe("Constants support is not implemented in RNN yet.");
-    ym(e) && (e = e[0]), e = e;
+    xm(e) && (e = e[0]), e = e;
     let n = this.stateful ? e[0] : null, s = e.slice(2);
     this.inputSpec[0] = new Dt({ shape: [n, null, ...s] });
     let r = [e[0]].concat(e.slice(2));
@@ -14659,7 +14717,7 @@ var OS = class extends Ge {
   apply(e, t) {
     let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
     t == null && (t = {});
-    let r = DS(e, n, s, this.numConstants);
+    let r = MS(e, n, s, this.numConstants);
     e = r.inputs, n = r.initialState, s = r.constants;
     let a = [], i = [];
     if (n != null) {
@@ -14684,7 +14742,7 @@ var OS = class extends Ge {
       if (r.length !== a)
         throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);
       this.unroll && console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
-      let i = { training: s }, u = FS((h, f) => {
+      let i = { training: s }, u = LS((h, f) => {
         let m = this.cell.call([h].concat(f), i);
         return [m[0], m.slice(1)];
       }, e, r, this.goBackwards, n, null, this.unroll, this.returnSequences), l = u[0], c = u[1], p = u[2];
@@ -14696,7 +14754,7 @@ var OS = class extends Ge {
   getInitialState(e) {
     return j(() => {
       let t = $t(e.shape);
-      return t = ve(t, [1, 2]), t = Vl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? gm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [gm(t, [1, this.cell.stateSize])] : [t];
+      return t = ve(t, [1, 2]), t = Bl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? ym(t, [1, n]) : t) : this.cell.stateSize > 1 ? [ym(t, [1, this.cell.stateSize])] : [t];
     });
   }
   get trainableWeights() {
@@ -14712,21 +14770,21 @@ var OS = class extends Ge {
     let e = super.getConfig(), t = { returnSequences: this.returnSequences, returnState: this.returnState, goBackwards: this.goBackwards, stateful: this.stateful, unroll: this.unroll };
     this.numConstants != null && (t.numConstants = this.numConstants);
     let n = this.cell.getConfig();
-    return this.getClassName() === OS.className && (t.cell = { className: this.cell.getClassName(), config: n }), { ...n, ...e, ...t };
+    return this.getClassName() === BS.className && (t.cell = { className: this.cell.getClassName(), config: n }), { ...n, ...e, ...t };
   }
   static fromConfig(e, t, n = {}) {
     let s = t.cell, r = ms(s, n);
     return new e(Object.assign(t, { cell: r }));
   }
 };
-var Ar = OS;
-Ar.className = "RNN";
-ae.registerClass(Ar);
-var ql = class extends Ge {
+var Er = BS;
+Er.className = "RNN";
+ae.registerClass(Er);
+var ql = class extends He {
 };
-var Lp = class extends ql {
+var Vp = class extends ql {
   constructor(e) {
-    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
+    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = eo([1, br([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = eo([1, br([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     e = nt(e), this.kernel = this.addWeight("kernel", [e[e.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
@@ -14738,7 +14796,7 @@ var Lp = class extends ql {
       let n = e[1];
       e = e[0];
       let s = t.training == null ? false : t.training;
-      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Qn(e), rate: this.dropout, training: s, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Qn(n), rate: this.recurrentDropout, training: s, dropoutFunc: this.dropoutFunc }));
+      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(e), rate: this.dropout, training: s, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(n), rate: this.recurrentDropout, training: s, dropoutFunc: this.dropoutFunc }));
       let r, a = this.dropoutMask, i = this.recurrentDropoutMask;
       a != null ? r = Es(V(e, a), this.kernel.read()) : r = Es(e, this.kernel.read()), this.bias != null && (r = ws(r, this.bias.read())), i != null && (n = V(n, i));
       let o = ie(r, Es(n, this.recurrentKernel.read()));
@@ -14746,15 +14804,15 @@ var Lp = class extends ql {
     });
   }
   getConfig() {
-    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout };
+    let e = super.getConfig(), t = { units: this.units, activation: yr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout };
     return { ...e, ...t };
   }
 };
-Lp.className = "SimpleRNNCell";
-ae.registerClass(Lp);
-var oy = class extends Ar {
+Vp.className = "SimpleRNNCell";
+ae.registerClass(Vp);
+var ly = class extends Er {
   constructor(e) {
-    e.cell = new Lp(e), super(e);
+    e.cell = new Vp(e), super(e);
   }
   call(e, t) {
     return j(() => {
@@ -14767,13 +14825,13 @@ var oy = class extends Ar {
     return new e(t);
   }
 };
-oy.className = "SimpleRNN";
-ae.registerClass(oy);
-var Bp = class extends ql {
+ly.className = "SimpleRNN";
+ae.registerClass(ly);
+var Wp = class extends ql {
   constructor(e) {
     if (super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.resetAfter)
       throw new U("GRUCell does not support reset_after parameter set to true.");
-    this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
+    this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = vr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = eo([1, br([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = eo([1, br([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     e = nt(e);
@@ -14785,7 +14843,7 @@ var Bp = class extends ql {
       if (e = e, e.length !== 2)
         throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);
       let n = t.training == null ? false : t.training, s = e[1];
-      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Qn(e), rate: this.dropout, training: n, count: 3, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Qn(s), rate: this.recurrentDropout, training: n, count: 3, dropoutFunc: this.dropoutFunc }));
+      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 3, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 3, dropoutFunc: this.dropoutFunc }));
       let r = this.dropoutMask, a = this.recurrentDropoutMask, i, o, u;
       0 < this.dropout && this.dropout < 1 && (e = V(e, r[0]));
       let l = Es(e, this.kernel.read());
@@ -14799,15 +14857,15 @@ var Bp = class extends ql {
     });
   }
   getConfig() {
-    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), recurrentActivation: br(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation, resetAfter: false };
+    let e = super.getConfig(), t = { units: this.units, activation: yr(this.activation), recurrentActivation: yr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation, resetAfter: false };
     return { ...e, ...t };
   }
 };
-Bp.className = "GRUCell";
-ae.registerClass(Bp);
-var uy = class extends Ar {
+Wp.className = "GRUCell";
+ae.registerClass(Wp);
+var cy = class extends Er {
   constructor(e) {
-    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Bp(e), super(e);
+    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Wp(e), super(e);
   }
   call(e, t) {
     return j(() => {
@@ -14820,11 +14878,11 @@ var uy = class extends Ar {
     return t.implmentation === 0 && (t.implementation = 1), new e(t);
   }
 };
-uy.className = "GRU";
-ae.registerClass(uy);
+cy.className = "GRU";
+ae.registerClass(cy);
 var jl = class extends ql {
   constructor(e) {
-    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
+    super(e), this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = vr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = eo([1, br([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = eo([1, br([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     var t;
@@ -14837,8 +14895,8 @@ var jl = class extends ql {
         let r = this.biasInitializer, a = this.units;
         s = new (t = class extends ns {
           apply(i, o) {
-            let u = r.apply([a]), l = new Np().apply([a]), c = r.apply([a * 2]);
-            return $x($x(u, l), c);
+            let u = r.apply([a]), l = new _p().apply([a]), c = r.apply([a * 2]);
+            return kx(kx(u, l), c);
           }
         }, t.className = "CustomInit", t)();
       } else
@@ -14854,7 +14912,7 @@ var jl = class extends ql {
       if (e = e, e.length !== 3)
         throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
       let s = e[1], r = e[2];
-      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Qn(e), rate: this.dropout, training: n, count: 4, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Qn(s), rate: this.recurrentDropout, training: n, count: 4, dropoutFunc: this.dropoutFunc }));
+      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(e), rate: this.dropout, training: n, count: 4, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(s), rate: this.recurrentDropout, training: n, count: 4, dropoutFunc: this.dropoutFunc }));
       let a = this.dropoutMask, i = this.recurrentDropoutMask, o, u, l, c;
       0 < this.dropout && this.dropout < 1 && (e = V(e, a[0]));
       let p = Es(e, this.kernel.read());
@@ -14866,13 +14924,13 @@ var jl = class extends ql {
     });
   }
   getConfig() {
-    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), recurrentActivation: br(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), unitForgetBias: this.unitForgetBias, kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation };
+    let e = super.getConfig(), t = { units: this.units, activation: yr(this.activation), recurrentActivation: yr(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), unitForgetBias: this.unitForgetBias, kernelRegularizer: it(this.kernelRegularizer), recurrentRegularizer: it(this.recurrentRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation };
     return { ...e, ...t };
   }
 };
 jl.className = "LSTMCell";
 ae.registerClass(jl);
-var ly = class extends Ar {
+var dy = class extends Er {
   constructor(e) {
     e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new jl(e), super(e);
   }
@@ -14887,9 +14945,9 @@ var ly = class extends Ar {
     return t.implmentation === 0 && (t.implementation = 1), new e(t);
   }
 };
-ly.className = "LSTM";
-ae.registerClass(ly);
-var Vp = class extends ql {
+dy.className = "LSTM";
+ae.registerClass(dy);
+var Up = class extends ql {
   constructor(e) {
     super(e), this.cells = e.cells;
   }
@@ -14918,10 +14976,10 @@ var Vp = class extends ql {
     });
   }
   build(e) {
-    ym(e) && (e = e[0]), e = e;
+    xm(e) && (e = e[0]), e = e;
     let t;
     this.cells.forEach((n, s) => {
-      ea(`RNNCell_${s}`, () => {
+      ta(`RNNCell_${s}`, () => {
         n.build(e), Array.isArray(n.stateSize) ? t = n.stateSize[0] : t = n.stateSize, e = [e[0], t];
       });
     }), this.built = true;
@@ -14960,7 +15018,7 @@ var Vp = class extends ql {
     let e = [];
     for (let t of this.cells)
       e.push(...t.weights);
-    return vm(e);
+    return wm(e);
   }
   setWeights(e) {
     let t = [];
@@ -14969,16 +15027,16 @@ var Vp = class extends ql {
       for (let a = 0; a < n.weights.length; ++a)
         t.push([n.weights[a], r[a]]);
     }
-    Pb(t);
+    Db(t);
   }
 };
-Vp.className = "StackedRNNCells";
-ae.registerClass(Vp);
-function vr(e) {
-  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : XI(t(), n), o = () => Ul(i, t, s);
+Up.className = "StackedRNNCells";
+ae.registerClass(Up);
+function xr(e) {
+  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : JI(t(), n), o = () => Wl(i, t, s);
   return !r || r <= 1 ? Ht(o().clone()) : Array(r).fill(void 0).map(o).map((l) => Ht(l.clone()));
 }
-var PS = class extends Ar {
+var VS = class extends Er {
   constructor(e) {
     if (e.unroll)
       throw new Fe("Unrolling is not possible with convolutional RNNs.");
@@ -15034,11 +15092,11 @@ var PS = class extends Ar {
     return [...e.slice(0, 2), ...o ? [n, c, p] : [c, p, n]];
   }
 };
-PS.className = "ConvRNN2D";
-var Wp = class extends jl {
+VS.className = "ConvRNN2D";
+var Gp = class extends jl {
   constructor(e) {
     let { filters: t, kernelSize: n, strides: s, padding: r, dataFormat: a, dilationRate: i } = e;
-    super({ ...e, units: t }), this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Xi(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Xi(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Un(this.padding), this.dataFormat = a || "channelsLast", Ct(this.dataFormat), this.dilationRate = Xi(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
+    super({ ...e, units: t }), this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Qi(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Qi(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Gn(this.padding), this.dataFormat = a || "channelsLast", Ct(this.dataFormat), this.dilationRate = Qi(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
   }
   build(e) {
     var t;
@@ -15056,7 +15114,7 @@ var Wp = class extends jl {
         o = new (t = class extends ns {
           apply(c, p) {
             let d = u.apply([l]), h = Mn([l]), f = u.apply([l * 2]);
-            return $b([d, h, f]);
+            return Cb([d, h, f]);
           }
         }, t.className = "CustomInit", t)();
       } else
@@ -15070,9 +15128,9 @@ var Wp = class extends jl {
       if (e.length !== 3)
         throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
       let n = t.training || false, s = e[0], r = e[1], a = e[2], i = 4;
-      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Qn(s), rate: this.dropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
+      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = xr({ ones: () => Zn(s), rate: this.dropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
       let o = this.dropoutMask, u = (Z, te, ee) => !te || !te[ee] ? Z : V(te[ee], Z), l = u(s, o, 0), c = u(s, o, 1), p = u(s, o, 2), d = u(s, o, 3);
-      0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Qn(r), rate: this.recurrentDropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
+      0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = xr({ ones: () => Zn(r), rate: this.recurrentDropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
       let h = this.recurrentDropoutMask, f = u(r, h, 0), m = u(r, h, 1), g = u(r, h, 2), b = u(r, h, 3), y = 3, [v, x, k, T] = Bn(this.kernel.read(), i, y), [N, E, A, P] = this.useBias ? Bn(this.bias.read(), i) : [null, null, null, null];
       l = this.inputConv(l, v, N, this.padding), c = this.inputConv(c, x, E, this.padding), p = this.inputConv(p, k, A, this.padding), d = this.inputConv(d, T, P, this.padding);
       let [R, F, $, z] = Bn(this.recurrentKernel.read(), i, y);
@@ -15086,27 +15144,27 @@ var Wp = class extends jl {
     return { ...t, ...n };
   }
   inputConv(e, t, n, s) {
-    let r = ca(e, t, this.strides, s || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
+    let r = da(e, t, this.strides, s || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
     return n ? ws(r, n, this.dataFormat) : r;
   }
   recurrentConv(e, t) {
-    return ca(e, t, 1, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
+    return da(e, t, 1, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
   }
 };
-Wp.className = "ConvLSTM2DCell";
-ae.registerClass(Wp);
-var cy = class extends PS {
+Gp.className = "ConvLSTM2DCell";
+ae.registerClass(Gp);
+var py = class extends VS {
   constructor(e) {
-    let t = new Wp(e);
+    let t = new Gp(e);
     super({ ...e, cell: t });
   }
   static fromConfig(e, t) {
     return new e(t);
   }
 };
-cy.className = "ConvLSTM2D";
-ae.registerClass(cy);
-var Up = class extends Ge {
+py.className = "ConvLSTM2D";
+ae.registerClass(py);
+var Hp = class extends He {
   constructor(e) {
     super(e), this.rate = Math.max(Math.min(e.rate, 1), 0), this.noiseShape = e.noiseShape, this.seed = e.seed, this.supportsMasking = true;
   }
@@ -15124,7 +15182,7 @@ var Up = class extends Ge {
       let n = Oe(e);
       if (0 < this.rate && this.rate < 1) {
         let s = t.training == null ? false : t.training, r = this.getNoiseShape(n);
-        return Ul(() => XI(n, this.rate, r, this.seed), () => n, s);
+        return Wl(() => JI(n, this.rate, r, this.seed), () => n, s);
       }
       return e;
     });
@@ -15137,9 +15195,9 @@ var Up = class extends Ge {
     return super.dispose();
   }
 };
-Up.className = "Dropout";
-ae.registerClass(Up);
-var dy = class extends Up {
+Hp.className = "Dropout";
+ae.registerClass(Hp);
+var hy = class extends Hp {
   constructor(e) {
     super(e), this.inputSpec = [{ ndim: 3 }];
   }
@@ -15148,15 +15206,15 @@ var dy = class extends Up {
     return [t[0], 1, t[2]];
   }
 };
-dy.className = "SpatialDropout1D";
-ae.registerClass(dy);
-var py = class extends Ge {
+hy.className = "SpatialDropout1D";
+ae.registerClass(hy);
+var fy = class extends He {
   constructor(e) {
     if (super(e), this.activation = null, this.useBias = true, this.kernel = null, this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.batchInputShape == null && e.inputShape == null && e.inputDim != null) {
       let t = null;
       e.batchSize != null && (t = e.batchSize), this.batchInputShape = [t, e.inputDim];
     }
-    this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation), e.useBias != null && (this.useBias = e.useBias), this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelConstraint = Pt(e.kernelConstraint), this.biasConstraint = Pt(e.biasConstraint), this.kernelRegularizer = ft(e.kernelRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.activityRegularizer = ft(e.activityRegularizer), this.supportsMasking = true, this.inputSpec = [{ minNDim: 2 }];
+    this.units = e.units, Bt(this.units, "units"), this.activation = vr(e.activation), e.useBias != null && (this.useBias = e.useBias), this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelConstraint = Pt(e.kernelConstraint), this.biasConstraint = Pt(e.biasConstraint), this.kernelRegularizer = ft(e.kernelRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.activityRegularizer = ft(e.activityRegularizer), this.supportsMasking = true, this.inputSpec = [{ minNDim: 2 }];
   }
   build(e) {
     e = nt(e);
@@ -15171,18 +15229,18 @@ var py = class extends Ge {
   call(e, t) {
     return j(() => {
       this.invokeCallHook(e, t);
-      let n = Oe(e), s = UI(this.activation.getClassName()), r;
+      let n = Oe(e), s = qI(this.activation.getClassName()), r;
       return s != null ? r = Es(n, this.kernel.read(), s, this.bias ? this.bias.read() : null) : (r = Es(n, this.kernel.read()), this.bias != null && (r = ws(r, this.bias.read())), this.activation != null && (r = this.activation.apply(r))), r;
     });
   }
   getConfig() {
-    let e = { units: this.units, activation: br(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
+    let e = { units: this.units, activation: yr(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: it(this.kernelRegularizer), biasRegularizer: it(this.biasRegularizer), activityRegularizer: it(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-py.className = "Dense";
-ae.registerClass(py);
-var hy = class extends Ge {
+fy.className = "Dense";
+ae.registerClass(fy);
+var my = class extends He {
   constructor(e) {
     e = e || {}, super(e), this.inputSpec = [{ minNDim: 3 }], this.dataFormat = e.dataFormat;
   }
@@ -15191,7 +15249,7 @@ var hy = class extends Ge {
     for (let t of e.slice(1))
       if (t == null)
         throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);
-    return [e[0], dr(e, 1)];
+    return [e[0], pr(e, 1)];
   }
   call(e, t) {
     return j(() => {
@@ -15201,9 +15259,9 @@ var hy = class extends Ge {
         let s = [0];
         for (let r = 2; r < n.rank; ++r)
           s.push(r);
-        s.push(1), n = qe(n, s);
+        s.push(1), n = Ge(n, s);
       }
-      return rL(n);
+      return sz(n);
     });
   }
   getConfig() {
@@ -15213,11 +15271,11 @@ var hy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-hy.className = "Flatten";
-ae.registerClass(hy);
-var fy = class extends Ge {
+my.className = "Flatten";
+ae.registerClass(my);
+var gy = class extends He {
   constructor(e) {
-    super(e), this.supportsMasking = true, this.activation = yr(e.activation);
+    super(e), this.supportsMasking = true, this.activation = vr(e.activation);
   }
   call(e, t) {
     return j(() => {
@@ -15227,13 +15285,13 @@ var fy = class extends Ge {
     });
   }
   getConfig() {
-    let e = { activation: br(this.activation) }, t = super.getConfig();
+    let e = { activation: yr(this.activation) }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-fy.className = "Activation";
-ae.registerClass(fy);
-var my = class extends Ge {
+gy.className = "Activation";
+ae.registerClass(gy);
+var by = class extends He {
   constructor(e) {
     super(e), this.n = e.n, this.inputSpec = [{ ndim: 2 }];
   }
@@ -15241,16 +15299,16 @@ var my = class extends Ge {
     return [e[0], this.n, e[1]];
   }
   call(e, t) {
-    return j(() => (e = Oe(e), nL(e, this.n)));
+    return j(() => (e = Oe(e), tz(e, this.n)));
   }
   getConfig() {
     let e = { n: this.n }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-my.className = "RepeatVector";
-ae.registerClass(my);
-var gy = class extends Ge {
+by.className = "RepeatVector";
+ae.registerClass(by);
+var yy = class extends He {
   constructor(e) {
     super(e), this.targetShape = e.targetShape;
     for (let t = 0; t < this.targetShape.length; ++t)
@@ -15271,7 +15329,7 @@ var gy = class extends Ge {
       else
         r *= u;
     }
-    let i = dr(e);
+    let i = pr(e);
     if (a !== null) {
       if (r === 0 || i % r !== 0)
         throw new U(n);
@@ -15301,15 +15359,15 @@ var gy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-gy.className = "Reshape";
-ae.registerClass(gy);
-var by = class extends Ge {
+yy.className = "Reshape";
+ae.registerClass(yy);
+var vy = class extends He {
   constructor(e) {
     if (super(e), e.dims == null)
       throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
     if (!Array.isArray(e.dims))
       throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);
-    let t = ys(1, e.dims.length + 1);
+    let t = bs(1, e.dims.length + 1);
     if (!w.arraysEqual(e.dims.slice().sort(), t))
       throw new Error("Invalid permutation `dims`: " + JSON.stringify(e.dims) + " `dims` must contain consecutive integers starting from 1.");
     this.dims = e.dims, this.dimsIncludingBatch = [0].concat(this.dims), this.inputSpec = [new Dt({ ndim: this.dims.length + 1 })];
@@ -15322,16 +15380,16 @@ var by = class extends Ge {
     }), t;
   }
   call(e, t) {
-    return qe(Oe(e), this.dimsIncludingBatch);
+    return Ge(Oe(e), this.dimsIncludingBatch);
   }
   getConfig() {
     let e = { dims: this.dims }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-by.className = "Permute";
-ae.registerClass(by);
-var yy = class extends Ge {
+vy.className = "Permute";
+ae.registerClass(vy);
+var xy = class extends He {
   constructor(e) {
     super(e == null ? {} : e), this.supportsMasking = true, e != null ? this.maskValue = e.maskValue == null ? 0 : e.maskValue : this.maskValue = 0;
   }
@@ -15344,19 +15402,19 @@ var yy = class extends Ge {
   }
   computeMask(e, t) {
     let n = Oe(e), s = -1;
-    return pm(Xu(n, this.maskValue), s);
+    return hm(Yu(n, this.maskValue), s);
   }
   call(e, t) {
     return j(() => {
       this.invokeCallHook(e, t);
-      let n = Oe(e), s = -1, r = true, a = pm(Xu(n, this.maskValue), s, r);
+      let n = Oe(e), s = -1, r = true, a = hm(Yu(n, this.maskValue), s, r);
       return V(n, ce(a, n.dtype));
     });
   }
 };
-yy.className = "Masking";
-ae.registerClass(yy);
-var vy = class extends Ge {
+xy.className = "Masking";
+ae.registerClass(xy);
+var wy = class extends He {
   constructor(e) {
     if (super(e), this.embeddings = null, this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", e.batchInputShape == null && e.inputShape == null) {
       let t = null;
@@ -15370,7 +15428,7 @@ var vy = class extends Ge {
   warnOnIncompatibleInputShape(e) {
   }
   computeMask(e, t) {
-    return j(() => this.maskZero ? (e = Oe(e), Xu(e, je(e))) : null);
+    return j(() => this.maskZero ? (e = Oe(e), Yu(e, je(e))) : null);
   }
   computeOutputShape(e) {
     if (e = nt(e), this.inputLength == null)
@@ -15393,8 +15451,8 @@ var vy = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      n.dtype !== "int32" && (n = Sp(n, "int32"));
-      let s = KI(this.embeddings.read(), G(n, [n.size]));
+      n.dtype !== "int32" && (n = Tp(n, "int32"));
+      let s = ZI(this.embeddings.read(), G(n, [n.size]));
       return G(s, nt(this.computeOutputShape(n.shape)));
     });
   }
@@ -15403,9 +15461,9 @@ var vy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-vy.className = "Embedding";
-ae.registerClass(vy);
-var bi = class extends Ge {
+wy.className = "Embedding";
+ae.registerClass(wy);
+var xi = class extends He {
   constructor(e) {
     super(e || {}), this.supportsMasking = true;
   }
@@ -15442,7 +15500,7 @@ var bi = class extends Ge {
     let t = [];
     for (let r of e)
       r != null && r[0] !== null && t.push(r[0]);
-    if (t = cr(t), t.length > 1)
+    if (t = dr(t), t.length > 1)
       throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);
     let n = e[0] == null ? null : e[0].slice(1);
     for (let r = 1; r < e.length; ++r) {
@@ -15450,18 +15508,18 @@ var bi = class extends Ge {
       n = this.computeElementwiseOpOutputShape(n, a);
     }
     let s = e.map((r) => r.length);
-    e.indexOf(null) === -1 && cr(s).length === 1 ? this.reshapeRequired = false : this.reshapeRequired = true;
+    e.indexOf(null) === -1 && dr(s).length === 1 ? this.reshapeRequired = false : this.reshapeRequired = true;
   }
   call(e, t) {
     return j(() => {
       if (e = e, this.reshapeRequired) {
         let n = [], s = e.map((r) => r.rank);
         if (s.indexOf(null) === -1) {
-          let r = gr(s);
+          let r = br(s);
           for (let a of e) {
             let i = a.rank;
             for (let o = 0; o < r - i; ++o)
-              a = Vl(a, 1);
+              a = Bl(a, 1);
             n.push(a);
           }
           return this.mergeFunction(n);
@@ -15470,11 +15528,11 @@ var bi = class extends Ge {
           for (let o of e) {
             let u = o.rank;
             if (u == null) {
-              let l = o.shape, c = l[0], p = l.slice(1).concat([c]), d = G(o, [c].concat(dr(l.slice(1))));
-              d = qe(d, [1, 0]), d = G(d, p), n.push(d), r = true;
+              let l = o.shape, c = l[0], p = l.slice(1).concat([c]), d = G(o, [c].concat(pr(l.slice(1))));
+              d = Ge(d, [1, 0]), d = G(d, p), n.push(d), r = true;
             } else if (u > 1) {
-              let l = ys(1, u).concat([0]);
-              n.push(qe(o, l)), r = true;
+              let l = bs(1, u).concat([0]);
+              n.push(Ge(o, l)), r = true;
             } else
               n.push(o);
           }
@@ -15482,10 +15540,10 @@ var bi = class extends Ge {
           if (r) {
             if (i == null) {
               let o = a.shape, u = o.length, l = o[u - 1], c = [l].concat(o.slice(0, o.length - 1));
-              a = G(qe(G(a, [-1, l]), [1, 0]), c);
+              a = G(Ge(G(a, [-1, l]), [1, 0]), c);
             } else if (i > 1) {
-              let o = [i - 1].concat(ys(0, i - 1));
-              a = qe(a, o);
+              let o = [i - 1].concat(bs(0, i - 1));
+              a = Ge(a, o);
             }
           }
           return a;
@@ -15505,7 +15563,7 @@ var bi = class extends Ge {
     let n = [];
     for (let s of e)
       s != null && s[0] !== null && n.push(s[0]);
-    return n = cr(n), n.length === 1 ? t = n.concat(t) : t = [null].concat(t), t;
+    return n = dr(n), n.length === 1 ? t = n.concat(t) : t = [null].concat(t), t;
   }
   computeMask(e, t) {
     return j(() => {
@@ -15527,7 +15585,7 @@ var bi = class extends Ge {
     });
   }
 };
-var xy = class extends bi {
+var ky = class extends xi {
   constructor(e) {
     super(e);
   }
@@ -15540,9 +15598,9 @@ var xy = class extends bi {
     });
   }
 };
-xy.className = "Add";
-ae.registerClass(xy);
-var wy = class extends bi {
+ky.className = "Add";
+ae.registerClass(ky);
+var Iy = class extends xi {
   constructor(e) {
     super(e);
   }
@@ -15555,9 +15613,9 @@ var wy = class extends bi {
     });
   }
 };
-wy.className = "Multiply";
-ae.registerClass(wy);
-var ky = class extends bi {
+Iy.className = "Multiply";
+ae.registerClass(Iy);
+var Sy = class extends xi {
   constructor(e) {
     super(e);
   }
@@ -15570,39 +15628,39 @@ var ky = class extends bi {
     });
   }
 };
-ky.className = "Average";
-ae.registerClass(ky);
-var Iy = class extends bi {
-  constructor(e) {
-    super(e);
-  }
-  mergeFunction(e) {
-    return j(() => {
-      let t = e[0];
-      for (let n = 1; n < e.length; ++n)
-        t = $r(t, e[n]);
-      return t;
-    });
-  }
-};
-Iy.className = "Maximum";
-ae.registerClass(Iy);
-var Sy = class extends bi {
-  constructor(e) {
-    super(e);
-  }
-  mergeFunction(e) {
-    return j(() => {
-      let t = e[0];
-      for (let n = 1; n < e.length; ++n)
-        t = bp(t, e[n]);
-      return t;
-    });
-  }
-};
-Sy.className = "Minimum";
+Sy.className = "Average";
 ae.registerClass(Sy);
-var Cy = class extends bi {
+var Cy = class extends xi {
+  constructor(e) {
+    super(e);
+  }
+  mergeFunction(e) {
+    return j(() => {
+      let t = e[0];
+      for (let n = 1; n < e.length; ++n)
+        t = _r(t, e[n]);
+      return t;
+    });
+  }
+};
+Cy.className = "Maximum";
+ae.registerClass(Cy);
+var Ny = class extends xi {
+  constructor(e) {
+    super(e);
+  }
+  mergeFunction(e) {
+    return j(() => {
+      let t = e[0];
+      for (let n = 1; n < e.length; ++n)
+        t = vp(t, e[n]);
+      return t;
+    });
+  }
+};
+Ny.className = "Minimum";
+ae.registerClass(Ny);
+var Ty = class extends xi {
   constructor(e) {
     super(e), this.DEFAULT_AXIS = -1, e == null && (e = {}), this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis, this.supportsMasking = true, this.reshapeRequired = false;
   }
@@ -15634,7 +15692,7 @@ var Cy = class extends bi {
       throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(e));
   }
   mergeFunction(e) {
-    return j(() => $b(e, this.axis));
+    return j(() => Cb(e, this.axis));
   }
   computeOutputShape(e) {
     if (!(Array.isArray(e) && Array.isArray(e[0])))
@@ -15669,9 +15727,9 @@ var Cy = class extends bi {
         return null;
       let s = [];
       for (let a = 0; a < e.length; ++a)
-        t[a] == null ? s.push(ce(Qn(e[a]), "bool")) : t[a].rank < e[a].rank ? s.push(Pn(t[a], -1)) : s.push(t[a]);
+        t[a] == null ? s.push(ce(Zn(e[a]), "bool")) : t[a].rank < e[a].rank ? s.push(Pn(t[a], -1)) : s.push(t[a]);
       let r = Ft(s, this.axis);
-      return qk(r, -1, false);
+      return Yk(r, -1, false);
     });
   }
   getConfig() {
@@ -15679,14 +15737,14 @@ var Cy = class extends bi {
     return Object.assign(e, t), e;
   }
 };
-Cy.className = "Concatenate";
-ae.registerClass(Cy);
-function Cu(e, t) {
+Ty.className = "Concatenate";
+ae.registerClass(Ty);
+function Nu(e, t) {
   for (; e < 0; )
     e += t;
   return e;
 }
-function VB(e, t, n) {
+function QB(e, t, n) {
   if (e.shape.length > 3 || t.shape.length > 3)
     throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");
   if (w.assert(e.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`), w.assert(e.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`), typeof n == "number" && (n = [n, n]), e.dtype === "complex64" || t.dtype === "complex64")
@@ -15712,7 +15770,7 @@ function VB(e, t, n) {
       i = 0;
     let o;
     if (e.shape.length === 2 && t.shape.length === 2)
-      a[0] === a[1] ? o = ve(V(e, t), a[0]) : o = ve(V(qe(e, [1, 0]), t), a[1]);
+      a[0] === a[1] ? o = ve(V(e, t), a[0]) : o = ve(V(Ge(e, [1, 0]), t), a[1]);
     else {
       let u = a[0] !== e.shape.length - 1, l = a[1] === t.shape.length - 1;
       o = We(e, t, u, l);
@@ -15723,12 +15781,12 @@ function VB(e, t, n) {
       let l = [];
       for (let c = u; c < u + i; ++c)
         l.push(c);
-      o = mr(o, l);
+      o = gr(o, l);
     }
     return o.shape.length === 1 && (o = Pn(o, 1)), o;
   });
 }
-var Ny = class extends bi {
+var $y = class extends xi {
   constructor(e) {
     super(e), this.axes = e.axes, this.normalize = e.normalize == null ? false : e.normalize, this.supportsMasking = true, this.reshapeRequired = false;
   }
@@ -15745,11 +15803,11 @@ var Ny = class extends bi {
     if (e.length !== 2)
       throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);
     let t = e[0], n = e[1], s;
-    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => Cu(r, e[a].shape.length)) : s = [Cu(this.axes, t.shape.length), Cu(this.axes, n.shape.length)], this.normalize && (t = Nd(t, s[0]), n = Nd(n, s[1])), VB(t, n, s);
+    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => Nu(r, e[a].shape.length)) : s = [Nu(this.axes, t.shape.length), Nu(this.axes, n.shape.length)], this.normalize && (t = $d(t, s[0]), n = $d(n, s[1])), QB(t, n, s);
   }
   interpretAxes(e, t) {
     let n;
-    return Array.isArray(this.axes) ? n = this.axes : n = [Cu(this.axes, e.length), Cu(this.axes, t.length)], n;
+    return Array.isArray(this.axes) ? n = this.axes : n = [Nu(this.axes, e.length), Nu(this.axes, t.length)], n;
   }
   computeOutputShape(e) {
     w.assert(Array.isArray(e) && e.length === 2 && Array.isArray(e[0]) && Array.isArray(e[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
@@ -15769,9 +15827,9 @@ var Ny = class extends bi {
     return Object.assign(e, t), e;
   }
 };
-Ny.className = "Dot";
-ae.registerClass(Ny);
-var Ty = class extends Ge {
+$y.className = "Dot";
+ae.registerClass($y);
+var _y = class extends He {
   constructor(e) {
     super(e), this.supportsMasking = true, this.stddev = e.stddev;
   }
@@ -15786,13 +15844,13 @@ var Ty = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      return Ul(() => ie(Cp(n.shape, 0, this.stddev), n), () => n, t.training || false);
+      return Wl(() => ie($p(n.shape, 0, this.stddev), n), () => n, t.training || false);
     });
   }
 };
-Ty.className = "GaussianNoise";
-ae.registerClass(Ty);
-var $y = class extends Ge {
+_y.className = "GaussianNoise";
+ae.registerClass(_y);
+var Ay = class extends He {
   constructor(e) {
     super(e), this.supportsMasking = true, this.rate = e.rate;
   }
@@ -15807,16 +15865,16 @@ var $y = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      return this.rate > 0 && this.rate < 1 ? Ul(() => {
+      return this.rate > 0 && this.rate < 1 ? Wl(() => {
         let r = Math.sqrt(this.rate / (1 - this.rate));
-        return V(n, Cp(n.shape, 1, r));
+        return V(n, $p(n.shape, 1, r));
       }, () => n, t.training || false) : n;
     });
   }
 };
-$y.className = "GaussianDropout";
-ae.registerClass($y);
-var _y = class extends Ge {
+Ay.className = "GaussianDropout";
+ae.registerClass(Ay);
+var Ey = class extends He {
   constructor(e) {
     super(e), this.supportsMasking = true, this.rate = e.rate, this.noiseShape = e.noiseShape;
   }
@@ -15834,9 +15892,9 @@ var _y = class extends Ge {
     return j(() => {
       if (this.rate < 1 && this.rate > 0) {
         let n = this._getNoiseShape(e);
-        return Ul(() => {
-          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = Ko(Ml(n), this.rate);
-          u = Sp(u, "float32");
+        return Wl(() => {
+          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = Xo(Ml(n), this.rate);
+          u = Tp(u, "float32");
           let l = ((1 - this.rate) * (1 + this.rate * o ** 2)) ** -0.5, c = -l * o * this.rate, p = ie(V(r, u), V(ie(u, -1), o));
           return ie(V(p, l), c);
         }, () => Oe(e), t.training || false);
@@ -15845,39 +15903,39 @@ var _y = class extends Ge {
     });
   }
 };
-_y.className = "AlphaDropout";
-ae.registerClass(_y);
-function Ju(e, t, n, s, r, a = 1e-3) {
+Ey.className = "AlphaDropout";
+ae.registerClass(Ey);
+function el(e, t, n, s, r, a = 1e-3) {
   let i;
   if (e.rank === 2)
-    i = $E(e, t, n, s, r, a);
+    i = FE(e, t, n, s, r, a);
   else if (e.rank === 3)
-    i = AE(e, t, n, s, r, a);
+    i = PE(e, t, n, s, r, a);
   else if (e.rank === 4)
-    i = RE(e, t, n, s, r, a);
+    i = ME(e, t, n, s, r, a);
   else
     throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);
   return i;
 }
-function WB(e, t, n, s, r = 1e-3) {
+function ZB(e, t, n, s, r = 1e-3) {
   return j(() => {
-    let a = tb(e, s), i = a.mean, o = a.variance;
-    return [Ju(e, i, o, n, t, r), i, o];
+    let a = sb(e, s), i = a.mean, o = a.variance;
+    return [el(e, i, o, n, t, r), i, o];
   });
 }
-function UB(e, t, n, s, r = 1e-3) {
+function JB(e, t, n, s, r = 1e-3) {
   return j(() => {
-    let a = tb(e, s), i = a.mean, o = a.variance, u = [];
-    for (let f of ys(0, e.rank))
+    let a = sb(e, s), i = a.mean, o = a.variance, u = [];
+    for (let f of bs(0, e.rank))
       s.indexOf(f) !== -1 ? u.push(1) : u.push(e.shape[f]);
     let l = G(i, u), c = G(o, u), p = t == null ? null : G(t, u), d = n == null ? null : G(n, u);
-    return [Ju(e, l, c, d, p, r), i, o];
+    return [el(e, l, c, d, p, r), i, o];
   });
 }
-function GB(e, t, n, s, r = 1e-3) {
-  return w.arraysEqual(s.slice().sort(), ys(0, e.rank - 1)) ? WB(e, t, n, s, r) : UB(e, t, n, s, r);
+function eV(e, t, n, s, r = 1e-3) {
+  return w.arraysEqual(s.slice().sort(), bs(0, e.rank - 1)) ? ZB(e, t, n, s, r) : JB(e, t, n, s, r);
 }
-var Ay = class extends Ge {
+var Ry = class extends He {
   constructor(e) {
     e == null && (e = {}), super(e), this.supportsMasking = true, this.axis = e.axis == null ? -1 : e.axis, this.momentum = e.momentum == null ? 0.99 : e.momentum, this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ht(e.betaInitializer || "zeros"), this.gammaInitializer = ht(e.gammaInitializer || "ones"), this.movingMeanInitializer = ht(e.movingMeanInitializer || "zeros"), this.movingVarianceInitializer = ht(e.movingVarianceInitializer || "ones"), this.betaConstraint = Pt(e.betaConstraint), this.gammaConstraint = Pt(e.gammaConstraint), this.betaRegularizer = ft(e.betaRegularizer), this.gammaRegularizer = ft(e.gammaRegularizer);
   }
@@ -15892,22 +15950,22 @@ var Ay = class extends Ge {
   }
   call(e, t) {
     return j(() => {
-      let n = t.training == null ? false : t.training, s = Oe(e), r = s.shape, a = r.length, i = ys(0, a), o = this.axis >= 0 ? this.axis : this.axis + a;
+      let n = t.training == null ? false : t.training, s = Oe(e), r = s.shape, a = r.length, i = bs(0, a), o = this.axis >= 0 ? this.axis : this.axis + a;
       i.splice(o, 1);
-      let u = fa(1, a);
+      let u = ma(1, a);
       u[o] = r[o];
       let l = i.slice();
       l.sort();
-      let c = !w.arraysEqual(l, ys(0, a).slice(0, a - 1)), p = () => {
+      let c = !w.arraysEqual(l, bs(0, a).slice(0, a - 1)), p = () => {
         if (c) {
           let b = G(this.movingMean.read(), u), y = G(this.movingVariance.read(), u), v = this.center ? G(this.beta.read(), u) : null, x = this.scale ? G(this.gamma.read(), u) : null;
-          return Ju(s, b, y, v, x, this.epsilon);
+          return el(s, b, y, v, x, this.epsilon);
         } else
-          return Ju(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
+          return el(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
       };
       if (!n)
         return p();
-      let [d, h, f] = GB(s, this.gamma.read(), this.beta.read(), i, this.epsilon), m = (b, y, v) => {
+      let [d, h, f] = eV(s, this.gamma.read(), this.beta.read(), i, this.epsilon), m = (b, y, v) => {
         j(() => {
           let x = 1 - v, k = b.read(), T = V(ge(k, y), x);
           b.write(ge(k, T));
@@ -15923,9 +15981,9 @@ var Ay = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Ay.className = "BatchNormalization";
-ae.registerClass(Ay);
-var Ey = class extends Ge {
+Ry.className = "BatchNormalization";
+ae.registerClass(Ry);
+var Dy = class extends He {
   constructor(e) {
     if (e == null && (e = {}), super(e), this.axis = e.axis == null ? -1 : e.axis, typeof this.axis == "number") {
       if (!Number.isInteger(this.axis))
@@ -15947,7 +16005,7 @@ var Ey = class extends Ge {
     for (let r of this.axis)
       if (r < 0 || r >= t)
         throw new Error(`Invalid axis: ${r}`);
-    if (this.axis.length !== cr(this.axis).length)
+    if (this.axis.length !== dr(this.axis).length)
       throw new Error(`Found duplicate axes in: ${this.axis}`);
     let n = this.axis.map((r) => e[r]), s = true;
     this.scale ? this.gamma = this.addWeight("gamma", n, "float32", this.gammaInitializer, this.gammaRegularizer, s) : this.gamma = null, this.center ? this.beta = this.addWeight("beta", n, "float32", this.betaInitializer, this.betaRegularizer, s) : this.beta = null, this.built = true;
@@ -15955,13 +16013,13 @@ var Ey = class extends Ge {
   call(e, t) {
     let n = Oe(e), s = n.shape, r = s.length;
     return j(() => {
-      let { mean: i, variance: o } = tb(n, this.axis, true), u = fa(1, r);
+      let { mean: i, variance: o } = sb(n, this.axis, true), u = ma(1, r);
       for (let f of this.axis)
         u[f] = s[f];
       let l = (f) => f != null && f.shape.length !== r ? G(f, u) : f, c = l(this.gamma.read()), p = l(this.beta.read()), d = [], h = [];
       for (let f = 0; f < r; ++f)
         this.axis.indexOf(f) !== -1 ? (d.push(s[f]), h.push(1)) : (d.push(1), h.push(s[f]));
-      return i = ps(i, d), o = ps(o, d), c = ps(c, h), p = ps(p, h), Ju(n, i, o, p, c, this.epsilon);
+      return i = ps(i, d), o = ps(o, d), c = ps(c, h), p = ps(p, h), el(n, i, o, p, c, this.epsilon);
     });
   }
   getConfig() {
@@ -15969,23 +16027,23 @@ var Ey = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Ey.className = "LayerNormalization";
-ae.registerClass(Ey);
-function HB(e, t, n) {
+Dy.className = "LayerNormalization";
+ae.registerClass(Dy);
+function tV(e, t, n) {
   return j(() => {
     if (e.rank !== 4)
       throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);
     if (t == null && (t = [[1, 1], [1, 1]]), t.length !== 2 || t[0].length !== 2 || t[1].length !== 2)
       throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");
-    if (n == null && (n = bs()), n !== "channelsLast" && n !== "channelsFirst")
+    if (n == null && (n = ys()), n !== "channelsLast" && n !== "channelsFirst")
       throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
     let s;
-    return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], fi(e, s);
+    return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], bi(e, s);
   });
 }
-var Ry = class extends Ge {
+var Fy = class extends He {
   constructor(e) {
-    if (e == null && (e = {}), super(e), this.dataFormat = e.dataFormat == null ? bs() : e.dataFormat, e.padding == null)
+    if (e == null && (e = {}), super(e), this.dataFormat = e.dataFormat == null ? ys() : e.dataFormat, e.padding == null)
       this.padding = [[1, 1], [1, 1]];
     else if (typeof e.padding == "number")
       this.padding = [[e.padding, e.padding], [e.padding, e.padding]];
@@ -16012,30 +16070,30 @@ var Ry = class extends Ge {
     return this.dataFormat === "channelsFirst" ? (e[2] != null && e[2] >= 0 ? t = e[2] + this.padding[0][0] + this.padding[0][1] : t = null, e[3] != null && e[3] >= 0 ? n = e[3] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], e[1], t, n]) : (e[1] != null && e[1] >= 0 ? t = e[1] + this.padding[0][0] + this.padding[0][1] : t = null, e[2] != null && e[2] >= 0 ? n = e[2] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], t, n, e[3]]);
   }
   call(e, t) {
-    return j(() => HB(Oe(e), this.padding, this.dataFormat));
+    return j(() => tV(Oe(e), this.padding, this.dataFormat));
   }
   getConfig() {
     let e = { padding: this.padding, dataFormat: this.dataFormat }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-Ry.className = "ZeroPadding2D";
-ae.registerClass(Ry);
-function Gp(e, t, n, s, r, a) {
+Fy.className = "ZeroPadding2D";
+ae.registerClass(Fy);
+function qp(e, t, n, s, r, a) {
   return j(() => {
-    Ct(r), GI(a), Un(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = bs()), a == null && (a = "max"), e = Zb(e, r);
+    Ct(r), KI(a), Gn(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = ys()), a == null && (a = "max"), e = ey(e, r);
     let i, o = s === "same" ? "same" : "valid";
-    return a === "max" ? i = eb(e, t, n, o) : i = Gg(e, t, n, o), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
+    return a === "max" ? i = nb(e, t, n, o) : i = qg(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 3, 1, 2])), i;
   });
 }
-function zS(e, t, n, s, r, a) {
+function WS(e, t, n, s, r, a) {
   return j(() => {
-    Ct(r), GI(a), Un(s), n == null && (n = [1, 1, 1]), s == null && (s = "valid"), r == null && (r = bs()), a == null && (a = "max"), e = $S(e, r);
+    Ct(r), KI(a), Gn(s), n == null && (n = [1, 1, 1]), s == null && (s = "valid"), r == null && (r = ys()), a == null && (a = "max"), e = DS(e, r);
     let i, o = s === "same" ? "same" : "valid";
-    return a === "max" ? i = dI(e, t, n, o) : i = Yk(e, t, n, o), r === "channelsFirst" && (i = qe(i, [0, 4, 1, 2, 3])), i;
+    return a === "max" ? i = mI(e, t, n, o) : i = eI(e, t, n, o), r === "channelsFirst" && (i = Ge(i, [0, 4, 1, 2, 3])), i;
   });
 }
-var MS = class extends Ge {
+var US = class extends He {
   constructor(e) {
     if (e.poolSize == null && (e.poolSize = 2), super(e), typeof e.poolSize == "number")
       this.poolSize = [e.poolSize];
@@ -16051,7 +16109,7 @@ var MS = class extends Ge {
       this.strides = e.strides;
     else
       throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);
-    Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, Un(this.padding), this.inputSpec = [new Dt({ ndim: 3 })];
+    Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, Gn(this.padding), this.inputSpec = [new Dt({ ndim: 3 })];
   }
   computeOutputShape(e) {
     e = nt(e);
@@ -16060,9 +16118,9 @@ var MS = class extends Ge {
   }
   call(e, t) {
     return j(() => {
-      this.invokeCallHook(e, t), e = Vl(Oe(e), 2);
+      this.invokeCallHook(e, t), e = Bl(Oe(e), 2);
       let n = this.poolingFunction(Oe(e), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
-      return mr(n, [2]);
+      return gr(n, [2]);
     });
   }
   getConfig() {
@@ -16070,27 +16128,27 @@ var MS = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var Dy = class extends MS {
+var Oy = class extends US {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Un(s), Gp(e, t, n, s, r, "max");
+    return Ct(r), Gn(s), qp(e, t, n, s, r, "max");
   }
 };
-Dy.className = "MaxPooling1D";
-ae.registerClass(Dy);
-var Fy = class extends MS {
+Oy.className = "MaxPooling1D";
+ae.registerClass(Oy);
+var Py = class extends US {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Un(s), Gp(e, t, n, s, r, "avg");
+    return Ct(r), Gn(s), qp(e, t, n, s, r, "avg");
   }
 };
-Fy.className = "AveragePooling1D";
-ae.registerClass(Fy);
-var LS = class extends Ge {
+Py.className = "AveragePooling1D";
+ae.registerClass(Py);
+var GS = class extends He {
   constructor(e) {
     if (e.poolSize == null && (e.poolSize = [2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize], e.strides == null)
       this.strides = this.poolSize;
@@ -16100,7 +16158,7 @@ var LS = class extends Ge {
       this.strides = e.strides;
     } else
       this.strides = [e.strides, e.strides];
-    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Un(this.padding), this.inputSpec = [new Dt({ ndim: 4 })];
+    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Gn(this.padding), this.inputSpec = [new Dt({ ndim: 4 })];
   }
   computeOutputShape(e) {
     e = nt(e);
@@ -16115,27 +16173,27 @@ var LS = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var Oy = class extends LS {
+var zy = class extends GS {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Un(s), Gp(e, t, n, s, r, "max");
+    return Ct(r), Gn(s), qp(e, t, n, s, r, "max");
   }
 };
-Oy.className = "MaxPooling2D";
-ae.registerClass(Oy);
-var Py = class extends LS {
+zy.className = "MaxPooling2D";
+ae.registerClass(zy);
+var My = class extends GS {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Un(s), Gp(e, t, n, s, r, "avg");
+    return Ct(r), Gn(s), qp(e, t, n, s, r, "avg");
   }
 };
-Py.className = "AveragePooling2D";
-ae.registerClass(Py);
-var BS = class extends Ge {
+My.className = "AveragePooling2D";
+ae.registerClass(My);
+var HS = class extends He {
   constructor(e) {
     if (e.poolSize == null && (e.poolSize = [2, 2, 2]), super(e), this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize, e.poolSize], e.strides == null)
       this.strides = this.poolSize;
@@ -16145,7 +16203,7 @@ var BS = class extends Ge {
       this.strides = e.strides;
     } else
       this.strides = [e.strides, e.strides, e.strides];
-    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Un(this.padding), this.inputSpec = [new Dt({ ndim: 5 })];
+    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), Gn(this.padding), this.inputSpec = [new Dt({ ndim: 5 })];
   }
   computeOutputShape(e) {
     e = nt(e);
@@ -16160,27 +16218,27 @@ var BS = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var zy = class extends BS {
+var Ly = class extends HS {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Un(s), zS(e, t, n, s, r, "max");
+    return Ct(r), Gn(s), WS(e, t, n, s, r, "max");
   }
 };
-zy.className = "MaxPooling3D";
-ae.registerClass(zy);
-var My = class extends BS {
+Ly.className = "MaxPooling3D";
+ae.registerClass(Ly);
+var By = class extends HS {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return Ct(r), Un(s), zS(e, t, n, s, r, "avg");
+    return Ct(r), Gn(s), WS(e, t, n, s, r, "avg");
   }
 };
-My.className = "AveragePooling3D";
-ae.registerClass(My);
-var VS = class extends Ge {
+By.className = "AveragePooling3D";
+ae.registerClass(By);
+var qS = class extends He {
   constructor(e) {
     super(e), this.inputSpec = [new Dt({ ndim: 3 })];
   }
@@ -16191,7 +16249,7 @@ var VS = class extends Ge {
     throw new Fe();
   }
 };
-var Ly = class extends VS {
+var Vy = class extends qS {
   constructor(e) {
     super(e || {});
   }
@@ -16202,9 +16260,9 @@ var Ly = class extends VS {
     });
   }
 };
-Ly.className = "GlobalAveragePooling1D";
-ae.registerClass(Ly);
-var By = class extends VS {
+Vy.className = "GlobalAveragePooling1D";
+ae.registerClass(Vy);
+var Wy = class extends qS {
   constructor(e) {
     super(e || {});
   }
@@ -16215,9 +16273,9 @@ var By = class extends VS {
     });
   }
 };
-By.className = "GlobalMaxPooling1D";
-ae.registerClass(By);
-var WS = class extends Ge {
+Wy.className = "GlobalMaxPooling1D";
+ae.registerClass(Wy);
+var jS = class extends He {
   constructor(e) {
     super(e), this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, Ct(this.dataFormat), this.inputSpec = [new Dt({ ndim: 4 })];
   }
@@ -16232,7 +16290,7 @@ var WS = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var Vy = class extends WS {
+var Uy = class extends jS {
   call(e, t) {
     return j(() => {
       let n = Oe(e);
@@ -16240,9 +16298,9 @@ var Vy = class extends WS {
     });
   }
 };
-Vy.className = "GlobalAveragePooling2D";
-ae.registerClass(Vy);
-var Wy = class extends WS {
+Uy.className = "GlobalAveragePooling2D";
+ae.registerClass(Uy);
+var Gy = class extends jS {
   call(e, t) {
     return j(() => {
       let n = Oe(e);
@@ -16250,9 +16308,9 @@ var Wy = class extends WS {
     });
   }
 };
-Wy.className = "GlobalMaxPooling2D";
-ae.registerClass(Wy);
-var US = class extends Ge {
+Gy.className = "GlobalMaxPooling2D";
+ae.registerClass(Gy);
+var KS = class extends He {
   constructor(e) {
     super(e), this.layer = e.layer;
   }
@@ -16297,7 +16355,7 @@ var US = class extends Ge {
     return Object.assign(a, t), new e(a);
   }
 };
-var Uy = class extends US {
+var Hy = class extends KS {
   constructor(e) {
     super(e), this.supportsMasking = true;
   }
@@ -16314,22 +16372,22 @@ var Uy = class extends US {
     return [n[0], s].concat(n.slice(1));
   }
   call(e, t) {
-    return j(() => (e = Oe(e), FS((a, i) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
+    return j(() => (e = Oe(e), LS((a, i) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
   }
 };
-Uy.className = "TimeDistributed";
-ae.registerClass(Uy);
-function qB(e) {
-  mi(QM, "BidirectionalMergeMode", e);
+Hy.className = "TimeDistributed";
+ae.registerClass(Hy);
+function nV(e) {
+  yi(YP, "BidirectionalMergeMode", e);
 }
-var jB = "concat";
-var Gy = class extends US {
+var sV = "concat";
+var qy = class extends KS {
   constructor(e) {
     super(e);
     let t = e.layer.getConfig(), n = {};
     n.className = e.layer.getClassName(), n.config = t, this.forwardLayer = ms(n), t.goBackwards = t.goBackwards !== true;
     let s = {};
-    if (s.className = e.layer.getClassName(), s.config = t, this.backwardLayer = ms(s), this.forwardLayer.name = "forward_" + this.forwardLayer.name, this.backwardLayer.name = "backward_" + this.backwardLayer.name, this.mergeMode = e.mergeMode === void 0 ? jB : e.mergeMode, qB(this.mergeMode), e.weights)
+    if (s.className = e.layer.getClassName(), s.config = t, this.backwardLayer = ms(s), this.forwardLayer.name = "forward_" + this.forwardLayer.name, this.backwardLayer.name = "backward_" + this.backwardLayer.name, this.mergeMode = e.mergeMode === void 0 ? sV : e.mergeMode, nV(this.mergeMode), e.weights)
       throw new Fe("weights support is not implemented for Bidirectional layer yet.");
     this._stateful = e.layer.stateful, this.returnSequences = e.layer.returnSequences, this.returnState = e.layer.returnState, this.supportsMasking = true, this._trainable = true, this.inputSpec = e.layer.inputSpec, this.numConstants = null;
   }
@@ -16355,7 +16413,7 @@ var Gy = class extends US {
   apply(e, t) {
     let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
     t == null && (t = {});
-    let r = DS(e, n, s, this.numConstants);
+    let r = MS(e, n, s, this.numConstants);
     if (e = r.inputs, n = r.initialState, s = r.constants, Array.isArray(e) && (n = e.slice(1), e = e[0]), (n == null || n.length === 0) && s == null)
       return super.apply(e, t);
     let a = [], i = [];
@@ -16391,18 +16449,18 @@ var Gy = class extends US {
         s = this.forwardLayer.call(e, Object.assign(t, { initialState: o })), r = this.backwardLayer.call(e, Object.assign(t, { initialState: u }));
       }
       let a;
-      this.returnState && (Array.isArray(s) && (a = s.slice(1).concat(r.slice(1))), s = s[0], r = r[0]), this.returnSequences && (r = Zn(r, 1));
+      this.returnState && (Array.isArray(s) && (a = s.slice(1).concat(r.slice(1))), s = s[0], r = r[0]), this.returnSequences && (r = Jn(r, 1));
       let i;
-      return this.mergeMode === "concat" ? i = $b([s, r]) : this.mergeMode === "sum" ? i = ie(s, r) : this.mergeMode === "ave" ? i = V(0.5, ie(s, r)) : this.mergeMode === "mul" ? i = V(s, r) : this.mergeMode == null && (i = [s, r]), this.returnState ? this.mergeMode == null ? i.concat(a) : [i].concat(a) : i;
+      return this.mergeMode === "concat" ? i = Cb([s, r]) : this.mergeMode === "sum" ? i = ie(s, r) : this.mergeMode === "ave" ? i = V(0.5, ie(s, r)) : this.mergeMode === "mul" ? i = V(s, r) : this.mergeMode == null && (i = [s, r]), this.returnState ? this.mergeMode == null ? i.concat(a) : [i].concat(a) : i;
     });
   }
   resetStates(e) {
     this.forwardLayer.resetStates(), this.backwardLayer.resetStates();
   }
   build(e) {
-    ea(this.forwardLayer.name, () => {
+    ta(this.forwardLayer.name, () => {
       this.forwardLayer.build(e);
-    }), ea(this.backwardLayer.name, () => {
+    }), ta(this.backwardLayer.name, () => {
       this.backwardLayer.build(e);
     }), this.built = true;
   }
@@ -16436,279 +16494,279 @@ var Gy = class extends US {
     return s.layer = n, new e(s);
   }
 };
-Gy.className = "Bidirectional";
-ae.registerClass(Gy);
-function KB(e) {
-  return new Qo(e);
-}
-function XB(e) {
-  return new Xb(e);
-}
-function YB(e) {
-  return new qb(e);
-}
-function QB(e) {
-  return new jb(e);
-}
-function ZB(e) {
-  return new Kb(e);
-}
-function JB(e) {
-  return new Qb(e);
-}
-function eV(e) {
-  return new Yb(e);
-}
-function tV(e) {
-  return new sy(e);
-}
-function nV(e) {
-  return new zp(e);
-}
-function sV(e) {
-  return new ey(e);
-}
+qy.className = "Bidirectional";
+ae.registerClass(qy);
 function rV(e) {
-  return new Mp(e);
+  return new Zo(e);
 }
 function aV(e) {
-  return new ty(e);
+  return new Qb(e);
 }
 function iV(e) {
-  return new ny(e);
+  return new Kb(e);
 }
 function oV(e) {
-  return new ry(e);
+  return new Xb(e);
 }
 function uV(e) {
-  return new ay(e);
+  return new Yb(e);
 }
 function lV(e) {
-  return new iy(e);
+  return new Jb(e);
 }
 function cV(e) {
-  return new fy(e);
+  return new Zb(e);
 }
 function dV(e) {
-  return new py(e);
+  return new ay(e);
 }
 function pV(e) {
-  return new Up(e);
-}
-function hV(e) {
-  return new dy(e);
-}
-function fV(e) {
-  return new hy(e);
-}
-function mV(e) {
-  return new my(e);
-}
-function gV(e) {
-  return new gy(e);
-}
-function bV(e) {
-  return new by(e);
-}
-function yV(e) {
-  return new vy(e);
-}
-function vV(e) {
-  return new xy(e);
-}
-function xV(e) {
-  return new ky(e);
-}
-function wV(e) {
-  return new Cy(e);
-}
-function kV(e) {
-  return new Iy(e);
-}
-function IV(e) {
-  return new Sy(e);
-}
-function SV(e) {
-  return new wy(e);
-}
-function CV(e) {
-  return new Ny(e);
-}
-function NV(e) {
-  return new Ay(e);
-}
-function TV(e) {
-  return new Ey(e);
-}
-function $V(e) {
-  return new Ry(e);
-}
-function Hy(e) {
-  return new Fy(e);
-}
-function _V(e) {
-  return Hy(e);
-}
-function AV(e) {
-  return Hy(e);
-}
-function qy(e) {
-  return new Py(e);
-}
-function EV(e) {
-  return qy(e);
-}
-function RV(e) {
-  return qy(e);
-}
-function jy(e) {
-  return new My(e);
-}
-function DV(e) {
-  return jy(e);
-}
-function FV(e) {
-  return jy(e);
-}
-function OV(e) {
-  return new Ly(e);
-}
-function PV(e) {
-  return new Vy(e);
-}
-function GS(e) {
-  return new By(e);
-}
-function HS(e) {
-  return new Wy(e);
-}
-function qS(e) {
-  return new Dy(e);
-}
-function jS(e) {
-  return new Oy(e);
-}
-function zV(e) {
-  return new zy(e);
-}
-function MV(e) {
-  return new uy(e);
-}
-function LV(e) {
-  return new Bp(e);
-}
-function BV(e) {
-  return new ly(e);
-}
-function VV(e) {
-  return new jl(e);
-}
-function WV(e) {
-  return new oy(e);
-}
-function UV(e) {
   return new Lp(e);
 }
-function GV(e) {
-  return new cy(e);
+function hV(e) {
+  return new ny(e);
 }
-function HV(e) {
-  return new Wp(e);
+function fV(e) {
+  return new Bp(e);
 }
-function qV(e) {
-  return new Ar(e);
+function mV(e) {
+  return new sy(e);
 }
-function jV(e) {
-  return new Vp(e);
+function gV(e) {
+  return new ry(e);
 }
-function KV(e) {
-  return new Gy(e);
+function bV(e) {
+  return new iy(e);
 }
-function XV(e) {
-  return new Uy(e);
+function yV(e) {
+  return new oy(e);
 }
-var YV = GS;
-var QV = HS;
-var ZV = qS;
-var JV = jS;
-function eW(e) {
-  return new Ty(e);
+function vV(e) {
+  return new uy(e);
 }
-function tW(e) {
-  return new $y(e);
+function xV(e) {
+  return new gy(e);
 }
-function nW(e) {
-  return new _y(e);
+function wV(e) {
+  return new fy(e);
 }
-function sW(e) {
+function kV(e) {
+  return new Hp(e);
+}
+function IV(e) {
+  return new hy(e);
+}
+function SV(e) {
+  return new my(e);
+}
+function CV(e) {
+  return new by(e);
+}
+function NV(e) {
   return new yy(e);
 }
-var rW = {};
-Ae(rW, { MAPE: () => mW, MSE: () => yW, binaryAccuracy: () => aW, binaryCrossentropy: () => iW, categoricalAccuracy: () => uW, categoricalCrossentropy: () => lW, cosineProximity: () => pW, mape: () => gW, meanAbsoluteError: () => hW, meanAbsolutePercentageError: () => fW, meanSquaredError: () => bW, mse: () => vW, precision: () => cW, recall: () => dW, sparseCategoricalAccuracy: () => oW });
-function aW(e, t) {
-  return Lb(e, t);
+function TV(e) {
+  return new vy(e);
 }
-function iW(e, t) {
-  return rS(e, t);
+function $V(e) {
+  return new wy(e);
 }
-function oW(e, t) {
-  return aS(e, t);
+function _V(e) {
+  return new ky(e);
 }
-function uW(e, t) {
-  return Bb(e, t);
+function AV(e) {
+  return new Sy(e);
 }
-function lW(e, t) {
+function EV(e) {
+  return new Ty(e);
+}
+function RV(e) {
+  return new Cy(e);
+}
+function DV(e) {
+  return new Ny(e);
+}
+function FV(e) {
+  return new Iy(e);
+}
+function OV(e) {
+  return new $y(e);
+}
+function PV(e) {
+  return new Ry(e);
+}
+function zV(e) {
+  return new Dy(e);
+}
+function MV(e) {
+  return new Fy(e);
+}
+function jy(e) {
+  return new Py(e);
+}
+function LV(e) {
+  return jy(e);
+}
+function BV(e) {
+  return jy(e);
+}
+function Ky(e) {
+  return new My(e);
+}
+function VV(e) {
+  return Ky(e);
+}
+function WV(e) {
+  return Ky(e);
+}
+function Xy(e) {
+  return new By(e);
+}
+function UV(e) {
+  return Xy(e);
+}
+function GV(e) {
+  return Xy(e);
+}
+function HV(e) {
+  return new Vy(e);
+}
+function qV(e) {
+  return new Uy(e);
+}
+function XS(e) {
+  return new Wy(e);
+}
+function YS(e) {
+  return new Gy(e);
+}
+function QS(e) {
+  return new Oy(e);
+}
+function ZS(e) {
+  return new zy(e);
+}
+function jV(e) {
+  return new Ly(e);
+}
+function KV(e) {
+  return new cy(e);
+}
+function XV(e) {
+  return new Wp(e);
+}
+function YV(e) {
+  return new dy(e);
+}
+function QV(e) {
+  return new jl(e);
+}
+function ZV(e) {
+  return new ly(e);
+}
+function JV(e) {
+  return new Vp(e);
+}
+function eW(e) {
+  return new py(e);
+}
+function tW(e) {
+  return new Gp(e);
+}
+function nW(e) {
+  return new Er(e);
+}
+function sW(e) {
+  return new Up(e);
+}
+function rW(e) {
+  return new qy(e);
+}
+function aW(e) {
+  return new Hy(e);
+}
+var iW = XS;
+var oW = YS;
+var uW = QS;
+var lW = ZS;
+function cW(e) {
+  return new _y(e);
+}
+function dW(e) {
+  return new Ay(e);
+}
+function pW(e) {
+  return new Ey(e);
+}
+function hW(e) {
+  return new xy(e);
+}
+var fW = {};
+Ae(fW, { MAPE: () => CW, MSE: () => $W, binaryAccuracy: () => mW, binaryCrossentropy: () => gW, categoricalAccuracy: () => yW, categoricalCrossentropy: () => vW, cosineProximity: () => kW, mape: () => NW, meanAbsoluteError: () => IW, meanAbsolutePercentageError: () => SW, meanSquaredError: () => TW, mse: () => _W, precision: () => xW, recall: () => wW, sparseCategoricalAccuracy: () => bW });
+function mW(e, t) {
   return Vb(e, t);
 }
-function cW(e, t) {
-  return sS(e, t);
-}
-function dW(e, t) {
-  return QL(e, t);
-}
-function pW(e, t) {
-  return Mb(e, t);
-}
-function hW(e, t) {
-  return Op(e, t);
-}
-function fW(e, t) {
-  return Zo(e, t);
-}
-function mW(e, t) {
-  return Zo(e, t);
-}
 function gW(e, t) {
-  return Zo(e, t);
+  return lS(e, t);
 }
 function bW(e, t) {
-  return gi(e, t);
+  return cS(e, t);
 }
 function yW(e, t) {
-  return gi(e, t);
+  return Wb(e, t);
 }
 function vW(e, t) {
-  return gi(e, t);
+  return Ub(e, t);
 }
-var xW = {};
-Ae(xW, { modelFromJSON: () => EB });
-var wW = {};
-Ae(wW, { l1: () => IW, l1l2: () => kW, l2: () => SW });
-function kW(e) {
+function xW(e, t) {
+  return uS(e, t);
+}
+function wW(e, t) {
+  return dB(e, t);
+}
+function kW(e, t) {
+  return Bb(e, t);
+}
+function IW(e, t) {
+  return zp(e, t);
+}
+function SW(e, t) {
+  return Jo(e, t);
+}
+function CW(e, t) {
+  return Jo(e, t);
+}
+function NW(e, t) {
+  return Jo(e, t);
+}
+function TW(e, t) {
+  return vi(e, t);
+}
+function $W(e, t) {
+  return vi(e, t);
+}
+function _W(e, t) {
+  return vi(e, t);
+}
+var AW = {};
+Ae(AW, { modelFromJSON: () => VB });
+var EW = {};
+Ae(EW, { l1: () => DW, l1l2: () => RW, l2: () => FW });
+function RW(e) {
   return new Gl(e);
 }
-function IW(e) {
-  return PB(e);
+function DW(e) {
+  return qB(e);
 }
-function SW(e) {
-  return zB(e);
+function FW(e) {
+  return jB(e);
 }
-var CW = class extends Ji {
+var OW = class extends to {
   constructor() {
     super(...arguments), this.model = null;
   }
   setModel(e) {
-    if (!(e instanceof pr))
+    if (!(e instanceof hr))
       throw new Error("model must be a LayersModel, not some other Container");
     this.model = e;
   }
@@ -16716,20 +16774,20 @@ var CW = class extends Ji {
 function jc(e, t) {
   return e < t;
 }
-function Gx(e, t) {
+function Hx(e, t) {
   return e > t;
 }
-var NW = class extends CW {
+var PW = class extends OW {
   constructor(e) {
     if (super(), e == null && (e = {}), e.restoreBestWeights)
       throw new Fe("restoreBestWeights = True is not implemented in EarlyStopping yet.");
-    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = jc : this.mode === "max" ? this.monitorFunc = Gx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Gx : this.monitorFunc = jc, this.monitorFunc === jc && (this.minDelta *= -1);
+    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = jc : this.mode === "max" ? this.monitorFunc = Hx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Hx : this.monitorFunc = jc, this.monitorFunc === jc && (this.minDelta *= -1);
   }
   async onTrainBegin(e) {
     this.wait = 0, this.stoppedEpoch = 0, this.baseline != null ? this.best = this.baseline : this.best = this.monitorFunc === jc ? 1 / 0 : -1 / 0;
   }
   async onEpochEnd(e, t) {
-    await rr(t);
+    await ar(t);
     let n = this.getMonitorValue(t);
     n != null && (this.monitorFunc(n - this.minDelta, this.best) ? (this.best = n, this.wait = 0) : (this.wait++, this.wait >= this.patience && (this.stoppedEpoch = e, this.model.stopTraining = true)));
   }
@@ -16742,32 +16800,32 @@ var NW = class extends CW {
     return t == null && console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(e)}`), t;
   }
 };
-function TW(e) {
-  return new NW(e);
+function zW(e) {
+  return new PW(e);
 }
-var Ape = { earlyStopping: TW };
-var $W = X();
-$W.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (e) => {
+var Gpe = { earlyStopping: zW };
+var MW = X();
+MW.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (e) => {
   e && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
 });
-var KS = ((e) => (e[e.DT_INVALID = 0] = "DT_INVALID", e[e.DT_FLOAT = 1] = "DT_FLOAT", e[e.DT_DOUBLE = 2] = "DT_DOUBLE", e[e.DT_INT32 = 3] = "DT_INT32", e[e.DT_UINT8 = 4] = "DT_UINT8", e[e.DT_INT16 = 5] = "DT_INT16", e[e.DT_INT8 = 6] = "DT_INT8", e[e.DT_STRING = 7] = "DT_STRING", e[e.DT_COMPLEX64 = 8] = "DT_COMPLEX64", e[e.DT_INT64 = 9] = "DT_INT64", e[e.DT_BOOL = 10] = "DT_BOOL", e[e.DT_QINT8 = 11] = "DT_QINT8", e[e.DT_QUINT8 = 12] = "DT_QUINT8", e[e.DT_QINT32 = 13] = "DT_QINT32", e[e.DT_BFLOAT16 = 14] = "DT_BFLOAT16", e[e.DT_QINT16 = 15] = "DT_QINT16", e[e.DT_QUINT16 = 16] = "DT_QUINT16", e[e.DT_UINT16 = 17] = "DT_UINT16", e[e.DT_COMPLEX128 = 18] = "DT_COMPLEX128", e[e.DT_HALF = 19] = "DT_HALF", e[e.DT_RESOURCE = 20] = "DT_RESOURCE", e[e.DT_VARIANT = 21] = "DT_VARIANT", e[e.DT_UINT32 = 22] = "DT_UINT32", e[e.DT_UINT64 = 23] = "DT_UINT64", e[e.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", e[e.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", e[e.DT_INT32_REF = 103] = "DT_INT32_REF", e[e.DT_UINT8_REF = 104] = "DT_UINT8_REF", e[e.DT_INT16_REF = 105] = "DT_INT16_REF", e[e.DT_INT8_REF = 106] = "DT_INT8_REF", e[e.DT_STRING_REF = 107] = "DT_STRING_REF", e[e.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", e[e.DT_INT64_REF = 109] = "DT_INT64_REF", e[e.DT_BOOL_REF = 110] = "DT_BOOL_REF", e[e.DT_QINT8_REF = 111] = "DT_QINT8_REF", e[e.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", e[e.DT_QINT32_REF = 113] = "DT_QINT32_REF", e[e.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", e[e.DT_QINT16_REF = 115] = "DT_QINT16_REF", e[e.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", e[e.DT_UINT16_REF = 117] = "DT_UINT16_REF", e[e.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", e[e.DT_HALF_REF = 119] = "DT_HALF_REF", e[e.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", e[e.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", e[e.DT_UINT32_REF = 122] = "DT_UINT32_REF", e[e.DT_UINT64_REF = 123] = "DT_UINT64_REF", e))(KS || {});
-var Hx;
+var JS = ((e) => (e[e.DT_INVALID = 0] = "DT_INVALID", e[e.DT_FLOAT = 1] = "DT_FLOAT", e[e.DT_DOUBLE = 2] = "DT_DOUBLE", e[e.DT_INT32 = 3] = "DT_INT32", e[e.DT_UINT8 = 4] = "DT_UINT8", e[e.DT_INT16 = 5] = "DT_INT16", e[e.DT_INT8 = 6] = "DT_INT8", e[e.DT_STRING = 7] = "DT_STRING", e[e.DT_COMPLEX64 = 8] = "DT_COMPLEX64", e[e.DT_INT64 = 9] = "DT_INT64", e[e.DT_BOOL = 10] = "DT_BOOL", e[e.DT_QINT8 = 11] = "DT_QINT8", e[e.DT_QUINT8 = 12] = "DT_QUINT8", e[e.DT_QINT32 = 13] = "DT_QINT32", e[e.DT_BFLOAT16 = 14] = "DT_BFLOAT16", e[e.DT_QINT16 = 15] = "DT_QINT16", e[e.DT_QUINT16 = 16] = "DT_QUINT16", e[e.DT_UINT16 = 17] = "DT_UINT16", e[e.DT_COMPLEX128 = 18] = "DT_COMPLEX128", e[e.DT_HALF = 19] = "DT_HALF", e[e.DT_RESOURCE = 20] = "DT_RESOURCE", e[e.DT_VARIANT = 21] = "DT_VARIANT", e[e.DT_UINT32 = 22] = "DT_UINT32", e[e.DT_UINT64 = 23] = "DT_UINT64", e[e.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", e[e.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", e[e.DT_INT32_REF = 103] = "DT_INT32_REF", e[e.DT_UINT8_REF = 104] = "DT_UINT8_REF", e[e.DT_INT16_REF = 105] = "DT_INT16_REF", e[e.DT_INT8_REF = 106] = "DT_INT8_REF", e[e.DT_STRING_REF = 107] = "DT_STRING_REF", e[e.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", e[e.DT_INT64_REF = 109] = "DT_INT64_REF", e[e.DT_BOOL_REF = 110] = "DT_BOOL_REF", e[e.DT_QINT8_REF = 111] = "DT_QINT8_REF", e[e.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", e[e.DT_QINT32_REF = 113] = "DT_QINT32_REF", e[e.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", e[e.DT_QINT16_REF = 115] = "DT_QINT16_REF", e[e.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", e[e.DT_UINT16_REF = 117] = "DT_UINT16_REF", e[e.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", e[e.DT_HALF_REF = 119] = "DT_HALF_REF", e[e.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", e[e.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", e[e.DT_UINT32_REF = 122] = "DT_UINT32_REF", e[e.DT_UINT64_REF = 123] = "DT_UINT64_REF", e))(JS || {});
+var qx;
 ((e) => {
   let t;
   ((n) => {
     n[n.LEGACY = 0] = "LEGACY", n[n.V1 = 1] = "V1", n[n.V2 = 2] = "V2";
   })(t = e.CheckpointFormatVersion || (e.CheckpointFormatVersion = {}));
-})(Hx || (Hx = {}));
-var Ky = {};
-function Epe(e, t) {
+})(qx || (qx = {}));
+var Yy = {};
+function Hpe(e, t) {
   let n = { tfOpName: e, category: "custom", inputs: [], attrs: [], customExecutor: t };
-  Ky[e] = n;
+  Yy[e] = n;
 }
-function XS(e) {
-  return Ky[e];
+function e0(e) {
+  return Yy[e];
 }
-function Rpe(e) {
-  delete Ky[e];
+function qpe(e) {
+  delete Yy[e];
 }
 function I(e, t, n, s, r) {
   let a = t.inputParams[e];
@@ -16790,17 +16848,17 @@ function un(e, t, n, s) {
     if (o != null)
       return o;
   }
-  let i = n.currentContextIds.find((o) => !!t[Ed(r, o)]);
-  return i !== void 0 ? t[Ed(r, i)][a] : void 0;
+  let i = n.currentContextIds.find((o) => !!t[Dd(r, o)]);
+  return i !== void 0 ? t[Dd(r, i)][a] : void 0;
 }
-function _W(e, t, n) {
-  return t[Ed(e, n.currentContextId)];
+function LW(e, t, n) {
+  return t[Dd(e, n.currentContextId)];
 }
 function Ts(e, t) {
   let [n, s, r] = An(e);
-  return [Ed(n, t && t.currentContextId), s, r];
+  return [Dd(n, t && t.currentContextId), s, r];
 }
-function Ed(e, t) {
+function Dd(e, t) {
   return t ? `${e}-${t}` : e;
 }
 function An(e) {
@@ -16822,71 +16880,71 @@ function sd(e, t, n) {
   return s;
 }
 function Us(e) {
-  return e.kept ? e : lr(e);
+  return e.kept ? e : cr(e);
 }
-var YS = {};
-Ae(YS, { json: () => AW });
-var AW = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
-var QS = {};
-Ae(QS, { json: () => EW });
-var EW = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ClipByValue", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "clipValueMin", type: "number" }, { start: 2, name: "clipValueMax", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Complex", category: "basic_math", inputs: [{ start: 0, name: "real", type: "tensor" }, { start: 1, name: "imag", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ComplexAbs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Cosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log1p", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Reciprocal", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Softplus", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acosh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Erf", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Prod", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axes", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LeakyRelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "alpha", name: "alpha", type: "number", defaultValue: 0.2 }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "IsNan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
-var ZS = {};
-Ae(ZS, { json: () => RW });
-var RW = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, 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{ tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: 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-var JS = {};
-Ae(JS, { json: () => DW });
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"MaxPoolWithArgmax", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "include_batch_in_index", name: "includeBatchInIndex", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AvgPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Conv1D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "stride", name: "stride", type: "number" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NWC" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "dilation", name: "dilation", type: "number", defaultValue: 1 }] }, { tfOpName: "Conv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "_FusedConv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "use_cudnn_on_gpu", name: "useCudnnOnGpu", type: "bool", defaultValue: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number" }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { tfOpName: "DepthwiseConv2d", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "DepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "FusedDepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }] }, { tfOpName: "Conv3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "Dilation2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "rates", name: "dilations", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }] }];
-var e0 = {};
-Ae(e0, { json: () => FW });
-var FW = [{ tfOpName: "Fill", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }, { start: 1, name: "value", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "LinSpace", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "num", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "OneHot", category: "creation", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "depth", type: "number" }, { start: 2, name: "onValue", type: "number", defaultValue: 1 }, { start: 3, name: "offValue", type: "number", defaultValue: 0 }], attrs: [{ tfName: "axis", name: "axis", type: "number", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ones", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "OnesLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "RandomUniform", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Range", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "step", type: "number", defaultValue: 0 }], attrs: [{ tfName: "Tidx", name: "dtype", type: "dtype" }] }, { tfOpName: "TruncatedNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "means", name: "mean", type: "number", defaultValue: 0 }, { tfName: "stddev", name: "stdDev", type: "number", defaultValue: 1 }, { tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Zeros", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "ZerosLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Multinomial", category: "creation", inputs: [{ start: 0, name: "logits", type: "tensor" }, { start: 1, name: "numSamples", type: "number" }], attrs: [{ tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number" }, { tfName: "T", name: "dtype", type: "dtype" }, { tfName: "output_dtype", name: "output_dtype", type: "dtype" }] }];
 var t0 = {};
-Ae(t0, { json: () => OW });
-var OW = [{ tfOpName: "NonMaxSuppressionV2", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV3", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV4", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "T_threshold", name: "threshold", type: "dtype", notSupported: true }, { tfName: "pad_to_max_output_size", name: "padToMaxOutputSize", type: "bool" }] }, { tfOpName: "NonMaxSuppressionV5", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }, { start: 5, name: "softNmsSigma", type: "number" }] }, { tfOpName: "Where", category: "dynamic", inputs: [{ start: 0, name: "condition", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ListDiff", category: "dynamic", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
+Ae(t0, { json: () => BW });
+var BW = [{ tfOpName: "Add", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddV2", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AddN", category: "arithmetic", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "BiasAdd", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "Sub", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "RealDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Div", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "DivNoNan", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorDiv", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mul", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Maximum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Minimum", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Pow", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SquaredDifference", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Mod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "FloorMod", category: "arithmetic", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var n0 = {};
-Ae(n0, { json: () => PW });
-var PW = [{ tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }];
+Ae(n0, { json: () => VW });
+var VW = [{ tfOpName: "Abs", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Acos", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Asin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Atan2", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ceil", category: "basic_math", inputs: [{ start: 0, name: "x", type: 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[{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Elu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Exp", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Floor", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Log", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Imag", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Neg", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Real", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "Tout", name: "outputType", type: "dtype", notSupported: true }] }, { tfOpName: "Prelu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "alpha", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Relu6", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Selu", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sigmoid", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sin", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sinh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Rsqrt", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Square", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tan", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Tanh", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Sign", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Round", category: "basic_math", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Expm1", category: 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"tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var s0 = {};
-Ae(s0, { json: () => zW });
-var zW = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "min", name: "min", type: "number" }, { tfName: "max", name: "max", type: "number" }] }];
+Ae(s0, { json: () => WW });
+var WW = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, 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{ tfOpName: "StatelessIf", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "If", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "StatelessWhile", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "While", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPopBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListPushBack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListLength", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }] }, { tfOpName: "TensorListResize", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "size", type: "number" }] }];
 var r0 = {};
-Ae(r0, { json: () => MW });
-var MW = [{ tfOpName: "HashTable", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "HashTableV2", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "LookupTableImport", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableImportV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFind", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFindV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }];
+Ae(r0, { json: () => UW });
+var UW = [{ tfOpName: "AvgPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [], notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPoolWithArgmax", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "include_batch_in_index", name: "includeBatchInIndex", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "AvgPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MaxPool3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "ksize", name: "kernelSize", type: "number[]" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Conv1D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "stride", name: "stride", type: "number" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NWC" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "dilation", name: "dilation", type: "number", defaultValue: 1 }] }, { tfOpName: "Conv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "useCudnnOnGpu", name: "useCudnnOnGpu", type: "bool" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "_FusedConv2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "use_cudnn_on_gpu", name: "useCudnnOnGpu", type: "bool", defaultValue: true }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "leakyrelu_alpha", name: "leakyreluAlpha", type: "number" }] }, { tfOpName: "Conv2DBackpropInput", category: "convolution", inputs: [{ start: 2, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 0, name: "outputShape", type: "number[]" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]", notSupported: true }] }, { tfOpName: "DepthwiseConv2d", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "DepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "FusedDepthwiseConv2dNative", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]", defaultValue: [1, 1, 1, 1] }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "explicit_paddings", name: "explicitPaddings", type: "number[]", defaultValue: [] }] }, { tfOpName: "Conv3D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }, { tfName: "data_format", name: "dataFormat", type: "string", defaultValue: "NHWC" }, { tfName: "dilations", name: "dilations", type: "number[]" }] }, { tfOpName: "Dilation2D", category: "convolution", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "filter", type: "tensor" }], attrs: [{ tfName: "strides", name: "strides", type: "number[]" }, { tfName: "rates", name: "dilations", type: "number[]" }, { tfName: "padding", name: "pad", type: "string" }] }];
 var a0 = {};
-Ae(a0, { json: () => LW });
-var LW = [{ tfOpName: "ResizeBilinear", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ResizeNearestNeighbor", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "CropAndResize", category: "image", inputs: [{ start: 0, name: "image", type: "tensor" }, { start: 1, name: "boxes", type: "tensor" }, { start: 2, name: "boxInd", type: "tensor" }, { start: 3, name: "cropSize", type: "number[]" }], attrs: [{ tfName: "method", name: "method", type: "string" }, { tfName: "extrapolation_value", name: "extrapolationValue", type: "number" }] }, { tfOpName: "ImageProjectiveTransformV3", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "transforms", type: "tensor" }, { start: 2, name: "outputShape", type: "number[]" }, { start: 3, name: "fillValue", type: "number" }], attrs: [{ tfName: "interpolation", name: "interpolation", type: "string" }, { tfName: "fill_mode", name: "fillMode", type: "string" }] }];
+Ae(a0, { json: () => GW });
+var GW = [{ tfOpName: "Fill", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }, { start: 1, name: "value", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "LinSpace", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "num", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "OneHot", category: "creation", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "depth", type: "number" }, { start: 2, name: "onValue", type: "number", defaultValue: 1 }, { start: 3, name: "offValue", type: "number", defaultValue: 0 }], attrs: [{ tfName: "axis", name: "axis", type: "number", notSupported: true }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Ones", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "OnesLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "RandomUniform", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "minval", name: "minval", type: "number", defaultValue: 0 }, { tfName: "maxval", name: "maxval", type: "number", defaultValue: 1 }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "seed", name: "seed", type: "number", defaultValue: 0 }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Range", category: "creation", inputs: [{ start: 0, name: "start", type: "number" }, { start: 1, name: "stop", type: "number" }, { start: 2, name: "step", type: "number", defaultValue: 0 }], attrs: [{ tfName: "Tidx", name: "dtype", type: "dtype" }] }, { tfOpName: "TruncatedNormal", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "means", name: "mean", type: "number", defaultValue: 0 }, { tfName: "stddev", name: "stdDev", type: "number", defaultValue: 1 }, { tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number", defaultValue: 0, notSupported: true }, { tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "T", name: "T", type: "number", notSupported: true }] }, { tfOpName: "Zeros", category: "creation", inputs: [{ start: 0, name: "shape", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "ZerosLike", category: "creation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "Multinomial", category: "creation", inputs: [{ start: 0, name: "logits", type: "tensor" }, { start: 1, name: "numSamples", type: "number" }], attrs: [{ tfName: "seed", name: "seed", type: "number" }, { tfName: "seed2", name: "seed2", type: "number" }, { tfName: "T", name: "dtype", type: "dtype" }, { tfName: "output_dtype", name: "output_dtype", type: "dtype" }] }];
 var i0 = {};
-Ae(i0, { json: () => BW });
-var BW = [{ tfOpName: "Equal", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NotEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Greater", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "GreaterEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Less", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LessEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalAnd", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalOr", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Select", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SelectV2", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
+Ae(i0, { json: () => HW });
+var HW = [{ tfOpName: "NonMaxSuppressionV2", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV3", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }] }, { tfOpName: "NonMaxSuppressionV4", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "T_threshold", name: "threshold", type: "dtype", notSupported: true }, { tfName: "pad_to_max_output_size", name: "padToMaxOutputSize", type: "bool" }] }, { tfOpName: "NonMaxSuppressionV5", category: "dynamic", inputs: [{ start: 0, name: "boxes", type: "tensor" }, { start: 1, name: "scores", type: "tensor" }, { start: 2, name: "maxOutputSize", type: "number" }, { start: 3, name: "iouThreshold", type: "number" }, { start: 4, name: "scoreThreshold", type: "number" }, { start: 5, name: "softNmsSigma", type: "number" }] }, { tfOpName: "Where", category: "dynamic", inputs: [{ start: 0, name: "condition", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ListDiff", category: "dynamic", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "y", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var o0 = {};
-Ae(o0, { json: () => VW });
-var VW = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMulV2", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Transpose", category: "matrices", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "perm", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Einsum", category: "matrices", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "equation", name: "equation", type: "string" }, { tfName: "N", name: "n", type: "number", defaultValue: 2 }, { tfName: "T", name: "dtype", type: "dtype" }] }];
+Ae(o0, { json: () => qW });
+var qW = [{ tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }];
 var u0 = {};
-Ae(u0, { json: () => WW });
-var WW = [{ tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "normalization", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: true, notSupported: true }] }];
+Ae(u0, { json: () => jW });
+var jW = [{ tfOpName: "PlaceholderWithDefault", category: "graph", inputs: [{ start: 0, name: "default", type: "tensor" }], attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Placeholder", category: "graph", attrs: [{ tfName: "shape", name: "shape", type: "shape" }, { tfName: "dtype", name: "dtype", type: "dtype" }] }, { tfOpName: "Const", category: "graph" }, { tfOpName: "Identity", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IdentityN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Snapshot", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Rank", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Size", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "Shape", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "ShapeN", category: "graph", inputs: [{ start: 0, end: 0, name: "x", type: "tensors" }] }, { tfOpName: "Print", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "data", type: "tensors" }], attrs: [{ tfName: "message", name: "message", type: "string" }, { tfName: "first_n", name: "firstN", type: "number", notSupported: true }, { tfName: "summarize", name: "summarize", type: "number", defaultValue: 3 }] }, { tfOpName: "NoOp", category: "graph", inputs: [] }, { tfOpName: "StopGradient", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "FakeQuantWithMinMaxVars", category: "graph", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "min", name: "min", type: "number" }, { tfName: "max", name: "max", type: "number" }] }];
 var l0 = {};
-Ae(l0, { json: () => UW });
-var UW = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Cumprod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }];
+Ae(l0, { json: () => KW });
+var KW = [{ tfOpName: "HashTable", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "HashTableV2", category: "hash_table", inputs: [], attrs: [{ tfName: "shared_name", name: "sharedName", type: "string" }, { tfName: "use_node_name_sharing", name: "useNodeNameSharing", type: "bool" }, { tfName: "key_dtype", name: "keyDType", type: "dtype" }, { tfName: "value_dtype", name: "valueDType", type: "dtype" }] }, { tfOpName: "LookupTableImport", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableImportV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "values", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFind", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableFindV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }, { start: 1, name: "keys", type: "tensor" }, { start: 2, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "Tin", name: "tIn", type: "dtype", notSupported: true }, { tfName: "Tout", name: "tOut", type: "dtype", notSupported: true }] }, { tfOpName: "LookupTableSize", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }, { tfOpName: "LookupTableSizeV2", category: "hash_table", inputs: [{ start: 0, name: "tableHandle", type: "tensor" }] }];
 var c0 = {};
-Ae(c0, { json: () => GW });
-var GW = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: true }] }, { tfOpName: "Reverse", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "dims", type: "bool[]" }] }, { tfOpName: "ReverseV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }] }, { tfOpName: "Slice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "size", type: "number[]" }] }, { tfOpName: "StridedSlice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "end", type: "number[]" }, { start: 3, name: "strides", type: "number[]" }], attrs: [{ tfName: "begin_mask", name: "beginMask", type: "number", defaultValue: 0 }, { tfName: "end_mask", name: "endMask", type: "number", defaultValue: 0 }, { tfName: "new_axis_mask", name: "newAxisMask", type: "number", defaultValue: 0 }, { tfName: "ellipsis_mask", name: "ellipsisMask", type: "number", defaultValue: 0 }, { tfName: "shrink_axis_mask", name: "shrinkAxisMask", type: "number", defaultValue: 0 }] }, { tfOpName: "Pack", category: "slice_join", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "Unpack", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }, { tfName: "num", name: "num", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Tile", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "reps", type: "number[]" }] }, { tfOpName: "Split", category: "slice_join", inputs: [{ start: 0, name: "axis", type: "number", defaultValue: 0 }, { start: 1, name: "x", type: "tensor" }], attrs: [{ tfName: "num_split", name: "numOrSizeSplits", type: "number", defaultValue: 1 }] }, { tfOpName: "SplitV", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "numOrSizeSplits", type: "number[]" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "ScatterNd", category: "slice_join", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "shape", type: "number[]" }] }, { tfOpName: "GatherNd", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "slice_join", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: false, notSupported: true }] }];
+Ae(c0, { json: () => XW });
+var XW = [{ tfOpName: "ResizeBilinear", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "ResizeNearestNeighbor", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "size", type: "number[]" }], attrs: [{ tfName: "align_corners", name: "alignCorners", type: "bool" }, { tfName: "half_pixel_centers", name: "halfPixelCenters", type: "bool" }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "CropAndResize", category: "image", inputs: [{ start: 0, name: "image", type: "tensor" }, { start: 1, name: "boxes", type: "tensor" }, { start: 2, name: "boxInd", type: "tensor" }, { start: 3, name: "cropSize", type: "number[]" }], attrs: [{ tfName: "method", name: "method", type: "string" }, { tfName: "extrapolation_value", name: "extrapolationValue", type: "number" }] }, { tfOpName: "ImageProjectiveTransformV3", category: "image", inputs: [{ start: 0, name: "images", type: "tensor" }, { start: 1, name: "transforms", type: "tensor" }, { start: 2, name: "outputShape", type: "number[]" }, { start: 3, name: "fillValue", type: "number" }], attrs: [{ tfName: "interpolation", name: "interpolation", type: "string" }, { tfName: "fill_mode", name: "fillMode", type: "string" }] }];
 var d0 = {};
-Ae(d0, { json: () => HW });
-var HW = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }];
+Ae(d0, { json: () => YW });
+var YW = [{ tfOpName: "Equal", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NotEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Greater", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "GreaterEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Less", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LessEqual", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalAnd", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalNot", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "LogicalOr", category: "logical", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Select", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SelectV2", category: "logical", inputs: [{ start: 0, name: "condition", type: "tensor" }, { start: 1, name: "a", type: "tensor" }, { start: 2, name: "b", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }];
 var p0 = {};
-Ae(p0, { json: () => qW });
-var qW = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }];
+Ae(p0, { json: () => QW });
+var QW = [{ tfOpName: "_FusedMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }, { start: 2, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "num_args", name: "numArgs", type: "number" }, { tfName: "fused_ops", name: "fusedOps", type: "string[]", defaultValue: [] }, { tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-4 }, { tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "MatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "transpose_a", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "transpose_b", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMul", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "BatchMatMulV2", category: "matrices", inputs: [{ start: 0, name: "a", type: "tensor" }, { start: 1, name: "b", type: "tensor" }], attrs: [{ tfName: "adj_x", name: "transposeA", type: "bool", defaultValue: false }, { tfName: "adj_y", name: "transposeB", type: "bool", defaultValue: false }, { tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Transpose", category: "matrices", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "perm", type: "number[]" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "Einsum", category: "matrices", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "equation", name: "equation", type: "string" }, { tfName: "N", name: "n", type: "number", defaultValue: 2 }, { tfName: "T", name: "dtype", type: "dtype" }] }];
 var h0 = {};
-Ae(h0, { json: () => jW });
-var jW = [{ tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }];
+Ae(h0, { json: () => ZW });
+var ZW = [{ tfOpName: "FusedBatchNorm", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV2", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "FusedBatchNormV3", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "scale", type: "tensor" }, { start: 2, name: "offset", type: "tensor" }, { start: 3, name: "mean", type: "tensor" }, { start: 4, name: "variance", type: "tensor" }], attrs: [{ tfName: "epsilon", name: "epsilon", type: "number", defaultValue: 1e-3 }, { tfName: "data_format", name: "dataFormat", type: "string", notSupported: true }] }, { tfOpName: "LRN", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "depth_radius", name: "radius", type: "number", defaultValue: 5 }, { tfName: "bias", name: "bias", type: "number", defaultValue: 1 }, { tfName: "alpha", name: "alpha", type: "number", defaultValue: 1 }, { tfName: "beta", name: "beta", type: "number", defaultValue: 0.5 }] }, { tfOpName: "Softmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "LogSoftmax", category: "normalization", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "normalization", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: true, notSupported: true }] }];
 var f0 = {};
-Ae(f0, { json: () => KW });
-var KW = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }];
-var qx = class {
+Ae(f0, { json: () => JW });
+var JW = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Cumprod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }];
+var m0 = {};
+Ae(m0, { json: () => e4 });
+var e4 = [{ tfOpName: "ConcatV2", category: "slice_join", inputs: [{ start: 0, end: -1, name: "tensors", type: "tensors" }, { start: -1, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "Concat", category: "slice_join", inputs: [{ start: 1, end: 0, name: "tensors", type: "tensors" }, { start: 0, name: "axis", type: "number" }], attrs: [{ tfName: "N", name: "n", type: "number", defaultValue: 2 }] }, { tfOpName: "GatherV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }], attrs: [{ tfName: "batch_dims", name: "batchDims", type: "number", defaultValue: 0 }] }, { tfOpName: "Gather", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", notSupported: true }] }, { tfOpName: "Reverse", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "dims", type: "bool[]" }] }, { tfOpName: "ReverseV2", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }] }, { tfOpName: "Slice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "size", type: "number[]" }] }, { tfOpName: "StridedSlice", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "begin", type: "number[]" }, { start: 2, name: "end", type: "number[]" }, { start: 3, name: "strides", type: "number[]" }], attrs: [{ tfName: "begin_mask", name: "beginMask", type: "number", defaultValue: 0 }, { tfName: "end_mask", name: "endMask", type: "number", defaultValue: 0 }, { tfName: "new_axis_mask", name: "newAxisMask", type: "number", defaultValue: 0 }, { tfName: "ellipsis_mask", name: "ellipsisMask", type: "number", defaultValue: 0 }, { tfName: "shrink_axis_mask", name: "shrinkAxisMask", type: "number", defaultValue: 0 }] }, { tfOpName: "Pack", category: "slice_join", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "Unpack", category: "slice_join", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "axis", name: "axis", type: "number", defaultValue: 0 }, { tfName: "num", name: "num", type: "number", defaultValue: 0, notSupported: true }] }, { tfOpName: "Tile", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "reps", type: "number[]" }] }, { tfOpName: "Split", category: "slice_join", inputs: [{ start: 0, name: "axis", type: "number", defaultValue: 0 }, { start: 1, name: "x", type: "tensor" }], attrs: [{ tfName: "num_split", name: "numOrSizeSplits", type: "number", defaultValue: 1 }] }, { tfOpName: "SplitV", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "numOrSizeSplits", type: "number[]" }, { start: 2, name: "axis", type: "number", defaultValue: 0 }] }, { tfOpName: "ScatterNd", category: "slice_join", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "shape", type: "number[]" }] }, { tfOpName: "GatherNd", category: "slice_join", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }] }, { tfOpName: "SparseToDense", category: "slice_join", inputs: [{ start: 0, name: "sparseIndices", type: "tensor" }, { start: 1, name: "outputShape", type: "number[]" }, { start: 2, name: "sparseValues", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }], attrs: [{ tfName: "validate_indices", name: "validateIndices", type: "bool", defaultValue: false, notSupported: true }] }];
+var g0 = {};
+Ae(g0, { json: () => t4 });
+var t4 = [{ tfOpName: "SparseFillEmptyRows", category: "sparse", inputs: [{ start: 0, name: "indices", type: "tensor" }, { start: 1, name: "values", type: "tensor" }, { start: 2, name: "denseShape", type: "tensor" }, { start: 3, name: "defaultValue", type: "tensor" }] }, { tfOpName: "SparseReshape", category: "sparse", inputs: [{ start: 0, name: "inputIndices", type: "tensor" }, { start: 1, name: "inputShape", type: "tensor" }, { start: 2, name: "newShape", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "SparseSegmentMean", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }, { tfOpName: "SparseSegmentSum", category: "sparse", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "indices", type: "tensor" }, { start: 2, name: "segmentIds", type: "tensor" }] }];
+var b0 = {};
+Ae(b0, { json: () => n4 });
+var n4 = [{ tfOpName: "FFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "IFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "RFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }, { tfOpName: "IRFFT", category: "spectral", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "fft_length", type: "number", notSupported: true }] }];
+var y0 = {};
+Ae(y0, { json: () => s4 });
+var s4 = [{ tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "dataSplits", type: "tensor" }], attrs: [{ tfName: "separator", name: "separator", type: "string" }, { tfName: "ngram_widths", name: "nGramWidths", type: "number[]" }, { tfName: "left_pad", name: "leftPad", type: "string" }, { tfName: "right_pad", name: "rightPad", type: "string" }, { tfName: "pad_width", name: "padWidth", type: "number" }, { tfName: "preserve_short_sequences", name: "preserveShortSequences", type: "bool" }], outputs: ["ngrams", "ngrams_splits"] }, { tfOpName: "StringSplit", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }, { start: 1, name: "delimiter", type: "tensor" }], attrs: [{ tfName: "skip_empty", name: "skipEmpty", type: "bool" }], outputs: ["indices", "values", "shape"] }, { tfOpName: "StringToHashBucketFast", category: "string", inputs: [{ start: 0, name: "input", type: "tensor" }], attrs: [{ tfName: "num_buckets", name: "numBuckets", type: "number" }] }];
+var v0 = {};
+Ae(v0, { json: () => r4 });
+var r4 = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }];
+var jx = class {
   static get Instance() {
     return this._instance || (this._instance = new this());
   }
   constructor() {
-    let e = [YS, QS, ZS, JS, e0, t0, n0, s0, r0, a0, i0, o0, u0, l0, c0, d0, p0, h0, f0], t = [].concat(...e.map((n) => n.json));
+    let e = [t0, n0, s0, r0, a0, i0, o0, u0, l0, c0, d0, p0, h0, f0, m0, g0, b0, y0, v0], t = [].concat(...e.map((n) => n.json));
     this.opMappers = t.reduce((n, s) => (n[s.tfOpName] = s, n), {});
   }
   transformGraph(e, t = {}) {
@@ -16925,44 +16983,44 @@ var qx = class {
     return Object.keys(e || {}).reduce((t, n) => (t[e[n].name] = n, t), {});
   }
   mapNode(e) {
-    let t = XS(e.op) || this.opMappers[e.op] || {};
+    let t = e0(e.op) || this.opMappers[e.op] || {};
     e.attr == null && (e.attr = {});
     let n = { name: e.name, op: e.op, category: t.category, inputNames: (e.input || []).map((s) => s.startsWith("^") ? s.slice(1) : s), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t.outputs };
     return t.inputs != null && (n.inputParams = t.inputs.reduce((s, r) => (s[r.name] = { type: r.type, inputIndexStart: r.start, inputIndexEnd: r.end }, s), {})), t.attrs != null && (n.attrParams = t.attrs.reduce((s, r) => {
       let a = r.type, i;
       switch (r.type) {
         case "string":
-          i = Nm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Nm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          i = $m(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = $m(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "string[]":
-          i = Dm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Dm(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "number":
-          i = $m(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = $m(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "number[]":
-          i = Rm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Rm(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "bool":
-          i = Tm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Tm(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "bool[]":
           i = Om(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Om(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
-        case "shape":
-          i = Em(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Em(e.attr, r.tfDeprecatedName, r.defaultValue));
+        case "number":
+          i = Am(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = Am(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
-        case "shape[]":
+        case "number[]":
           i = Fm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Fm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
-        case "dtype":
+        case "bool":
           i = _m(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = _m(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
+        case "bool[]":
+          i = zm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = zm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
+        case "shape":
+          i = Dm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Dm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
+        case "shape[]":
+          i = Pm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Pm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
+        case "dtype":
+          i = Em(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Em(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
         case "dtype[]":
-          i = Am(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Am(e.attr, r.tfDeprecatedName, r.defaultValue));
+          i = Rm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Rm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "func":
-          i = jx(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = jx(e.attr, r.tfDeprecatedName, r.defaultValue));
+          i = Kx(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Kx(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "tensor":
         case "tensors":
@@ -16978,7 +17036,7 @@ var qx = class {
     t != null && (r = t.reduce((c, p) => (c[p.name] = this.mapNode(p), p.op === "Const" && s.push(c[p.name]), c), {}));
     let a = [], i = [];
     e.signature.inputArg.forEach((c) => {
-      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: Xy(c.type), type: "dtype" } }, children: [] };
+      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: Qy(c.type), type: "dtype" } }, children: [] };
       d.signatureKey = c.name, a.push(d), r[p] = d;
     }), Object.keys(r).forEach((c) => {
       let p = r[c];
@@ -17010,7 +17068,7 @@ var qx = class {
     return t != null && (n = t[n]), { name: n, dtype: e.type };
   }
 };
-function XW(e) {
+function a4(e) {
   let t = X().global;
   if (typeof t.atob != "undefined")
     return t.atob(e);
@@ -17018,24 +17076,24 @@ function XW(e) {
     return new Buffer(e, "base64").toString();
   throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
 }
-function m0(e, t) {
-  let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : XW(e);
+function x0(e, t) {
+  let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : a4(e);
   return t ? n : n.toLowerCase();
 }
-function Nm(e, t, n, s = false) {
+function $m(e, t, n, s = false) {
   let r = e[t];
-  return r != null ? m0(r.s, s) : n;
+  return r != null ? x0(r.s, s) : n;
 }
-function Tm(e, t, n) {
+function _m(e, t, n) {
   let s = e[t];
   return s ? s.b : n;
 }
-function $m(e, t, n) {
+function Am(e, t, n) {
   let s = e[t] || {}, r = s.i != null ? s.i : s.f != null ? s.f : n;
   return typeof r == "number" ? r : parseInt(r, 10);
 }
-function Xy(e) {
-  switch (typeof e == "string" && (e = KS[e]), e) {
+function Qy(e) {
+  switch (typeof e == "string" && (e = JS[e]), e) {
     case 1:
     case 19:
       return "float32";
@@ -17054,43 +17112,43 @@ function Xy(e) {
       return null;
   }
 }
-function jx(e, t, n) {
+function Kx(e, t, n) {
   let s = e[t];
   return s && s.func ? s.func.name : n;
 }
-function _m(e, t, n) {
-  let s = e[t];
-  return s && s.type ? Xy(s.type) : n;
-}
-function Am(e, t, n) {
-  let s = e[t];
-  return s && s.list && s.list.type ? s.list.type.map((r) => Xy(r)) : n;
-}
-function g0(e) {
-  if (!e.unknownRank)
-    return e.dim != null ? e.dim.map((t) => typeof t.size == "number" ? t.size : parseInt(t.size, 10)) : [];
-}
 function Em(e, t, n) {
   let s = e[t];
-  return s && s.shape ? g0(s.shape) : n;
+  return s && s.type ? Qy(s.type) : n;
 }
 function Rm(e, t, n) {
   let s = e[t];
-  return s ? ((s.list.f && s.list.f.length ? s.list.f : s.list.i) || []).map((r) => typeof r == "number" ? r : parseInt(r, 10)) : n;
+  return s && s.list && s.list.type ? s.list.type.map((r) => Qy(r)) : n;
 }
-function Dm(e, t, n, s = false) {
-  let r = e[t];
-  return r && r.list && r.list.s ? r.list.s.map((a) => m0(a, s)) : n;
+function w0(e) {
+  if (!e.unknownRank)
+    return e.dim != null ? e.dim.map((t) => typeof t.size == "number" ? t.size : parseInt(t.size, 10)) : [];
+}
+function Dm(e, t, n) {
+  let s = e[t];
+  return s && s.shape ? w0(s.shape) : n;
 }
 function Fm(e, t, n) {
   let s = e[t];
-  return s && s.list && s.list.shape ? s.list.shape.map((r) => g0(r)) : n;
+  return s ? ((s.list.f && s.list.f.length ? s.list.f : s.list.i) || []).map((r) => typeof r == "number" ? r : parseInt(r, 10)) : n;
 }
-function Om(e, t, n) {
+function Om(e, t, n, s = false) {
+  let r = e[t];
+  return r && r.list && r.list.s ? r.list.s.map((a) => x0(a, s)) : n;
+}
+function Pm(e, t, n) {
+  let s = e[t];
+  return s && s.list && s.list.shape ? s.list.shape.map((r) => w0(r)) : n;
+}
+function zm(e, t, n) {
   let s = e[t];
   return s && s.list && s.list.b ? s.list.b : n;
 }
-var YW = class {
+var i4 = class {
   constructor(e, t, n) {
     this.node = e, this.tensorMap = t, this.context = n, this.inputs = [], this.attrs = {}, this.inputs = e.inputNames.map((s) => this.getInput(s)), e.rawAttrs != null && (this.attrs = Object.keys(e.rawAttrs).reduce((s, r) => (s[r] = this.getAttr(r), s), {}));
   }
@@ -17102,127 +17160,127 @@ var YW = class {
     if (n.tensor != null)
       return un(e, this.tensorMap, this.context);
     if (n.i != null || n.f != null)
-      return $m(this.node.rawAttrs, e, t);
+      return Am(this.node.rawAttrs, e, t);
     if (n.s != null)
-      return Nm(this.node.rawAttrs, e, t);
+      return $m(this.node.rawAttrs, e, t);
     if (n.b != null)
-      return Tm(this.node.rawAttrs, e, t);
-    if (n.shape != null)
-      return Em(this.node.rawAttrs, e, t);
-    if (n.type != null)
       return _m(this.node.rawAttrs, e, t);
+    if (n.shape != null)
+      return Dm(this.node.rawAttrs, e, t);
+    if (n.type != null)
+      return Em(this.node.rawAttrs, e, t);
     if (n.list != null) {
       if (n.list.i != null || n.list.f != null)
-        return Rm(this.node.rawAttrs, e, t);
-      if (n.list.s != null)
-        return Dm(this.node.rawAttrs, e, t);
-      if (n.list.shape != null)
         return Fm(this.node.rawAttrs, e, t);
-      if (n.list.b != null)
+      if (n.list.s != null)
         return Om(this.node.rawAttrs, e, t);
+      if (n.list.shape != null)
+        return Pm(this.node.rawAttrs, e, t);
+      if (n.list.b != null)
+        return zm(this.node.rawAttrs, e, t);
       if (n.list.type != null)
-        return Am(this.node.rawAttrs, e, t);
+        return Rm(this.node.rawAttrs, e, t);
     }
     return t;
   }
 };
-var QW = (e, t, n) => {
+var o4 = (e, t, n) => {
   switch (e.op) {
     case "BiasAdd":
     case "AddV2":
     case "Add":
       return [ie(I("a", e, t, n), I("b", e, t, n))];
     case "AddN":
-      return [KA(I("tensors", e, t, n))];
+      return [eE(I("tensors", e, t, n))];
     case "FloorMod":
     case "Mod":
-      return [kD(I("a", e, t, n), I("b", e, t, n))];
+      return [TD(I("a", e, t, n), I("b", e, t, n))];
     case "Mul":
       return [V(I("a", e, t, n), I("b", e, t, n))];
     case "RealDiv":
     case "Div":
       return [xe(I("a", e, t, n), I("b", e, t, n))];
     case "DivNoNan":
-      return [vR(I("a", e, t, n), I("b", e, t, n))];
+      return [SR(I("a", e, t, n), I("b", e, t, n))];
     case "FloorDiv":
-      return [Hk(I("a", e, t, n), I("b", e, t, n))];
+      return [Xk(I("a", e, t, n), I("b", e, t, n))];
     case "Sub":
       return [ge(I("a", e, t, n), I("b", e, t, n))];
     case "Minimum":
-      return [bp(I("a", e, t, n), I("b", e, t, n))];
+      return [vp(I("a", e, t, n), I("b", e, t, n))];
     case "Maximum":
-      return [$r(I("a", e, t, n), I("b", e, t, n))];
+      return [_r(I("a", e, t, n), I("b", e, t, n))];
     case "Pow":
-      return [pa(I("a", e, t, n), I("b", e, t, n))];
+      return [ha(I("a", e, t, n), I("b", e, t, n))];
     case "SquaredDifference":
-      return [wI(I("a", e, t, n), I("b", e, t, n))];
+      return [CI(I("a", e, t, n), I("b", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var ZW = (e, t, n) => {
+var u4 = (e, t, n) => {
   switch (e.op) {
     case "Abs":
     case "ComplexAbs":
       return [Mt(I("x", e, t, n))];
     case "Acos":
-      return [GA(I("x", e, t, n))];
+      return [YA(I("x", e, t, n))];
     case "Acosh":
-      return [qA(I("x", e, t, n))];
+      return [ZA(I("x", e, t, n))];
     case "Asin":
-      return [tE(I("x", e, t, n))];
+      return [oE(I("x", e, t, n))];
     case "Asinh":
-      return [sE(I("x", e, t, n))];
-    case "Atan":
-      return [aE(I("x", e, t, n))];
-    case "Atan2":
-      return [oE(I("x", e, t, n), I("y", e, t, n))];
-    case "Atanh":
       return [lE(I("x", e, t, n))];
+    case "Atan":
+      return [dE(I("x", e, t, n))];
+    case "Atan2":
+      return [hE(I("x", e, t, n), I("y", e, t, n))];
+    case "Atanh":
+      return [mE(I("x", e, t, n))];
     case "Ceil":
-      return [ME(I("x", e, t, n))];
+      return [GE(I("x", e, t, n))];
     case "Complex":
-      return [oa(I("real", e, t, n), I("imag", e, t, n))];
+      return [ua(I("real", e, t, n), I("imag", e, t, n))];
     case "Cos":
-      return [jg(I("x", e, t, n))];
+      return [Xg(I("x", e, t, n))];
     case "Cosh":
-      return [nI(I("x", e, t, n))];
+      return [iI(I("x", e, t, n))];
     case "Elu":
-      return [fp(I("x", e, t, n))];
+      return [gp(I("x", e, t, n))];
     case "Erf":
-      return [CR(I("x", e, t, n))];
+      return [AR(I("x", e, t, n))];
     case "Exp":
-      return [Xn(I("x", e, t, n))];
-    case "Expm1":
-      return [_R(I("x", e, t, n))];
-    case "Floor":
-      return [mp(I("x", e, t, n))];
-    case "Log":
       return [Yn(I("x", e, t, n))];
+    case "Expm1":
+      return [FR(I("x", e, t, n))];
+    case "Floor":
+      return [bp(I("x", e, t, n))];
+    case "Log":
+      return [Qn(I("x", e, t, n))];
     case "Log1p":
-      return [Yg(I("x", e, t, n))];
+      return [Zg(I("x", e, t, n))];
     case "Imag":
-      return [Kg(I("x", e, t, n))];
+      return [Yg(I("x", e, t, n))];
     case "Neg":
       return [kt(I("x", e, t, n))];
     case "Reciprocal":
-      return [ZD(I("x", e, t, n))];
+      return [s3(I("x", e, t, n))];
     case "Real":
       return [wd(I("x", e, t, n))];
     case "Relu":
-      return [Xs(I("x", e, t, n))];
+      return [Ys(I("x", e, t, n))];
     case "Round":
-      return [fI(I("x", e, t, n))];
+      return [yI(I("x", e, t, n))];
     case "Selu":
-      return [gI(I("x", e, t, n))];
+      return [xI(I("x", e, t, n))];
     case "Sigmoid":
       return [qs(I("x", e, t, n))];
     case "Sin":
-      return [bI(I("x", e, t, n))];
+      return [wI(I("x", e, t, n))];
     case "Sign":
-      return [f3(I("x", e, t, n))];
+      return [v3(I("x", e, t, n))];
     case "Sinh":
-      return [yI(I("x", e, t, n))];
+      return [kI(I("x", e, t, n))];
     case "Softplus":
       return [zl(I("x", e, t, n))];
     case "Sqrt":
@@ -17230,28 +17288,28 @@ var ZW = (e, t, n) => {
     case "Square":
       return [ct(I("x", e, t, n))];
     case "Tanh":
-      return [qu(I("x", e, t, n))];
+      return [ju(I("x", e, t, n))];
     case "Tan":
-      return [O3(I("x", e, t, n))];
+      return [B3(I("x", e, t, n))];
     case "ClipByValue":
       return [Vn(I("x", e, t, n), I("clipValueMin", e, t, n), I("clipValueMax", e, t, n))];
     case "Relu6":
-      return [hI(I("x", e, t, n))];
+      return [bI(I("x", e, t, n))];
     case "Rsqrt":
-      return [mI(un(e.inputNames[0], t, n))];
+      return [vI(un(e.inputNames[0], t, n))];
     case "Prod":
-      return [pI(I("x", e, t, n), I("axes", e, t, n))];
+      return [gI(I("x", e, t, n), I("axes", e, t, n))];
     case "LeakyRelu":
-      return [Xg(I("x", e, t, n), I("alpha", e, t, n))];
+      return [Qg(I("x", e, t, n), I("alpha", e, t, n))];
     case "Prelu":
-      return [sb(I("x", e, t, n), I("alpha", e, t, n))];
+      return [ab(I("x", e, t, n), I("alpha", e, t, n))];
     case "IsNan":
-      return [BR(un(e.inputNames[0], t, n))];
+      return [HR(un(e.inputNames[0], t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function Hn(e, t, n = "") {
+function qn(e, t, n = "") {
   if (!(typeof e == "number" || typeof t == "number")) {
     w.assert(e.length === t.length, () => n + ` Shapes ${e} and ${t} must match`);
     for (let s = 0; s < e.length; s++) {
@@ -17260,20 +17318,20 @@ function Hn(e, t, n = "") {
     }
   }
 }
-function Kx(e) {
+function Xx(e) {
   return !(typeof e == "number" || e.some((t) => t < 0));
 }
-function Nu(e, t, n) {
-  let s = Pm(e, n), r = !Kx(s);
+function Tu(e, t, n) {
+  let s = Mm(e, n), r = !Xx(s);
   if (r && t.length === 0)
     throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);
   if (r && t.forEach((a) => {
-    s = Pm(a.shape, s);
-  }), !Kx(s))
+    s = Mm(a.shape, s);
+  }), !Xx(s))
     throw new Error(`Non-fully-defined elementShape: ${s}`);
   return s;
 }
-function Pm(e, t) {
+function Mm(e, t) {
   if (typeof e == "number")
     return t;
   if (typeof t == "number")
@@ -17289,7 +17347,7 @@ function Pm(e, t) {
   }
   return n;
 }
-var JW = class {
+var l4 = class {
   constructor(e, t, n, s, r, a, i) {
     this.name = e, this.dtype = t, this.maxSize = n, this.elementShape = s, this.identicalElementShapes = r, this.dynamicSize = a, this.clearAfterRead = i, this.tensors = [], this.closed_ = false, this.idTensor = we(0), Ht(this.idTensor);
   }
@@ -17329,7 +17387,7 @@ var JW = class {
     if (t.dtype !== this.dtype)
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
           because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);
-    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t.shape), Hn(this.elementShape, t.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), n.read)
+    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0) && (this.elementShape = t.shape), qn(this.elementShape, t.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${e}.`), n.read)
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);
     if (n.written)
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);
@@ -17353,7 +17411,7 @@ var JW = class {
     if (e.length === 0)
       return fs([], [0].concat(this.elementShape));
     let n = this.readMany(e);
-    return Hn(this.elementShape, n[0].shape, "TensorArray shape mismatch: "), Jn(n, 0);
+    return qn(this.elementShape, n[0].shape, "TensorArray shape mismatch: "), es(n, 0);
   }
   concat(e) {
     if (!!e && e !== this.dtype)
@@ -17364,7 +17422,7 @@ var JW = class {
     for (let s = 0; s < this.size(); s++)
       t.push(s);
     let n = this.readMany(t);
-    return Hn(this.elementShape, n[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`), Ft(n, 0);
+    return qn(this.elementShape, n[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`), Ft(n, 0);
   }
   scatter(e, t) {
     if (t.dtype !== this.dtype)
@@ -17391,7 +17449,7 @@ var JW = class {
       t = G(t, [1, n, r]);
       for (let o = 0; o < e.length; ++o) {
         let u = o === 0 ? 0 : s[o - 1], l = [0, u, 0], c = [1, e[o], r];
-        a[o] = G(He(t, l, c), this.elementShape);
+        a[o] = G(qe(t, l, c), this.elementShape);
       }
       return a;
     });
@@ -17401,19 +17459,19 @@ var JW = class {
     this.writeMany(i, a);
   }
 };
-var eo = class {
+var no = class {
   constructor(e, t, n, s = -1) {
     this.tensors = e, this.elementShape = t, this.elementDtype = n, e != null && e.forEach((r) => {
       if (n !== r.dtype)
         throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);
-      Hn(t, r.shape, "TensorList shape mismatch: "), Ht(r);
+      qn(t, r.shape, "TensorList shape mismatch: "), Ht(r);
     }), this.idTensor = we(0), this.maxNumElements = s, Ht(this.idTensor);
   }
   get id() {
     return this.idTensor.id;
   }
   copy() {
-    return new eo([...this.tensors], this.elementShape, this.elementDtype);
+    return new no([...this.tensors], this.elementShape, this.elementDtype);
   }
   clearAndClose(e) {
     this.tensors.forEach((t) => {
@@ -17428,11 +17486,11 @@ var eo = class {
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     if (n !== -1 && this.tensors.length !== n)
       throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);
-    Hn(e, this.elementShape, "TensorList shape mismatch: ");
-    let s = Nu(this.elementShape, this.tensors, e);
+    qn(e, this.elementShape, "TensorList shape mismatch: ");
+    let s = Tu(this.elementShape, this.tensors, e);
     return j(() => {
       let r = this.tensors.map((a) => G(a, s));
-      return Jn(r, 0);
+      return es(r, 0);
     });
   }
   popBack(e, t) {
@@ -17440,13 +17498,13 @@ var eo = class {
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     if (this.size() === 0)
       throw new Error("Trying to pop from an empty list.");
-    let n = Nu(this.elementShape, this.tensors, e), s = this.tensors.pop();
-    return Hn(s.shape, e, "TensorList shape mismatch: "), G(s, n);
+    let n = Tu(this.elementShape, this.tensors, e), s = this.tensors.pop();
+    return qn(s.shape, e, "TensorList shape mismatch: "), G(s, n);
   }
   pushBack(e) {
     if (e.dtype !== this.elementDtype)
       throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);
-    if (Hn(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
+    if (qn(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
       throw new Error("Trying to push element into a full list.");
     Ht(e), this.tensors.push(e);
   }
@@ -17455,7 +17513,7 @@ var eo = class {
       throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);
     if (this.maxNumElements !== -1 && e > this.maxNumElements)
       throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);
-    let t = new eo([], this.elementShape, this.elementDtype, this.maxNumElements);
+    let t = new no([], this.elementShape, this.elementDtype, this.maxNumElements);
     t.tensors.length = e;
     for (let n = 0; n < Math.min(this.tensors.length, e); ++n)
       t.tensors[n] = this.tensors[n];
@@ -17468,8 +17526,8 @@ var eo = class {
       throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);
     if (this.tensors[e] == null)
       throw new Error(`element at index ${e} is null.`);
-    Hn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
-    let s = Nu(this.elementShape, this.tensors, t);
+    qn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
+    let s = Tu(this.elementShape, this.tensors, t);
     return G(this.tensors[e], s);
   }
   setItem(e, t) {
@@ -17477,74 +17535,74 @@ var eo = class {
       throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);
     if (e < 0 || this.maxNumElements !== -1 && e >= this.maxNumElements)
       throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);
-    Hn(this.elementShape, t.shape, "TensorList shape mismatch: "), Ht(t), this.tensors[e] = t;
+    qn(this.elementShape, t.shape, "TensorList shape mismatch: "), Ht(t), this.tensors[e] = t;
   }
   gather(e, t, n) {
     if (t !== this.elementDtype)
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
-    Hn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
-    let s = Nu(this.elementShape, this.tensors, n);
+    qn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
+    let s = Tu(this.elementShape, this.tensors, n);
     return e.length === 0 ? fs([], [0].concat(s)) : j(() => {
       let r = e.map((a) => G(this.tensors[a], s));
-      return Jn(r, 0);
+      return es(r, 0);
     });
   }
   concat(e, t) {
     if (!!e && e !== this.elementDtype)
       throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
-    Hn(this.elementShape, t, "TensorList shape mismatch: ");
-    let n = Nu(this.elementShape, this.tensors, t);
+    qn(this.elementShape, t, "TensorList shape mismatch: ");
+    let n = Tu(this.elementShape, this.tensors, t);
     return this.size() === 0 ? fs([], [0].concat(n)) : j(() => {
       let s = this.tensors.map((r) => G(r, n));
       return Ft(s, 0);
     });
   }
 };
-function e4(e, t, n) {
+function c4(e, t, n) {
   let s = e.dtype;
   if (e.shape.length < 1)
     throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);
   if (e.dtype !== n)
     throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);
   let r = e.shape.slice(1);
-  Hn(r, t, "TensorList shape mismatch: ");
+  qn(r, t, "TensorList shape mismatch: ");
   let a = Fs(e);
-  return new eo(a, t, s);
+  return new no(a, t, s);
 }
-function t4(e, t, n) {
-  return new eo([], e, t, n);
+function d4(e, t, n) {
+  return new no([], e, t, n);
 }
-function n4(e, t, n, s) {
+function p4(e, t, n, s) {
   if (t.length !== e.shape[0])
     throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);
   let r = Math.max(...t);
   if (s != null && s !== -1 && r >= s)
     throw new Error(`Max index must be < array size (${r}  vs. ${s})`);
-  let a = new eo([], n, e.dtype, s), i = Fs(e, 0);
+  let a = new no([], n, e.dtype, s), i = Fs(e, 0);
   return t.forEach((o, u) => {
     a.setItem(o, i[u]);
   }), a;
 }
-function s4(e, t, n) {
+function h4(e, t, n) {
   let s = 0, r = t.map((c) => (s += c, s));
   if (s !== e.shape[0])
     throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
         ${s}, and tensor's shape is: ${e.shape}`);
-  let a = e.shape.slice(1), i = Pm(a, n), o = s === 0 ? 0 : e.size / s, u = j(() => {
+  let a = e.shape.slice(1), i = Mm(a, n), o = s === 0 ? 0 : e.size / s, u = j(() => {
     let c = [];
     e = G(e, [1, s, o]);
     for (let p = 0; p < t.length; ++p) {
       let d = p === 0 ? 0 : r[p - 1], h = [0, d, 0], f = [1, t[p], o];
-      c[p] = G(He(e, h, f), i);
+      c[p] = G(qe(e, h, f), i);
     }
     return e.dispose(), c;
-  }), l = new eo([], n, e.dtype, t.length);
+  }), l = new no([], n, e.dtype, t.length);
   for (let c = 0; c < u.length; c++)
     l.setItem(c, u[c]);
   return l;
 }
-var r4 = async (e, t, n) => {
+var f4 = async (e, t, n) => {
   switch (e.op) {
     case "If":
     case "StatelessIf": {
@@ -17601,7 +17659,7 @@ var r4 = async (e, t, n) => {
       return n.nextIteration(), [Us(s)];
     }
     case "TensorArrayV3": {
-      let s = I("size", e, t, n), r = I("dtype", e, t, n), a = I("elementShape", e, t, n), i = I("dynamicSize", e, t, n), o = I("clearAfterRead", e, t, n), u = I("identicalElementShapes", e, t, n), l = I("name", e, t, n), c = new JW(l, r, s, a, u, i, o);
+      let s = I("size", e, t, n), r = I("dtype", e, t, n), a = I("elementShape", e, t, n), i = I("dynamicSize", e, t, n), o = I("clearAfterRead", e, t, n), u = I("identicalElementShapes", e, t, n), l = I("name", e, t, n), c = new l4(l, r, s, a, u, i, o);
       return n.addTensorArray(c), [c.idTensor, we(1)];
     }
     case "TensorArrayWriteV3": {
@@ -17646,14 +17704,14 @@ var r4 = async (e, t, n) => {
     }
     case "TensorListScatterV2":
     case "TensorListScatter": {
-      let s = I("indices", e, t, n), r = I("tensor", e, t, n), a = I("elementShape", e, t, n), i = I("numElements", e, t, n), o = n4(r, s, a, i);
+      let s = I("indices", e, t, n), r = I("tensor", e, t, n), a = I("elementShape", e, t, n), i = I("numElements", e, t, n), o = p4(r, s, a, i);
       return n.addTensorList(o), [o.idTensor];
     }
     case "TensorListReserve":
     case "EmptyTensorList": {
       let s = I("elementShape", e, t, n), r = I("elementDType", e, t, n), a;
       e.op === "TensorListReserve" ? a = "numElements" : a = "maxNumElements";
-      let i = I(a, e, t, n), o = t4(s, r, i);
+      let i = I(a, e, t, n), o = d4(s, r, i);
       return n.addTensorList(o), [o.idTensor];
     }
     case "TensorListGather": {
@@ -17665,7 +17723,7 @@ var r4 = async (e, t, n) => {
       return [n.getTensorList(s.id).stack(r, a, i)];
     }
     case "TensorListFromTensor": {
-      let s = I("tensor", e, t, n), r = I("elementShape", e, t, n), a = I("elementDType", e, t, n), i = e4(s, r, a);
+      let s = I("tensor", e, t, n), r = I("elementShape", e, t, n), a = I("elementDType", e, t, n), i = c4(s, r, a);
       return n.addTensorList(i), [i.idTensor];
     }
     case "TensorListConcat": {
@@ -17681,7 +17739,7 @@ var r4 = async (e, t, n) => {
       return [n.getTensorList(s.id).popBack(r, a)];
     }
     case "TensorListSplit": {
-      let s = I("tensor", e, t, n), r = I("elementShape", e, t, n), a = I("lengths", e, t, n), i = s4(s, a, r);
+      let s = I("tensor", e, t, n), r = I("elementShape", e, t, n), a = I("lengths", e, t, n), i = h4(s, a, r);
       return n.addTensorList(i), [i.idTensor];
     }
     case "TensorListLength": {
@@ -17696,7 +17754,7 @@ var r4 = async (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function Xx(e, t, n) {
+function Yx(e, t, n) {
   let [s, r] = I("fusedOps", e, t, n), a = s === "biasadd", i = !a, o = r === "prelu", u = s === "fusedbatchnorm", l = I("numArgs", e, t, n);
   if (a) {
     if (o && l !== 2)
@@ -17711,67 +17769,67 @@ function Xx(e, t, n) {
   let g = I("leakyreluAlpha", e, t, n);
   return { stride: c, pad: p, dataFormat: d, dilations: h, biasArg: f, preluArg: m, activationFunc: r, leakyreluAlpha: g };
 }
-var a4 = (e, t, n) => {
+var m4 = (e, t, n) => {
   switch (e.op) {
     case "Conv1D": {
       let s = I("stride", e, t, n), r = I("pad", e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilation", e, t, n);
-      return [Zk(I("x", e, t, n), I("filter", e, t, n), s, r, a, i)];
+      return [nI(I("x", e, t, n), I("filter", e, t, n), s, r, a, i)];
     }
     case "Conv2D": {
       let s = I("strides", e, t, n), r = sd(e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilations", e, t, n);
-      return [ca(I("x", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, a, [i[1], i[2]])];
+      return [da(I("x", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, a, [i[1], i[2]])];
     }
     case "_FusedConv2D": {
-      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = Xx(e, t, n);
-      return [ha.conv2d({ x: I("x", e, t, n), filter: I("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
+      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = Yx(e, t, n);
+      return [fa.conv2d({ x: I("x", e, t, n), filter: I("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
     }
     case "FusedDepthwiseConv2dNative": {
-      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = Xx(e, t, n);
-      return [ha.depthwiseConv2d({ x: I("x", e, t, n), filter: I("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
+      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = Yx(e, t, n);
+      return [fa.depthwiseConv2d({ x: I("x", e, t, n), filter: I("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
     }
     case "Conv2DBackpropInput":
     case "Conv2dTranspose": {
       let s = I("outputShape", e, t, n), r = I("strides", e, t, n), a = sd(e, t, n);
-      return [Jk(I("x", e, t, n), I("filter", e, t, n), s, [r[1], r[2]], a)];
+      return [sI(I("x", e, t, n), I("filter", e, t, n), s, [r[1], r[2]], a)];
     }
     case "DepthwiseConv2dNative":
     case "DepthwiseConv2d": {
       let s = I("strides", e, t, n), r = sd(e, t, n), a = I("dilations", e, t, n), i = I("dataFormat", e, t, n).toUpperCase();
-      return [hp(I("input", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, i, [a[1], a[2]])];
+      return [mp(I("input", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, i, [a[1], a[2]])];
     }
     case "Conv3D": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilations", e, t, n);
-      return [eI(I("x", e, t, n), I("filter", e, t, n), [s[1], s[2], s[3]], r, a, [i[1], i[2], i[3]])];
+      return [rI(I("x", e, t, n), I("filter", e, t, n), [s[1], s[2], s[3]], r, a, [i[1], i[2], i[3]])];
     }
     case "AvgPool": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
-      return [Gg(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
+      return [qg(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
     }
     case "MaxPool": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
-      return [eb(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
+      return [nb(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
     }
     case "MaxPoolWithArgmax": {
-      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n), i = I("includeBatchInIndex", e, t, n), { result: o, indexes: u } = fD(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r, i);
+      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n), i = I("includeBatchInIndex", e, t, n), { result: o, indexes: u } = vD(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r, i);
       return [o, u];
     }
     case "AvgPool3D": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
-      return [Yk(I("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
+      return [eI(I("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
     }
     case "MaxPool3D": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
-      return [dI(I("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
+      return [mI(I("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
     }
     case "Dilation2D": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("dilations", e, t, n), i = s[1], o = s[2], u = a[1], l = a[2];
-      return [fR(I("x", e, t, n), I("filter", e, t, n), [i, o], r, [u, l], "NHWC")];
+      return [vR(I("x", e, t, n), I("filter", e, t, n), [i, o], r, [u, l], "NHWC")];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var i4 = (e, t, n) => {
+var g4 = (e, t, n) => {
   switch (e.op) {
     case "Fill": {
       let s = I("shape", e, t, n), r = I("dtype", e, t, n), a = I("value", e, t, n);
@@ -17779,11 +17837,11 @@ var i4 = (e, t, n) => {
     }
     case "LinSpace": {
       let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("num", e, t, n);
-      return [GR(s, r, a)];
+      return [XR(s, r, a)];
     }
     case "Multinomial": {
       let s = I("logits", e, t, n), r = I("numSamples", e, t, n), a = I("seed", e, t, n);
-      return [TD(s, r, a)];
+      return [RD(s, r, a)];
     }
     case "OneHot": {
       let s = I("indices", e, t, n), r = I("depth", e, t, n), a = I("onValue", e, t, n), i = I("offValue", e, t, n);
@@ -17792,16 +17850,16 @@ var i4 = (e, t, n) => {
     case "Ones":
       return [Mn(I("shape", e, t, n), I("dtype", e, t, n))];
     case "OnesLike":
-      return [Qn(I("x", e, t, n))];
+      return [Zn(I("x", e, t, n))];
     case "RandomUniform":
       return [Ml(I("shape", e, t, n), I("minval", e, t, n), I("maxval", e, t, n), I("dtype", e, t, n))];
     case "Range": {
       let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("step", e, t, n);
-      return [Yu(s, r, a, I("dtype", e, t, n))];
+      return [Qu(s, r, a, I("dtype", e, t, n))];
     }
     case "TruncatedNormal": {
       let s = I("shape", e, t, n), r = I("mean", e, t, n), a = I("stdDev", e, t, n), i = I("seed", e, t, n);
-      return [db(s, r, a, I("dtype", e, t, n), i)];
+      return [hb(s, r, a, I("dtype", e, t, n), i)];
     }
     case "Zeros":
       return [$t(I("shape", e, t, n), I("dtype", e, t, n))];
@@ -17811,54 +17869,54 @@ var i4 = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function Hf(e, t, n) {
+function qf(e, t, n) {
   let s = I("boxes", e, t, n), r = I("scores", e, t, n), a = I("maxOutputSize", e, t, n), i = I("iouThreshold", e, t, n), o = I("scoreThreshold", e, t, n), u = I("softNmsSigma", e, t, n);
   return { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u };
 }
-var o4 = async (e, t, n) => {
+var b4 = async (e, t, n) => {
   switch (e.op) {
     case "NonMaxSuppressionV5": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = Hf(e, t, n), l = await qn.nonMaxSuppressionWithScoreAsync(s, r, a, i, o, u);
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = qf(e, t, n), l = await jn.nonMaxSuppressionWithScoreAsync(s, r, a, i, o, u);
       return [l.selectedIndices, l.selectedScores];
     }
     case "NonMaxSuppressionV4": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Hf(e, t, n), u = I("padToMaxOutputSize", e, t, n), l = await qn.nonMaxSuppressionPaddedAsync(s, r, a, i, o, u);
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = qf(e, t, n), u = I("padToMaxOutputSize", e, t, n), l = await jn.nonMaxSuppressionPaddedAsync(s, r, a, i, o, u);
       return [l.selectedIndices, l.validOutputs];
     }
     case "NonMaxSuppressionV3":
     case "NonMaxSuppressionV2": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Hf(e, t, n);
-      return [await qn.nonMaxSuppressionAsync(s, r, a, i, o)];
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = qf(e, t, n);
+      return [await jn.nonMaxSuppressionAsync(s, r, a, i, o)];
     }
     case "Where": {
-      let s = ce(I("condition", e, t, n), "bool"), r = [await II(s)];
+      let s = ce(I("condition", e, t, n), "bool"), r = [await TI(s)];
       return s.dispose(), r;
     }
     case "ListDiff":
-      return p3(I("x", e, t, n), I("y", e, t, n));
+      return b3(I("x", e, t, n), I("y", e, t, n));
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var u4 = (e, t, n) => {
+var y4 = (e, t, n) => {
   switch (e.op) {
     case "TopKV2": {
-      let s = I("x", e, t, n), r = I("k", e, t, n), a = I("sorted", e, t, n), i = z3(s, r, a);
+      let s = I("x", e, t, n), r = I("k", e, t, n), a = I("sorted", e, t, n), i = W3(s, r, a);
       return [i.values, i.indices];
     }
     case "Unique": {
-      let s = I("x", e, t, n), r = hx(s);
+      let s = I("x", e, t, n), r = mx(s);
       return [r.values, r.indices];
     }
     case "UniqueV2": {
-      let s = I("x", e, t, n), r = I("axis", e, t, n), a = hx(s, r);
+      let s = I("x", e, t, n), r = I("axis", e, t, n), a = mx(s, r);
       return [a.values, a.indices];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var l4 = (e, t, n) => {
+var v4 = (e, t, n) => {
   switch (e.op) {
     case "Const":
       return t[e.name];
@@ -17898,7 +17956,7 @@ var l4 = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var c4 = class {
+var x4 = class {
   constructor(e, t) {
     this.keyDType = e, this.valueDType = t, this.handle = we(0), this.tensorMap = /* @__PURE__ */ new Map(), Ht(this.handle);
   }
@@ -17936,7 +17994,7 @@ var c4 = class {
         let a = n[r], i = this.findWithDefault(a, t);
         s.push(i);
       }
-      return Jn(s);
+      return es(s);
     });
   }
   findWithDefault(e, t) {
@@ -17950,11 +18008,11 @@ var c4 = class {
       throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`);
   }
 };
-var d4 = async (e, t, n, s) => {
+var w4 = async (e, t, n, s) => {
   switch (e.op) {
     case "HashTable":
     case "HashTableV2": {
-      let r = I("keyDType", e, t, n), a = I("valueDType", e, t, n), i = new c4(r, a);
+      let r = I("keyDType", e, t, n), a = I("valueDType", e, t, n), i = new x4(r, a);
       return s.addHashTable(e.name, i), [i.handle];
     }
     case "LookupTableImport":
@@ -17976,48 +18034,48 @@ var d4 = async (e, t, n, s) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var p4 = (e, t, n) => {
+var k4 = (e, t, n) => {
   switch (e.op) {
     case "ResizeBilinear": {
       let s = I("images", e, t, n), r = I("size", e, t, n), a = I("alignCorners", e, t, n), i = I("halfPixelCenters", e, t, n);
-      return [qn.resizeBilinear(s, [r[0], r[1]], a, i)];
+      return [jn.resizeBilinear(s, [r[0], r[1]], a, i)];
     }
     case "ResizeNearestNeighbor": {
       let s = I("images", e, t, n), r = I("size", e, t, n), a = I("alignCorners", e, t, n), i = I("halfPixelCenters", e, t, n);
-      return [qn.resizeNearestNeighbor(s, [r[0], r[1]], a, i)];
+      return [jn.resizeNearestNeighbor(s, [r[0], r[1]], a, i)];
     }
     case "CropAndResize": {
       let s = I("image", e, t, n), r = I("boxes", e, t, n), a = I("boxInd", e, t, n), i = I("cropSize", e, t, n), o = I("method", e, t, n), u = I("extrapolationValue", e, t, n);
-      return [qn.cropAndResize(s, r, a, i, o, u)];
+      return [jn.cropAndResize(s, r, a, i, o, u)];
     }
     case "ImageProjectiveTransformV3": {
       let s = I("images", e, t, n), r = I("transforms", e, t, n), a = I("outputShape", e, t, n), i = I("fillValue", e, t, n), o = I("interpolation", e, t, n), u = I("fillMode", e, t, n);
-      return [qn.transform(s, r, o.toLowerCase(), u.toLowerCase(), i, a)];
+      return [jn.transform(s, r, o.toLowerCase(), u.toLowerCase(), i, a)];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var h4 = (e, t, n) => {
+var I4 = (e, t, n) => {
   switch (e.op) {
     case "Equal":
-      return [Kn(I("a", e, t, n), I("b", e, t, n))];
+      return [Xn(I("a", e, t, n), I("b", e, t, n))];
     case "NotEqual":
-      return [Xu(I("a", e, t, n), I("b", e, t, n))];
+      return [Yu(I("a", e, t, n), I("b", e, t, n))];
     case "Greater":
-      return [Wn(I("a", e, t, n), I("b", e, t, n))];
+      return [Un(I("a", e, t, n), I("b", e, t, n))];
     case "GreaterEqual":
-      return [Ko(I("a", e, t, n), I("b", e, t, n))];
-    case "Less":
-      return [aI(I("a", e, t, n), I("b", e, t, n))];
-    case "LessEqual":
       return [Xo(I("a", e, t, n), I("b", e, t, n))];
+    case "Less":
+      return [lI(I("a", e, t, n), I("b", e, t, n))];
+    case "LessEqual":
+      return [Yo(I("a", e, t, n), I("b", e, t, n))];
     case "LogicalAnd":
       return [Ds(I("a", e, t, n), I("b", e, t, n))];
     case "LogicalNot":
-      return [Jg(I("a", e, t, n))];
+      return [tb(I("a", e, t, n))];
     case "LogicalOr":
-      return [cI(I("a", e, t, n), I("b", e, t, n))];
+      return [fI(I("a", e, t, n), I("b", e, t, n))];
     case "Select":
     case "SelectV2":
       return [vn(I("condition", e, t, n), I("a", e, t, n), I("b", e, t, n))];
@@ -18025,16 +18083,16 @@ var h4 = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var f4 = (e, t, n) => {
+var S4 = (e, t, n) => {
   switch (e.op) {
     case "BatchMatMul":
     case "BatchMatMulV2":
     case "MatMul":
       return [We(I("a", e, t, n), I("b", e, t, n), I("transposeA", e, t, n), I("transposeB", e, t, n))];
     case "Einsum":
-      return [kR(I("equation", e, t, n), ...I("tensors", e, t, n))];
+      return [TR(I("equation", e, t, n), ...I("tensors", e, t, n))];
     case "Transpose":
-      return [qe(I("x", e, t, n), I("perm", e, t, n))];
+      return [Ge(I("x", e, t, n), I("perm", e, t, n))];
     case "_FusedMatMul":
       let [s, r] = I("fusedOps", e, t, n), a = s === "biasadd", i = r === "prelu", o = I("numArgs", e, t, n), u = I("leakyreluAlpha", e, t, n);
       if (a) {
@@ -18044,31 +18102,31 @@ var f4 = (e, t, n) => {
           throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
       }
       let [l, c] = I("args", e, t, n);
-      return [ha.matMul({ a: I("a", e, t, n), b: I("b", e, t, n), transposeA: I("transposeA", e, t, n), transposeB: I("transposeB", e, t, n), bias: l, activation: r, preluActivationWeights: c, leakyreluAlpha: u })];
+      return [fa.matMul({ a: I("a", e, t, n), b: I("b", e, t, n), transposeA: I("transposeA", e, t, n), transposeB: I("transposeB", e, t, n), bias: l, activation: r, preluActivationWeights: c, leakyreluAlpha: u })];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var m4 = (e, t, n) => {
+var C4 = (e, t, n) => {
   switch (e.op) {
     case "FusedBatchNorm":
     case "FusedBatchNormV2":
-      return [ju(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
+      return [Ku(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
     case "FusedBatchNormV3":
-      return [ju(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
+      return [Ku(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
     case "LRN":
-      return [qR(I("x", e, t, n), I("radius", e, t, n), I("bias", e, t, n), I("alpha", e, t, n), I("beta", e, t, n))];
+      return [QR(I("x", e, t, n), I("radius", e, t, n), I("bias", e, t, n), I("alpha", e, t, n), I("beta", e, t, n))];
     case "Softmax":
-      return [ub(I("x", e, t, n))];
+      return [cb(I("x", e, t, n))];
     case "LogSoftmax":
-      return [iI(I("x", e, t, n))];
+      return [cI(I("x", e, t, n))];
     case "SparseToDense":
-      return [NI(I("sparseIndices", e, t, n), I("outputShape", e, t, n), I("sparseValues", e, t, n), I("defaultValue", e, t, n))];
+      return [AI(I("sparseIndices", e, t, n), I("outputShape", e, t, n), I("sparseValues", e, t, n), I("defaultValue", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var g4 = (e, t, n) => {
+var N4 = (e, t, n) => {
   switch (e.op) {
     case "Max": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
@@ -18080,7 +18138,7 @@ var g4 = (e, t, n) => {
     }
     case "Min": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
-      return [fm(I("x", e, t, n), i, o)];
+      return [gm(I("x", e, t, n), i, o)];
     }
     case "Sum": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
@@ -18088,44 +18146,44 @@ var g4 = (e, t, n) => {
     }
     case "All": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
-      return [qk(I("x", e, t, n), i, o)];
+      return [Yk(I("x", e, t, n), i, o)];
     }
     case "Any": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
-      return [pm(I("x", e, t, n), i, o)];
+      return [hm(I("x", e, t, n), i, o)];
     }
     case "ArgMax": {
       let i = I("axis", e, t, n);
-      return [Hu(I("x", e, t, n), i)];
+      return [qu(I("x", e, t, n), i)];
     }
     case "ArgMin": {
       let i = I("axis", e, t, n);
-      return [JA(I("x", e, t, n), i)];
+      return [aE(I("x", e, t, n), i)];
     }
     case "Prod": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
-      return [pI(I("x", e, t, n), i, o)];
+      return [gI(I("x", e, t, n), i, o)];
     }
     case "Cumprod": {
       let i = I("axis", e, t, n), o = I("exclusive", e, t, n), u = I("reverse", e, t, n);
-      return [aR(I("x", e, t, n), i, o, u)];
+      return [mm(I("x", e, t, n), i, o, u)];
     }
     case "Cumsum": {
       let i = I("axis", e, t, n), o = I("exclusive", e, t, n), u = I("reverse", e, t, n);
-      return [sI(I("x", e, t, n), i, o, u)];
+      return [oI(I("x", e, t, n), i, o, u)];
     }
     case "Bincount":
       let s = I("x", e, t, n), r = I("weights", e, t, n), a = I("size", e, t, n);
-      return [Qk(s, r, a)];
+      return [tI(s, r, a)];
     case "DenseBincount": {
       let i = I("x", e, t, n), o = I("weights", e, t, n), u = I("size", e, t, n), l = I("binaryOutput", e, t, n);
-      return [uR(i, o, u, l)];
+      return [hR(i, o, u, l)];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var b4 = (e, t, n) => {
+var T4 = (e, t, n) => {
   switch (e.op) {
     case "ConcatV2":
     case "Concat": {
@@ -18134,40 +18192,40 @@ var b4 = (e, t, n) => {
     }
     case "Gather": {
       let s = I("x", e, t, n), r = I("indices", e, t, n);
-      return [Ku(s, ce(r, "int32"), 0)];
+      return [Xu(s, ce(r, "int32"), 0)];
     }
     case "GatherV2": {
       let s = I("axis", e, t, n), r = I("batchDims", e, t, n), a = I("x", e, t, n), i = I("indices", e, t, n);
-      return [Ku(a, ce(i, "int32"), s, r)];
+      return [Xu(a, ce(i, "int32"), s, r)];
     }
     case "Reverse": {
       let s = I("dims", e, t, n), r = [];
       for (let i = 0; i < s.length; i++)
         s[i] && r.push(i);
       let a = I("x", e, t, n);
-      return [Zn(a, r)];
+      return [Jn(a, r)];
     }
     case "ReverseV2": {
       let s = I("axis", e, t, n), r = I("x", e, t, n);
-      return [Zn(r, s)];
+      return [Jn(r, s)];
     }
     case "Slice": {
       let s = I("begin", e, t, n), r = I("size", e, t, n);
-      return [He(I("x", e, t, n), s, r)];
+      return [qe(I("x", e, t, n), s, r)];
     }
     case "StridedSlice": {
       let s = I("begin", e, t, n), r = I("end", e, t, n), a = I("strides", e, t, n), i = I("beginMask", e, t, n), o = I("endMask", e, t, n), u = I("ellipsisMask", e, t, n), l = I("newAxisMask", e, t, n), c = I("shrinkAxisMask", e, t, n), p = I("x", e, t, n);
-      return [D3(p, s, r, a, i, o, u, l, c)];
+      return [M3(p, s, r, a, i, o, u, l, c)];
     }
     case "Pack":
       return j(() => {
-        let s = I("axis", e, t, n), r = I("tensors", e, t, n), a = r[0].shape, i = mr(r[0]).shape, o = r.map((u) => {
+        let s = I("axis", e, t, n), r = I("tensors", e, t, n), a = r[0].shape, i = gr(r[0]).shape, o = r.map((u) => {
           let l = w.arraysEqual(u.shape, a);
-          if (!l && !w.arraysEqual(mr(u).shape, i))
+          if (!l && !w.arraysEqual(gr(u).shape, i))
             throw new Error("the input tensors shape does not match");
           return l ? u : G(u, a);
         });
-        return [Jn(o, s)];
+        return [es(o, s)];
       });
     case "Unpack": {
       let s = I("axis", e, t, n), r = I("tensor", e, t, n);
@@ -18184,21 +18242,21 @@ var b4 = (e, t, n) => {
     }
     case "ScatterNd": {
       let s = I("indices", e, t, n), r = I("values", e, t, n), a = I("shape", e, t, n);
-      return [X3(s, r, a)];
+      return [eF(s, r, a)];
     }
     case "GatherNd": {
       let s = I("x", e, t, n), r = I("indices", e, t, n);
-      return [J3(s, r)];
+      return [rF(s, r)];
     }
     case "SparseToDense": {
       let s = I("sparseIndices", e, t, n), r = I("outputShape", e, t, n), a = I("sparseValues", e, t, n), i = I("defaultValue", e, t, n);
-      return [NI(s, a, r, a.dtype === i.dtype ? i : ce(i, a.dtype))];
+      return [AI(s, a, r, a.dtype === i.dtype ? i : ce(i, a.dtype))];
     }
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var y4 = (e, t, n) => {
+var $4 = (e, t, n) => {
   switch (e.op) {
     case "SparseFillEmptyRows": {
       let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: i } = Wc.sparseFillEmptyRows(I("indices", e, t, n), I("values", e, t, n), I("denseShape", e, t, n), I("defaultValue", e, t, n));
@@ -18216,37 +18274,37 @@ var y4 = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var v4 = (e, t, n) => {
+var _4 = (e, t, n) => {
   switch (e.op) {
     case "FFT":
-      return [lb(I("x", e, t, n))];
+      return [db(I("x", e, t, n))];
     case "IFFT":
       return [Id(I("x", e, t, n))];
     case "RFFT":
-      return [cb(I("x", e, t, n))];
+      return [pb(I("x", e, t, n))];
     case "IRFFT":
-      return [xI(I("x", e, t, n))];
+      return [SI(I("x", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var x4 = (e, t, n) => {
+var A4 = (e, t, n) => {
   switch (e.op) {
     case "StringNGrams": {
-      let { nGrams: s, nGramsSplits: r } = Mf.stringNGrams(I("data", e, t, n), I("dataSplits", e, t, n), I("separator", e, t, n), I("nGramWidths", e, t, n), I("leftPad", e, t, n), I("rightPad", e, t, n), I("padWidth", e, t, n), I("preserveShortSequences", e, t, n));
+      let { nGrams: s, nGramsSplits: r } = Bf.stringNGrams(I("data", e, t, n), I("dataSplits", e, t, n), I("separator", e, t, n), I("nGramWidths", e, t, n), I("leftPad", e, t, n), I("rightPad", e, t, n), I("padWidth", e, t, n), I("preserveShortSequences", e, t, n));
       return [s, r];
     }
     case "StringSplit": {
-      let { indices: s, values: r, shape: a } = Mf.stringSplit(I("input", e, t, n), I("delimiter", e, t, n), I("skipEmpty", e, t, n));
+      let { indices: s, values: r, shape: a } = Bf.stringSplit(I("input", e, t, n), I("delimiter", e, t, n), I("skipEmpty", e, t, n));
       return [s, r, a];
     }
     case "StringToHashBucketFast":
-      return [Mf.stringToHashBucketFast(I("input", e, t, n), I("numBuckets", e, t, n))];
+      return [Bf.stringToHashBucketFast(I("input", e, t, n), I("numBuckets", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-var w4 = (e, t, n) => {
+var E4 = (e, t, n) => {
   switch (e.op) {
     case "Cast":
       return [ce(I("x", e, t, n), I("dtype", e, t, n))];
@@ -18256,80 +18314,80 @@ var w4 = (e, t, n) => {
     }
     case "Squeeze": {
       let s = I("axis", e, t, n);
-      return [mr(I("x", e, t, n), s)];
+      return [gr(I("x", e, t, n), s)];
     }
     case "Reshape":
       return [G(I("x", e, t, n), I("shape", e, t, n))];
     case "MirrorPad":
-      return [xD(I("x", e, t, n), I("padding", e, t, n), I("mode", e, t, n))];
+      return [CD(I("x", e, t, n), I("padding", e, t, n), I("mode", e, t, n))];
     case "PadV2":
     case "Pad":
-      return [fi(I("x", e, t, n), I("padding", e, t, n), I("constantValue", e, t, n))];
+      return [bi(I("x", e, t, n), I("padding", e, t, n), I("constantValue", e, t, n))];
     case "SpaceToBatchND": {
       let s = I("blockShape", e, t, n), r = I("paddings", e, t, n);
-      return [nb(I("x", e, t, n), s, r)];
+      return [rb(I("x", e, t, n), s, r)];
     }
     case "BatchToSpaceND": {
       let s = I("blockShape", e, t, n), r = I("crops", e, t, n);
-      return [Hg(I("x", e, t, n), s, r)];
+      return [jg(I("x", e, t, n), s, r)];
     }
     case "DepthToSpace": {
       let s = I("blockSize", e, t, n), r = I("dataFormat", e, t, n).toUpperCase();
-      return [cR(I("x", e, t, n), s, r)];
+      return [mR(I("x", e, t, n), s, r)];
     }
     case "BroadcastTo":
       return [nd(I("x", e, t, n), I("shape", e, t, n))];
     case "BroadcastArgs":
-      return [OE(I("s0", e, t, n), I("s1", e, t, n))];
+      return [VE(I("s0", e, t, n), I("s1", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function Yx(e, t, n, s) {
+function Qx(e, t, n, s) {
   let r = ((a, i, o) => {
     switch (a.category) {
       case "arithmetic":
-        return j(() => QW(a, i, o));
+        return j(() => o4(a, i, o));
       case "basic_math":
-        return j(() => ZW(a, i, o));
-      case "control":
-        return r4(a, i, o);
-      case "convolution":
-        return j(() => a4(a, i, o));
-      case "creation":
-        return j(() => i4(a, i, o));
-      case "dynamic":
-        return o4(a, i, o);
-      case "evaluation":
         return j(() => u4(a, i, o));
-      case "image":
-        return j(() => p4(a, i, o));
-      case "graph":
-        return j(() => l4(a, i, o));
-      case "logical":
-        return j(() => h4(a, i, o));
-      case "matrices":
-        return j(() => f4(a, i, o));
-      case "normalization":
+      case "control":
+        return f4(a, i, o);
+      case "convolution":
         return j(() => m4(a, i, o));
-      case "reduction":
+      case "creation":
         return j(() => g4(a, i, o));
-      case "slice_join":
-        return j(() => b4(a, i, o));
-      case "sparse":
+      case "dynamic":
+        return b4(a, i, o);
+      case "evaluation":
         return j(() => y4(a, i, o));
-      case "spectral":
+      case "image":
+        return j(() => k4(a, i, o));
+      case "graph":
         return j(() => v4(a, i, o));
+      case "logical":
+        return j(() => I4(a, i, o));
+      case "matrices":
+        return j(() => S4(a, i, o));
+      case "normalization":
+        return j(() => C4(a, i, o));
+      case "reduction":
+        return j(() => N4(a, i, o));
+      case "slice_join":
+        return j(() => T4(a, i, o));
+      case "sparse":
+        return j(() => $4(a, i, o));
+      case "spectral":
+        return j(() => _4(a, i, o));
       case "string":
-        return j(() => x4(a, i, o));
+        return j(() => A4(a, i, o));
       case "transformation":
-        return j(() => w4(a, i, o));
+        return j(() => E4(a, i, o));
       case "hash_table":
-        return d4(a, i, o, s);
+        return w4(a, i, o, s);
       case "custom":
-        let u = XS(a.op);
+        let u = e0(a.op);
         if (u && u.customExecutor)
-          return u.customExecutor(new YW(a, i, o));
+          return u.customExecutor(new i4(a, i, o));
         throw TypeError(`Custom op ${a.op} is not registered.`);
       default:
         throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);
@@ -18337,7 +18395,7 @@ function Yx(e, t, n, s) {
   })(e, t, n);
   return w.isPromise(r) ? r.then((a) => [].concat(a)) : [].concat(r);
 }
-var Qx = class {
+var Zx = class {
   constructor(e = {}, t = {}, n = {}, s = {}) {
     this.weightMap = e, this.tensorArrayMap = t, this.tensorListMap = n, this.functionMap = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds();
   }
@@ -18406,13 +18464,13 @@ var Qx = class {
       this.tensorListMap[t].clearAndClose(e);
   }
 };
-function Zx(e, t, n, s) {
+function Jx(e, t, n, s) {
   let r = /* @__PURE__ */ new Set(), a = [], i = null, o = null, u = /* @__PURE__ */ new Set(), l = Object.keys(e).map((d) => An(d)[0]), c = [];
   s != null && (c = s.map((d) => An(d.name)[0]));
   let p = [...t];
   for (; p.length > 0; ) {
     let d = p.pop();
-    if ((b0(d) || N4(d) || T4(d)) && i == null && (i = d, o = i.children.map((h) => h.name).filter((h) => r.has(h))), r.add(d.name), n[d.name] == null && l.indexOf(d.name) === -1 && c.indexOf(d.name) === -1) {
+    if ((k0(d) || P4(d) || z4(d)) && i == null && (i = d, o = i.children.map((h) => h.name).filter((h) => r.has(h))), r.add(d.name), n[d.name] == null && l.indexOf(d.name) === -1 && c.indexOf(d.name) === -1) {
       if (d.inputs.length === 0) {
         a.push(d.name);
         continue;
@@ -18424,7 +18482,7 @@ function Zx(e, t, n, s) {
   }
   return { inputs: e, outputs: t, usedNodes: r, missingInputs: a, dynamicNode: i, syncInputs: o };
 }
-function k4(e, t, n) {
+function R4(e, t, n) {
   let { usedNodes: s, inputs: r } = n, a = [], i = Object.keys(r).map((c) => An(c)[0]).map((c) => e.nodes[c]), o = e.initNodes;
   i.forEach((c) => {
     s.has(c.name) && a.push(c);
@@ -18442,22 +18500,22 @@ function k4(e, t, n) {
   }
   return l;
 }
-var I4 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
-var S4 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
-var C4 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
-function b0(e) {
-  return I4.indexOf(e.op) >= 0;
+var D4 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
+var F4 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
+var O4 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
+function k0(e) {
+  return D4.indexOf(e.op) >= 0;
 }
-function N4(e) {
-  return S4.indexOf(e.op) >= 0;
+function P4(e) {
+  return F4.indexOf(e.op) >= 0;
 }
-function T4(e) {
-  return C4.indexOf(e.op) >= 0;
+function z4(e) {
+  return O4.indexOf(e.op) >= 0;
 }
-var zm = class {
+var Lm = class {
   constructor(e, t) {
     this.graph = e, this.parent = t, this.compiledMap = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPERATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.intermediateTensors = {}, this.keepTensorForDebug = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((n) => {
-      this._functionExecutorMap[n] = new zm(e.functions[n], this);
+      this._functionExecutorMap[n] = new Lm(e.functions[n], this);
     });
   }
   get weightIds() {
@@ -18499,14 +18557,14 @@ var zm = class {
     return n.join(this.SEPERATOR) + "--" + s.join(this.SEPERATOR);
   }
   compile(e, t) {
-    let n = Zx(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
+    let n = Jx(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
     if (r != null)
       throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);
     if (s.length > 0) {
       let i = t.map((u) => u.name), o = Object.keys(e);
       throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`);
     }
-    return k4(this.graph, this.weightMap, n);
+    return R4(this.graph, this.weightMap, n);
   }
   execute(e, t) {
     e = this.mapInputs(e);
@@ -18518,7 +18576,7 @@ var zm = class {
     o == null && (o = this.compile(e, a), this.compiledMap.set(i, o));
     let u = {}, l = {};
     return j(() => {
-      let c = new Qx(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
+      let c = new Zx(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
       Object.keys(e).forEach((f) => {
         let [m, g] = An(f), b = [];
         b[g] = e[f], p[m] = b;
@@ -18527,7 +18585,7 @@ var zm = class {
       for (let f = 0; f < o.length; f++) {
         let m = o[f];
         if (!p[m.name]) {
-          let g = Yx(m, p, c, this._resourceManager);
+          let g = Qx(m, p, c, this._resourceManager);
           if (w.isPromise(g))
             throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);
           p[m.name] = g, this.checkTensorForDisposal(m.name, m, p, c, d, r, h);
@@ -18545,7 +18603,7 @@ var zm = class {
       o != null && (i[o.id] = (i[o.id] || 0) + t.children.length);
     }), t.inputs.forEach((o) => {
       if (o.category !== "control") {
-        let u = _W(o.name, n, s);
+        let u = LW(o.name, n, s);
         u != null && u.forEach((l) => {
           if (l && !l.kept && !r.has(l.id)) {
             let c = i[l.id];
@@ -18592,7 +18650,7 @@ var zm = class {
       console.warn(l.message);
     }
     this.resetIntermediateTensors();
-    let a = new Qx(this.weightMap, s, r, this.functionExecutorMap);
+    let a = new Zx(this.weightMap, s, r, this.functionExecutorMap);
     this.tensorsMap = await this.executeWithControlFlow(e, a, t, n);
     let i = t.map((l) => un(l, this.tensorsMap, a)), o = i.map((l) => l.id), u = Object.keys(e).map((l) => e[l].id);
     return this.keepIds = /* @__PURE__ */ new Set([...o, ...u, ...this.weightIds]), this.keepTensorForDebug || this.disposeTensorsMap(), this.parent == null && a.dispose(this.keepIds), i;
@@ -18604,7 +18662,7 @@ var zm = class {
   async executeWithControlFlow(e, t, n, s) {
     let r = Object.keys(e), a = r.map((y) => this.graph.nodes[An(y)[0]]), i = n.map((y) => An(y)[0]), o = i.map((y) => this.graph.nodes[y]);
     o.length === 0 && (o = this._outputs);
-    let { usedNodes: u, missingInputs: l, dynamicNode: c, syncInputs: p } = Zx(e, o, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((y) => ({ node: y, contexts: t.currentContext })), h = { ...this.weightMap };
+    let { usedNodes: u, missingInputs: l, dynamicNode: c, syncInputs: p } = Jx(e, o, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((y) => ({ node: y, contexts: t.currentContext })), h = { ...this.weightMap };
     Object.keys(e).forEach((y) => {
       let [v, x] = An(y), k = [];
       k[x] = e[y], h[v] = k;
@@ -18615,7 +18673,7 @@ var zm = class {
       await Promise.all(y);
     }
     c == null && !s && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");
-    let b = o.filter((y) => !b0(y) && !un(y.name, h, t)).map((y) => y.name);
+    let b = o.filter((y) => !k0(y) && !un(y.name, h, t)).map((y) => y.name);
     if (b.length > 0) {
       let y = "";
       throw c != null && (y = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`), new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`);
@@ -18629,7 +18687,7 @@ var zm = class {
       n.currentContext = c.contexts;
       let p = "";
       if (c.node.op === "Enter" && I("isConstant", c.node, s, n) && ([p] = Ts(c.node.name, n)), s[c.node.name] == null) {
-        let d = Yx(c.node, s, n, this._resourceManager);
+        let d = Qx(c.node, s, n, this._resourceManager);
         p || ([p] = Ts(c.node.name, n));
         let h = n.currentContext;
         w.isPromise(d) ? l.push(d.then((f) => (s[p] = f, n.currentContext = h, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u), f))) : (s[p] = d, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u));
@@ -18686,7 +18744,7 @@ var zm = class {
     });
   }
 };
-var $4 = class {
+var M4 = class {
   constructor(e = {}, t = {}) {
     this.hashTableNameToHandle = e, this.hashTableMap = t;
   }
@@ -18706,11 +18764,11 @@ var $4 = class {
       this.hashTableNameToHandle[e].dispose(), delete this.hashTableNameToHandle[e];
   }
 };
-var _4 = "?tfjs-format=file";
-var A4 = "model.json";
-var E4 = class {
+var L4 = "?tfjs-format=file";
+var B4 = "model.json";
+var V4 = class {
   constructor(e, t = {}) {
-    this.modelUrl = e, this.loadOptions = t, this.version = "n/a", t == null && (this.loadOptions = {}), this.resourceManager = new $4();
+    this.modelUrl = e, this.loadOptions = t, this.version = "n/a", t == null && (this.loadOptions = {}), this.resourceManager = new M4();
   }
   get modelVersion() {
     return this.version;
@@ -18762,9 +18820,9 @@ var E4 = class {
     let t = this.artifacts.modelTopology, n;
     this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null ? n = this.artifacts.userDefinedMetadata.signature : n = this.artifacts.signature, this.signature = n, this.version = `${t.versions.producer}.${t.versions.minConsumer}`;
     let s = En.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
-    if (this.executor = new zm(qx.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
-      let r = qx.Instance.transformGraph(e.modelInitializer);
-      this.initializer = new zm(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
+    if (this.executor = new Lm(jx.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
+      let r = jx.Instance.transformGraph(e.modelInitializer);
+      this.initializer = new Lm(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
     }
     return true;
   }
@@ -18817,22 +18875,22 @@ var E4 = class {
     this.executor.dispose(), this.initializer && this.initializer.dispose(), this.resourceManager.dispose();
   }
 };
-async function Dpe(e, t = {}) {
+async function jpe(e, t = {}) {
   if (e == null)
     throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
-  t == null && (t = {}), t.fromTFHub && e.load == null && (e.endsWith("/") || (e = e + "/"), e = `${e}${A4}${_4}`);
-  let n = new E4(e, t);
+  t == null && (t = {}), t.fromTFHub && e.load == null && (e.endsWith("/") || (e = e + "/"), e = `${e}${B4}${L4}`);
+  let n = new V4(e, t);
   return await n.load(), n;
 }
-var Fpe = "0.0.0";
-var R4 = {};
-Ae(R4, { CSVDataset: () => _0, Dataset: () => Jo, FileDataSource: () => P0, TextLineDataset: () => $0, URLDataSource: () => z0, array: () => tU, csv: () => pU, func: () => hU, generator: () => fU, microphone: () => gU, version_data: () => bU, webcam: () => mU, zip: () => nU });
-var D4 = xa(Wd());
-var F4 = xa(Wd());
-function O4(e, t) {
-  return Rd(e, t);
+var Kpe = "0.0.0";
+var W4 = {};
+Ae(W4, { CSVDataset: () => F0, Dataset: () => eu, FileDataSource: () => V0, TextLineDataset: () => D0, URLDataSource: () => W0, array: () => dU, csv: () => kU, func: () => IU, generator: () => SU, microphone: () => NU, version_data: () => TU, webcam: () => CU, zip: () => pU });
+var U4 = wa(Gd());
+var G4 = wa(Gd());
+function H4(e, t) {
+  return Fd(e, t);
 }
-function Rd(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set()) {
+function Fd(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set()) {
   if (e == null)
     return null;
   if (typeof Blob == "function" && e instanceof Blob)
@@ -18845,11 +18903,11 @@ function Rd(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set())
   if (r.recurse && r.value !== null)
     throw new Error("A deep map function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (to(e)) {
+    if (so(e)) {
       let a = Array.isArray(e) ? [] : {};
       s.add(e);
       for (let i in e) {
-        let o = e[i], u = Rd(o, t, n, s);
+        let o = e[i], u = Fd(o, t, n, s);
         a[i] = u;
       }
       return s.delete(e), e.__proto__ && (a.__proto__ = e.__proto__), a;
@@ -18858,10 +18916,10 @@ function Rd(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set())
   else
     return n.set(e, r.value), r.value;
 }
-function P4(e, t = v0) {
-  return y0(e, t);
+function q4(e, t = S0) {
+  return I0(e, t);
 }
-function y0(e, t, n = /* @__PURE__ */ new Set()) {
+function I0(e, t, n = /* @__PURE__ */ new Set()) {
   let s = e[0];
   if (n.has(s))
     throw new Error("Circular references are not supported.");
@@ -18869,11 +18927,11 @@ function y0(e, t, n = /* @__PURE__ */ new Set()) {
   if (r.recurse && r.value !== null)
     throw new Error("A deep zip function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (to(s)) {
+    if (so(s)) {
       let a = Array.isArray(s) ? [] : {};
       n.add(s);
       for (let i in s) {
-        let o = e.map((l) => l[i]), u = y0(o, t, n);
+        let o = e.map((l) => l[i]), u = I0(o, t, n);
         a[i] = u;
       }
       return n.delete(s), a;
@@ -18882,12 +18940,12 @@ function y0(e, t, n = /* @__PURE__ */ new Set()) {
   else
     return r.value;
 }
-function v0(e) {
-  return e === null ? null : to(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
+function S0(e) {
+  return e === null ? null : so(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
-async function x0(e, t) {
+async function C0(e, t) {
   let n = /* @__PURE__ */ new Map();
-  Rd(e, t, n);
+  Fd(e, t, n);
   for (let r of Array.from(n.keys())) {
     let a = n.get(r);
     if (w.isPromise(a)) {
@@ -18895,31 +18953,31 @@ async function x0(e, t) {
       n.set(r, i);
     }
   }
-  return Rd(e, t, n);
+  return Fd(e, t, n);
 }
-function to(e) {
+function so(e) {
   let t = false;
   if (X().get("IS_BROWSER"))
     t = e instanceof TextDecoder;
   else {
-    let { StringDecoder: n } = Ww();
+    let { StringDecoder: n } = qw();
     t = e instanceof n;
   }
   return e != null && !ArrayBuffer.isView(e) && (Array.isArray(e) || typeof e == "object" && !(e instanceof et) && !(e instanceof Promise) && !t);
 }
-function z4(e) {
-  return e == null || M4(e) || Array.isArray(e) || typeof e == "object" && e instanceof et || w.isTypedArray(e);
+function j4(e) {
+  return e == null || K4(e) || Array.isArray(e) || typeof e == "object" && e instanceof et || w.isTypedArray(e);
 }
-function M4(e) {
+function K4(e) {
   return e === null || typeof e != "object" && typeof e != "function";
 }
-function L4(e) {
-  return O4(e, B4);
+function X4(e) {
+  return H4(e, Y4);
 }
-function B4(e) {
-  return e instanceof et ? { value: e.clone(), recurse: false } : to(e) ? { value: null, recurse: true } : { value: e, recurse: false };
+function Y4(e) {
+  return e instanceof et ? { value: e.clone(), recurse: false } : so(e) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
-var w0 = class {
+var N0 = class {
   constructor(e) {
     if (this.capacity = e, this.begin = 0, this.end = 0, e == null)
       throw new RangeError("Can't create a ring buffer of unknown capacity.");
@@ -18986,9 +19044,9 @@ var w0 = class {
     return this.set(t, this.pop()), n;
   }
 };
-var k0 = class extends w0 {
+var T0 = class extends N0 {
   constructor() {
-    super(k0.INITIAL_CAPACITY);
+    super(T0.INITIAL_CAPACITY);
   }
   isFull() {
     return false;
@@ -19006,19 +19064,19 @@ var k0 = class extends w0 {
     this.data = t, this.capacity = e, this.doubledCapacity = 2 * this.capacity, this.begin = 0, this.end = n;
   }
 };
-var I0 = k0;
-I0.INITIAL_CAPACITY = 32;
-function S0(e) {
-  return new U4(e);
+var $0 = T0;
+$0.INITIAL_CAPACITY = 32;
+function _0(e) {
+  return new J4(e);
 }
-function Yy(e) {
-  return new G4(e);
+function Zy(e) {
+  return new eU(e);
 }
-function V4(e, t) {
-  return new C0(e, t);
+function Q4(e, t) {
+  return new A0(e, t);
 }
-function W4(e, t = N0.FAIL) {
-  return new J4(e, t);
+function Z4(e, t = E0.FAIL) {
+  return new lU(e, t);
 }
 var Ut = class {
   async toArray() {
@@ -19044,22 +19102,22 @@ var Ut = class {
       t = await this.next(), n = e(t.value);
   }
   handleErrors(e) {
-    return new Q4(this, e);
+    return new oU(this, e);
   }
   filter(e) {
-    return new X4(this, e);
+    return new aU(this, e);
   }
   map(e) {
-    return new Y4(this, e);
+    return new iU(this, e);
   }
   mapAsync(e) {
-    return new Jx(this, e);
+    return new ew(this, e);
   }
   serialMapAsync(e) {
-    return new Jx(this, e).serial();
+    return new ew(this, e).serial();
   }
   flatmap(e) {
-    return new Z4(this, e);
+    return new uU(this, e);
   }
   async forEachAsync(e) {
     return this.map(e).resolveFully();
@@ -19068,31 +19126,31 @@ var Ut = class {
     return this.serialMapAsync(e).resolveWhile((t) => t === true);
   }
   rowMajorBatch(e, t = true) {
-    return new K4(this, e, t);
+    return new rU(this, e, t);
   }
-  columnMajorBatch(e, t = true, n = v0) {
-    return this.rowMajorBatch(e, t).map((r) => P4(r, n));
+  columnMajorBatch(e, t = true, n = S0) {
+    return this.rowMajorBatch(e, t).map((r) => q4(r, n));
   }
   concatenate(e, t) {
-    return new C0(S0([this, e]), t);
+    return new A0(_0([this, e]), t);
   }
   take(e) {
-    return e < 0 || e == null ? this : new j4(this, e);
+    return e < 0 || e == null ? this : new sU(this, e);
   }
   skip(e) {
-    return e < 0 || e == null ? this : new q4(this, e);
+    return e < 0 || e == null ? this : new nU(this, e);
   }
   prefetch(e) {
-    return new T0(this, e);
+    return new R0(this, e);
   }
   shuffle(e, t) {
-    return new eU(this, e, t);
+    return new cU(this, e, t);
   }
   serial() {
-    return new H4(this);
+    return new tU(this);
   }
 };
-var U4 = class extends Ut {
+var J4 = class extends Ut {
   constructor(e) {
     super(), this.items = e, this.trav = 0;
   }
@@ -19103,10 +19161,10 @@ var U4 = class extends Ut {
     if (this.trav >= this.items.length)
       return { value: null, done: true };
     let e = this.items[this.trav];
-    return this.trav++, { value: L4(e), done: false };
+    return this.trav++, { value: X4(e), done: false };
   }
 };
-var G4 = class extends Ut {
+var eU = class extends Ut {
   constructor(e) {
     super(), this.nextFn = e;
   }
@@ -19121,7 +19179,7 @@ var G4 = class extends Ut {
     }
   }
 };
-var H4 = class extends Ut {
+var tU = class extends Ut {
   constructor(e) {
     super(), this.upstream = e, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19135,7 +19193,7 @@ var H4 = class extends Ut {
     return this.upstream.next();
   }
 };
-var q4 = class extends Ut {
+var nU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.maxCount = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19155,7 +19213,7 @@ var q4 = class extends Ut {
     return this.upstream.next();
   }
 };
-var j4 = class extends Ut {
+var sU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.maxCount = t, this.count = 0;
   }
@@ -19166,7 +19224,7 @@ var j4 = class extends Ut {
     return this.count++ >= this.maxCount ? { value: null, done: true } : this.upstream.next();
   }
 };
-var K4 = class extends Ut {
+var rU = class extends Ut {
   constructor(e, t, n = true) {
     super(), this.upstream = e, this.batchSize = t, this.enableSmallLastBatch = n, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19187,7 +19245,7 @@ var K4 = class extends Ut {
     return { value: e, done: false };
   }
 };
-var X4 = class extends Ut {
+var aU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.predicate = t, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19206,7 +19264,7 @@ var X4 = class extends Ut {
     }
   }
 };
-var Y4 = class extends Ut {
+var iU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.transform = t;
   }
@@ -19223,7 +19281,7 @@ var Y4 = class extends Ut {
     return { value: n, done: false };
   }
 };
-var Q4 = class extends Ut {
+var oU = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.handler = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
   }
@@ -19243,7 +19301,7 @@ var Q4 = class extends Ut {
       }
   }
 };
-var Jx = class extends Ut {
+var ew = class extends Ut {
   constructor(e, t) {
     super(), this.upstream = e, this.transform = t;
   }
@@ -19260,9 +19318,9 @@ var Jx = class extends Ut {
     return { value: n, done: false };
   }
 };
-var Qy = class extends Ut {
+var Jy = class extends Ut {
   constructor() {
-    super(), this.outputQueue = new I0(), this.lastRead = Promise.resolve({ value: null, done: false });
+    super(), this.outputQueue = new $0(), this.lastRead = Promise.resolve({ value: null, done: false });
   }
   async next() {
     return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
@@ -19274,7 +19332,7 @@ var Qy = class extends Ut {
     return { value: this.outputQueue.shift(), done: false };
   }
 };
-var Z4 = class extends Qy {
+var uU = class extends Jy {
   constructor(e, t) {
     super(), this.upstream = e, this.transform = t;
   }
@@ -19292,7 +19350,7 @@ var Z4 = class extends Qy {
     return true;
   }
 };
-var C0 = class extends Ut {
+var A0 = class extends Ut {
   constructor(e, t) {
     super(), this.baseErrorHandler = t, this.lastRead = null, this.iterator = null, this.moreIterators = e;
   }
@@ -19313,8 +19371,8 @@ var C0 = class extends Ut {
     return t.done ? (this.iterator = null, this.readFromChain(e)) : t;
   }
 };
-var N0 = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(N0 || {});
-var J4 = class extends Ut {
+var E0 = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(E0 || {});
+var lU = class extends Ut {
   constructor(e, t = 0) {
     super(), this.iterators = e, this.mismatchMode = t, this.count = 0, this.currentPromise = null;
   }
@@ -19327,7 +19385,7 @@ var J4 = class extends Ut {
     function s(a) {
       return a instanceof Ut ? { value: a.next().then((o) => (t++, o.done && n++, o.value)), recurse: false } : { value: null, recurse: true };
     }
-    let r = await x0(this.iterators, s);
+    let r = await C0(this.iterators, s);
     if (t === n)
       return { value: null, done: true };
     if (n > 0)
@@ -19345,9 +19403,9 @@ var J4 = class extends Ut {
     return this.currentPromise = this.nextState(this.currentPromise), this.currentPromise;
   }
 };
-var T0 = class extends Ut {
+var R0 = class extends Ut {
   constructor(e, t) {
-    super(), this.upstream = e, this.bufferSize = t, this.buffer = new w0(t);
+    super(), this.upstream = e, this.bufferSize = t, this.buffer = new N0(t);
   }
   summary() {
     return `${this.upstream.summary()} -> Prefetch`;
@@ -19362,9 +19420,9 @@ var T0 = class extends Ut {
     return this.refill(), this.buffer.shift();
   }
 };
-var eU = class extends T0 {
+var cU = class extends R0 {
   constructor(e, t, n) {
-    super(e, t), this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = F4.alea(n || w.now().toString()), this.lastRead = Promise.resolve({ value: null, done: false });
+    super(e, t), this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = G4.alea(n || w.now().toString()), this.lastRead = Promise.resolve({ value: null, done: false });
   }
   async next() {
     return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
@@ -19386,7 +19444,7 @@ var eU = class extends T0 {
     return { value: null, done: true };
   }
 };
-var Jo = class {
+var eu = class {
   constructor() {
     this.size = null;
   }
@@ -19395,7 +19453,7 @@ var Jo = class {
     w.assert(e > 0, () => `batchSize needs to be positive, but it is
       ${e}`);
     let s;
-    return this.size === 1 / 0 || this.size == null ? s = this.size : t ? s = Math.ceil(this.size / e) : s = Math.floor(this.size / e), _n(async () => (await n.iterator()).columnMajorBatch(e, t, sU), s);
+    return this.size === 1 / 0 || this.size == null ? s = this.size : t ? s = Math.ceil(this.size / e) : s = Math.floor(this.size / e), _n(async () => (await n.iterator()).columnMajorBatch(e, t, hU), s);
   }
   concatenate(e) {
     let t = this, n;
@@ -19425,8 +19483,8 @@ var Jo = class {
   repeat(e) {
     let t = this, n;
     return this.size != null && e > 0 ? n = this.size * e : e === 0 ? n = 0 : this.size != null && (e === void 0 || e < 0) ? n = 1 / 0 : n = null, _n(async () => {
-      let s = Yy(async () => ({ value: await t.iterator(), done: false }));
-      return V4(s.take(e));
+      let s = Zy(async () => ({ value: await t.iterator(), done: false }));
+      return Q4(s.take(e));
     }, n);
   }
   skip(e) {
@@ -19436,7 +19494,7 @@ var Jo = class {
   shuffle(e, t, n = true) {
     if (e == null || e < 0)
       throw this.size == null ? new RangeError("`Dataset.shuffle()` requires bufferSize to be specified.") : new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);
-    let s = this, r = D4.alea(t || w.now().toString());
+    let s = this, r = U4.alea(t || w.now().toString());
     return _n(async () => {
       let a = r.int32();
       return n && (a += r.int32()), (await s.iterator()).shuffle(e, a.toString());
@@ -19457,9 +19515,9 @@ var Jo = class {
     return (await this.iterator()).toArrayForTest();
   }
 };
-Jo.MAX_BUFFER_SIZE = 1e4;
+eu.MAX_BUFFER_SIZE = 1e4;
 function _n(e, t = null) {
-  return new class extends Jo {
+  return new class extends eu {
     constructor() {
       super(...arguments), this.size = t;
     }
@@ -19468,11 +19526,11 @@ function _n(e, t = null) {
     }
   }();
 }
-function tU(e) {
-  return _n(async () => S0(e), e.length);
+function dU(e) {
+  return _n(async () => _0(e), e.length);
 }
-function nU(e) {
-  if (!to(e))
+function pU(e) {
+  if (!so(e))
     throw new Error("The argument to zip() must be an object or array.");
   let t;
   if (Array.isArray(e))
@@ -19482,28 +19540,28 @@ function nU(e) {
     for (let n in e)
       t = t == null ? e[n].size : Math.min(t, e[n].size);
   return _n(async () => {
-    let n = await x0(e, (s) => {
-      if (s instanceof Jo)
+    let n = await C0(e, (s) => {
+      if (s instanceof eu)
         return { value: s.iterator(), recurse: false };
-      if (to(s))
+      if (so(s))
         return { value: null, recurse: true };
       throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
     });
-    return W4(n, 1);
+    return Z4(n, 1);
   }, t);
 }
-function sU(e) {
+function hU(e) {
   if (e === null)
     return null;
   let t = e[0];
-  return z4(t) ? { value: rU(e), recurse: false } : { value: null, recurse: true };
+  return j4(t) ? { value: fU(e), recurse: false } : { value: null, recurse: true };
 }
-function rU(e) {
+function fU(e) {
   if (e.length === 0)
     throw new Error("Can't make a batch of zero elements.");
-  return e[0] instanceof et ? Jn(e) : fs(e);
+  return e[0] instanceof et ? es(e) : fs(e);
 }
-var $0 = class extends Jo {
+var D0 = class extends eu {
   constructor(e) {
     super(), this.input = e;
   }
@@ -19513,14 +19571,14 @@ var $0 = class extends Jo {
   }
 };
 var Kc = '"';
-var Tu = Symbol("out");
-var ew = Symbol("field");
+var $u = Symbol("out");
+var tw = Symbol("field");
 var Xc = Symbol("quote");
-var qf = Symbol("quoteafterquote");
-var tw = Symbol("quoteinquote");
-var _0 = class extends Jo {
+var jf = Symbol("quoteafterquote");
+var nw = Symbol("quoteinquote");
+var F0 = class extends eu {
   constructor(e, t) {
-    super(), this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new $0(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
+    super(), this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new D0(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
   }
   async columnNames() {
     return this.columnNamesValidated || await this.setColumnNames(), this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
@@ -19597,10 +19655,10 @@ var _0 = class extends Jo {
     return e === "1" || e.toLowerCase() === "true" ? 1 : 0;
   }
   parseRow(e, t = true) {
-    let n = [], s = 0, r = e.length, a = Tu;
+    let n = [], s = 0, r = e.length, a = $u;
     for (let i = 0; i < r; i++)
       switch (a) {
-        case Tu:
+        case $u:
           switch (e.charAt(i)) {
             case Kc:
               s = i + 1, a = Xc;
@@ -19608,17 +19666,17 @@ var _0 = class extends Jo {
             case this.delimiter:
               if (s = i + 1, this.delimiter === " " && this.delimWhitespace)
                 break;
-              n.push(""), a = Tu;
+              n.push(""), a = $u;
               break;
             default:
-              a = ew, s = i;
+              a = tw, s = i;
               break;
           }
           break;
-        case ew:
+        case tw:
           switch (e.charAt(i)) {
             case this.delimiter:
-              n.push(e.substring(s, i)), a = Tu, s = i + 1;
+              n.push(e.substring(s, i)), a = $u, s = i + 1;
               break;
             default:
           }
@@ -19626,25 +19684,25 @@ var _0 = class extends Jo {
         case Xc:
           switch (e.charAt(i)) {
             case Kc:
-              a = qf;
+              a = jf;
               break;
             default:
           }
           break;
-        case qf:
+        case jf:
           switch (e.charAt(i)) {
             case this.delimiter:
-              n.push(e.substring(s, i - 1)), a = Tu, s = i + 1;
+              n.push(e.substring(s, i - 1)), a = $u, s = i + 1;
               break;
             case Kc:
               a = Xc;
               break;
             default:
-              a = tw;
+              a = nw;
               break;
           }
           break;
-        case tw:
+        case nw:
           switch (e.charAt(i)) {
             case Kc:
               a = Xc;
@@ -19654,12 +19712,12 @@ var _0 = class extends Jo {
           break;
         default:
       }
-    if (a === qf ? n.push(e.substring(s, r - 1)) : n.push(e.substring(s)), t && n.length !== this.fullColumnNames.length)
+    if (a === jf ? n.push(e.substring(s, r - 1)) : n.push(e.substring(s)), t && n.length !== this.fullColumnNames.length)
       throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);
     return n;
   }
 };
-var A0 = class extends Ut {
+var O0 = class extends Ut {
   constructor(e) {
     super(), this.microphoneConfig = e, this.isClosed = false, this.fftSize = e.fftSize || 1024;
     let t = Math.log2(this.fftSize);
@@ -19674,7 +19732,7 @@ var A0 = class extends Ut {
   static async create(e = {}) {
     if (!X().get("IS_BROWSER"))
       throw new Error("microphone API is only supported in browser environment.");
-    let t = new A0(e);
+    let t = new O0(e);
     return await t.start(), t;
   }
   async start() {
@@ -19736,14 +19794,14 @@ var A0 = class extends Ut {
     return n.set(e, n.length - e.length), fs(n, t);
   }
 };
-var E0 = class extends Ut {
+var P0 = class extends Ut {
   constructor(e, t) {
     if (super(), this.webcamVideoElement = e, this.webcamConfig = t, this.isClosed = true, this.resize = false, this.needToResize())
       if (this.resize = true, this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth], this.cropBoxInd = Zt([0], "int32"), this.webcamConfig.centerCrop) {
         let n = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width, s = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height, r = (1 - n) / 2, a = (1 - s) / 2, i = r + n, o = s + a;
-        this.cropBox = Ki([a, r, o, i], [1, 4]);
+        this.cropBox = Yi([a, r, o, i], [1, 4]);
       } else
-        this.cropBox = Ki([0, 0, 1, 1], [1, 4]);
+        this.cropBox = Yi([0, 0, 1, 1], [1, 4]);
   }
   summary() {
     return "webcam";
@@ -19756,7 +19814,7 @@ var E0 = class extends Ut {
         throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
       e.width = t.resizeWidth, e.height = t.resizeHeight;
     }
-    let n = new E0(e, t);
+    let n = new P0(e, t);
     return await n.start(), n;
   }
   async start() {
@@ -19784,7 +19842,7 @@ var E0 = class extends Ut {
       return { value: null, done: true };
     let e;
     try {
-      e = Nk.fromPixels(this.webcamVideoElement);
+      e = Ak.fromPixels(this.webcamVideoElement);
     } catch (t) {
       throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`);
     }
@@ -19805,7 +19863,7 @@ var E0 = class extends Ut {
   cropAndResizeFrame(e) {
     return j(() => {
       let t = Pn(ce(e, "float32"), 0), n;
-      n = qn.cropAndResize(t, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
+      n = jn.cropAndResize(t, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
       let s = n.shape;
       return G(n, s.slice(1));
     });
@@ -19826,16 +19884,16 @@ var E0 = class extends Ut {
     throw new Error("Can not convert infinite video stream to array.");
   }
 };
-var R0 = class {
+var z0 = class {
 };
-var D0 = class extends Ut {
+var M0 = class extends Ut {
   split(e) {
-    return new aU(this, e);
+    return new mU(this, e);
   }
 };
-var aU = class extends D0 {
+var mU = class extends M0 {
   constructor(e, t) {
-    super(), this.upstream = e, this.impl = new iU(e, t);
+    super(), this.upstream = e, this.impl = new gU(e, t);
   }
   summary() {
     return this.impl.summary();
@@ -19844,7 +19902,7 @@ var aU = class extends D0 {
     return this.impl.next();
   }
 };
-var iU = class extends Qy {
+var gU = class extends Jy {
   constructor(e, t) {
     super(), this.upstream = e, this.separator = t, this.carryover = "";
   }
@@ -19862,14 +19920,14 @@ var iU = class extends Qy {
     return this.carryover = t[t.length - 1], true;
   }
 };
-var oU = class extends Ut {
+var bU = class extends Ut {
   decodeUTF8() {
-    return new uU(this);
+    return new yU(this);
   }
 };
-var uU = class extends D0 {
+var yU = class extends M0 {
   constructor(e) {
-    super(), this.upstream = e, this.impl = new lU(e);
+    super(), this.upstream = e, this.impl = new vU(e);
   }
   summary() {
     return this.impl.summary();
@@ -19878,12 +19936,12 @@ var uU = class extends D0 {
     return this.impl.next();
   }
 };
-var lU = class extends Qy {
+var vU = class extends Jy {
   constructor(e) {
     if (super(), this.upstream = e, X().get("IS_BROWSER"))
       this.decoder = new TextDecoder("utf-8");
     else {
-      let { StringDecoder: t } = Ww();
+      let { StringDecoder: t } = qw();
       this.decoder = new t("utf8");
     }
   }
@@ -19899,7 +19957,7 @@ var lU = class extends Qy {
     return X().get("IS_BROWSER") ? n = this.decoder.decode(t, { stream: true }) : n = this.decoder.write(Buffer.from(t.buffer)), this.outputQueue.push(n), true;
   }
 };
-var F0 = class extends oU {
+var L0 = class extends bU {
   constructor(e, t = {}) {
     super(), this.file = e, this.options = t, w.assert(e instanceof Uint8Array || (X().get("IS_BROWSER") ? e instanceof File || e instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now."), this.offset = t.offset || 0, this.chunkSize = t.chunkSize || 1024 * 1024;
   }
@@ -19926,72 +19984,72 @@ var F0 = class extends oU {
     }), done: false };
   }
 };
-async function cU(e, t = {}, n) {
+async function xU(e, t = {}, n) {
   let s, r;
-  typeof e == "string" ? s = e : (s = e.url, r = dU(e));
+  typeof e == "string" ? s = e : (s = e.url, r = wU(e));
   let a = await (n || w.fetch)(s, r);
   if (a.ok) {
     let i = new Uint8Array(await a.arrayBuffer());
-    return new F0(i, t);
+    return new L0(i, t);
   } else
     throw new Error(a.statusText);
 }
-var dU = (e) => ({ method: e.method, headers: e.headers, body: e.body, mode: e.mode, credentials: e.credentials, cache: e.cache, redirect: e.redirect, referrer: e.referrer, integrity: e.integrity });
-function O0(e) {
+var wU = (e) => ({ method: e.method, headers: e.headers, body: e.body, mode: e.mode, credentials: e.credentials, cache: e.cache, redirect: e.redirect, referrer: e.referrer, integrity: e.integrity });
+function B0(e) {
   return typeof e == "string" && e.slice(0, 7) === "file://";
 }
-var P0 = class extends R0 {
+var V0 = class extends z0 {
   constructor(e, t = {}) {
     super(), this.input = e, this.options = t;
   }
   async iterator() {
-    if (O0(this.input) && X().get("IS_NODE")) {
-      let e = tg();
+    if (B0(this.input) && X().get("IS_NODE")) {
+      let e = sg();
       this.input = e.readFileSync(this.input.slice(7));
     }
-    return new F0(this.input, this.options);
+    return new L0(this.input, this.options);
   }
 };
-var z0 = class extends R0 {
+var W0 = class extends z0 {
   constructor(e, t = {}) {
     super(), this.url = e, this.fileOptions = t;
   }
   async iterator() {
-    return O0(this.url) ? new P0(this.url, this.fileOptions).iterator() : cU(this.url, this.fileOptions);
+    return B0(this.url) ? new V0(this.url, this.fileOptions).iterator() : xU(this.url, this.fileOptions);
   }
 };
-function pU(e, t = {}) {
-  return new _0(new z0(e), t);
+function kU(e, t = {}) {
+  return new F0(new W0(e), t);
 }
-function hU(e) {
-  let t = Yy(e);
+function IU(e) {
+  let t = Zy(e);
   return _n(async () => t);
 }
-function fU(e) {
+function SU(e) {
   return _n(async () => {
     let t = await e();
-    return Yy(() => t.next());
+    return Zy(() => t.next());
   });
 }
-async function mU(e, t) {
-  return E0.create(e, t);
+async function CU(e, t) {
+  return P0.create(e, t);
 }
-async function gU(e) {
-  return A0.create(e);
+async function NU(e) {
+  return O0.create(e);
 }
-var bU = "0.0.0";
+var TU = "0.0.0";
 function be(e, t) {
   Array.isArray(e) || (e = [e]), e.forEach((n) => {
     n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the CPU backend.`);
   });
 }
-var yU = xs.whereImpl;
-var M0 = class extends nl {
+var $U = xs.whereImpl;
+var U0 = class extends sl {
   constructor() {
-    super(), this.blockSize = 48, this.firstUse = true, this.data = new Ud(this, Ss());
+    super(), this.blockSize = 48, this.firstUse = true, this.data = new Hd(this, Ss());
   }
   nextDataId() {
-    return M0.nextDataId++;
+    return U0.nextDataId++;
   }
   write(e, t, n) {
     this.firstUse && (this.firstUse = false, X().get("IS_NODE") && S.warn(`
@@ -20076,7 +20134,7 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
   where(e) {
     be([e], "where");
     let t = this.readSync(e.dataId);
-    return yU(e.shape, t);
+    return $U(e.shape, t);
   }
   dispose() {
   }
@@ -20087,23 +20145,23 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
     return super.epsilon();
   }
 };
-var L0 = M0;
-L0.nextDataId = 0;
-var Zy = {};
-Ae(Zy, { addImpl: () => V0, bincountImpl: () => ev, bincountReduceImpl: () => W0, ceilImpl: () => U0, concatImpl: () => tv, equalImpl: () => G0, expImpl: () => q0, expm1Impl: () => K0, floorImpl: () => X0, gatherNdImpl: () => Y0, gatherV2Impl: () => Q0, greaterEqualImpl: () => J0, greaterImpl: () => Z0, lessEqualImpl: () => tC, lessImpl: () => eC, linSpaceImpl: () => nC, logImpl: () => sC, maxImpl: () => rC, maximumImpl: () => aC, minimumImpl: () => iC, multiplyImpl: () => nv, negImpl: () => oC, notEqualImpl: () => uC, prodImpl: () => lC, rangeImpl: () => rv, rsqrtImpl: () => cC, sigmoidImpl: () => iG, simpleAbsImpl: () => B0, sliceImpl: () => Fd, sparseFillEmptyRowsImpl: () => pC, sparseReshapeImpl: () => hC, sparseSegmentReductionImpl: () => av, sqrtImpl: () => lG, squaredDifferenceImpl: () => fC, stridedSliceImpl: () => mC, stringNGramsImpl: () => gC, stringSplitImpl: () => bC, stringToHashBucketFastImpl: () => yC, subImpl: () => vC, tileImpl: () => xC, topKImpl: () => kC, transposeImpl: () => sv, uniqueImpl: () => IC });
-function B0(e) {
+var G0 = U0;
+G0.nextDataId = 0;
+var ev = {};
+Ae(ev, { addImpl: () => q0, bincountImpl: () => nv, bincountReduceImpl: () => j0, ceilImpl: () => K0, concatImpl: () => sv, equalImpl: () => X0, expImpl: () => Q0, expm1Impl: () => J0, floorImpl: () => eC, gatherNdImpl: () => tC, gatherV2Impl: () => nC, greaterEqualImpl: () => rC, greaterImpl: () => sC, lessEqualImpl: () => iC, lessImpl: () => aC, linSpaceImpl: () => oC, logImpl: () => uC, maxImpl: () => lC, maximumImpl: () => cC, minimumImpl: () => dC, multiplyImpl: () => rv, negImpl: () => pC, notEqualImpl: () => hC, prodImpl: () => fC, rangeImpl: () => iv, rsqrtImpl: () => mC, sigmoidImpl: () => gG, simpleAbsImpl: () => H0, sliceImpl: () => Pd, sparseFillEmptyRowsImpl: () => bC, sparseReshapeImpl: () => yC, sparseSegmentReductionImpl: () => ov, sqrtImpl: () => vG, squaredDifferenceImpl: () => vC, stridedSliceImpl: () => xC, stringNGramsImpl: () => wC, stringSplitImpl: () => kC, stringToHashBucketFastImpl: () => IC, subImpl: () => SC, tileImpl: () => CC, topKImpl: () => TC, transposeImpl: () => av, uniqueImpl: () => $C });
+function H0(e) {
   let t = new Float32Array(e.length);
   for (let n = 0; n < e.length; ++n)
     t[n] = Math.abs(e[n]);
   return t;
 }
-var vU = (e) => {
+var _U = (e) => {
   let { x: t } = e.inputs, n = e.backend;
   be(t, "abs");
   let s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId).values;
-  return s = B0(r), n.makeOutput(s, t.shape, t.dtype);
+  return s = H0(r), n.makeOutput(s, t.shape, t.dtype);
 };
-var xU = { kernelName: io, backendName: "cpu", kernelFunc: vU };
+var AU = { kernelName: uo, backendName: "cpu", kernelFunc: _U };
 function At(e) {
   return (t, n, s, r, a) => {
     let i = S.assertAndGetBroadcastShape(t, n), o = i.length, u = w.computeStrides(i), l = w.sizeFromShape(i), c = w.getTypedArrayFromDType(a, l), p = t.length, d = n.length, h = w.computeStrides(t), f = w.computeStrides(n), m = S.getBroadcastDims(t, i), g = S.getBroadcastDims(n, i);
@@ -20126,10 +20184,10 @@ function Rn(e) {
   let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = n.makeTensorInfo(s.shape, "complex64"), u = n.data.get(o.dataId);
   return u.complexTensorInfos = { real: n.makeTensorInfo(s.shape, "float32", a), imag: n.makeTensorInfo(r.shape, "float32", i) }, o;
 }
-var wU = { kernelName: jd, backendName: "cpu", kernelFunc: Rn };
-function Dd(e, t, n = "float32") {
+var EU = { kernelName: Xd, backendName: "cpu", kernelFunc: Rn };
+function Od(e, t, n = "float32") {
   if (n === "complex64") {
-    let r = Dd(e, t, "float32"), a = Dd(e, t, "float32");
+    let r = Od(e, t, "float32"), a = Od(e, t, "float32");
     return Rn({ inputs: { real: r, imag: a }, backend: e });
   }
   let s = w.makeZerosTypedArray(w.sizeFromShape(t), n);
@@ -20139,22 +20197,22 @@ function Os(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   return n.incRef(s.dataId), { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-var kU = { kernelName: Ba, backendName: "cpu", kernelFunc: Os };
-function ma(e) {
+var RU = { kernelName: Wa, backendName: "cpu", kernelFunc: Os };
+function ga(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.real, a = n.data.get(r.dataId).values;
   return n.makeTensorInfo(r.shape, r.dtype, a);
 }
-var IU = { kernelName: np, backendName: "cpu", kernelFunc: ma };
-function xr(e) {
+var DU = { kernelName: rp, backendName: "cpu", kernelFunc: ga };
+function wr(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return Os({ inputs: { x: r }, backend: n });
-    let i = Dd(n, r.shape, r.dtype), o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Rn({ inputs: { real: o, imag: i }, backend: n });
+    let i = Od(n, r.shape, r.dtype), o = wr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Rn({ inputs: { real: o, imag: i }, backend: n });
     return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   }
   if (r.dtype === "complex64") {
-    let i = ma({ inputs: { input: r }, backend: n }), o = xr({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
+    let i = ga({ inputs: { input: r }, backend: n }), o = wr({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
     return n.disposeIntermediateTensorInfo(i), o;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
@@ -20171,7 +20229,7 @@ function xr(e) {
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var SU = { kernelName: Na, backendName: "cpu", kernelFunc: xr };
+var FU = { kernelName: Ta, backendName: "cpu", kernelFunc: wr };
 function Gt(e, t, n, s) {
   return n == null ? ({ inputs: r, backend: a }) => {
     let { a: i, b: o } = r, u = a;
@@ -20181,7 +20239,7 @@ function Gt(e, t, n, s) {
   } : ({ inputs: r, backend: a }) => {
     let { a: i, b: o } = r, u = a;
     if (i.dtype === "complex64" || o.dtype === "complex64") {
-      let l = xr({ inputs: { x: i }, backend: u, attrs: { dtype: "complex64" } }), c = u.data.get(l.dataId), p = c.complexTensorInfos.real, d = c.complexTensorInfos.imag, h = u.data.get(p.dataId).values, f = u.data.get(d.dataId).values, m = xr({ inputs: { x: o }, backend: u, attrs: { dtype: "complex64" } }), g = u.data.get(m.dataId), b = g.complexTensorInfos.real, y = g.complexTensorInfos.imag, v = u.data.get(b.dataId).values, x = u.data.get(y.dataId).values, [k, T, N] = n(i.shape, o.shape, h, f, v, x), E = u.makeTensorInfo(N, "float32", k), A = u.makeTensorInfo(N, "float32", T), P = Rn({ inputs: { real: E, imag: A }, backend: u });
+      let l = wr({ inputs: { x: i }, backend: u, attrs: { dtype: "complex64" } }), c = u.data.get(l.dataId), p = c.complexTensorInfos.real, d = c.complexTensorInfos.imag, h = u.data.get(p.dataId).values, f = u.data.get(d.dataId).values, m = wr({ inputs: { x: o }, backend: u, attrs: { dtype: "complex64" } }), g = u.data.get(m.dataId), b = g.complexTensorInfos.real, y = g.complexTensorInfos.imag, v = u.data.get(b.dataId).values, x = u.data.get(y.dataId).values, [k, T, N] = n(i.shape, o.shape, h, f, v, x), E = u.makeTensorInfo(N, "float32", k), A = u.makeTensorInfo(N, "float32", T), P = Rn({ inputs: { real: E, imag: A }, backend: u });
       return u.disposeIntermediateTensorInfo(l), u.disposeIntermediateTensorInfo(m), u.disposeIntermediateTensorInfo(E), u.disposeIntermediateTensorInfo(A), P;
     } else {
       let l = u.data.get(i.dataId).values, c = u.data.get(o.dataId).values, p = s || i.dtype, [d, h] = t(i.shape, o.shape, l, c, p);
@@ -20189,7 +20247,7 @@ function Gt(e, t, n, s) {
     }
   };
 }
-function Jy(e) {
+function tv(e) {
   return (t, n, s, r, a, i) => {
     let o = S.assertAndGetBroadcastShape(t, n), u = w.sizeFromShape(o), l = o.length, c = w.computeStrides(o), p = w.getTypedArrayFromDType("float32", u), d = w.getTypedArrayFromDType("float32", u), h = S.getBroadcastDims(t, o), f = S.getBroadcastDims(n, o), m = S.mergeRealAndImagArrays(s, r), g = S.mergeRealAndImagArrays(a, i), b = t.length, y = w.computeStrides(t), v = n.length, x = w.computeStrides(n);
     if (h.length + f.length === 0)
@@ -20209,11 +20267,11 @@ function Jy(e) {
     return [p, d, o];
   };
 }
-var V0 = At((e, t) => e + t);
-var CU = Jy((e, t, n, s) => ({ real: e + n, imag: t + s }));
-var Kl = Gt(Ir, V0, CU);
-var NU = { kernelName: Ir, backendName: "cpu", kernelFunc: Kl };
-function ev(e, t, n, s, r) {
+var q0 = At((e, t) => e + t);
+var OU = tv((e, t, n, s) => ({ real: e + n, imag: t + s }));
+var Kl = Gt(Sr, q0, OU);
+var PU = { kernelName: Sr, backendName: "cpu", kernelFunc: Kl };
+function nv(e, t, n, s, r) {
   let a = w.sizeFromShape(s), i = w.makeZerosTypedArray(r, n);
   for (let o = 0; o < e.length; o++) {
     let u = e[o];
@@ -20223,7 +20281,7 @@ function ev(e, t, n, s, r) {
   }
   return i;
 }
-function W0(e, t, n, s = false) {
+function j0(e, t, n, s = false) {
   let r = e.shape[0], a = e.shape[1], i = De([r, n], t.dtype);
   for (let o = 0; o < r; o++)
     for (let u = 0; u < a; u++) {
@@ -20234,7 +20292,7 @@ function W0(e, t, n, s = false) {
     }
   return i;
 }
-function Er(e) {
+function Rr(e) {
   return (t, n, s) => {
     let r = w.getTypedArrayFromDType(n, t.length);
     for (let a = 0; a < t.length; ++a)
@@ -20253,7 +20311,7 @@ function st(e, t, n) {
     return o.makeTensorInfo(i.shape, c, p);
   };
 }
-function eu(e, t, n) {
+function tu(e, t, n) {
   return ({ inputs: s, attrs: r, backend: a }) => {
     let { x: i } = s;
     if (be(i, e), i.dtype === "string" || n === "string")
@@ -20262,10 +20320,10 @@ function eu(e, t, n) {
     return o.makeTensorInfo(i.shape, l, c);
   };
 }
-var U0 = Er((e) => Math.ceil(e));
-var TU = eu(Ta, U0);
-var $U = { kernelName: Ta, backendName: "cpu", kernelFunc: TU };
-function tv(e, t, n, s) {
+var K0 = Rr((e) => Math.ceil(e));
+var zU = tu($a, K0);
+var MU = { kernelName: $a, backendName: "cpu", kernelFunc: zU };
+function sv(e, t, n, s) {
   let r = w.getArrayFromDType(n, w.sizeFromShape(t));
   if (s && n !== "string") {
     let a = 0;
@@ -20287,19 +20345,19 @@ function tv(e, t, n, s) {
   }
   return r;
 }
-var G0 = At((e, t) => e === t ? 1 : 0);
-var H0 = Gt(ho, G0, null, "bool");
-var _U = { kernelName: ho, backendName: "cpu", kernelFunc: H0 };
-var q0 = Er((e) => Math.exp(e));
-var j0 = eu(Oa, q0, "float32");
-var AU = { kernelName: Oa, backendName: "cpu", kernelFunc: j0 };
-var K0 = Er((e) => Math.expm1(e));
-var EU = eu(mo, K0);
-var RU = { kernelName: mo, backendName: "cpu", kernelFunc: EU };
-var X0 = Er((e) => Math.floor(e));
-var DU = eu(Pa, X0);
-var FU = { kernelName: Pa, backendName: "cpu", kernelFunc: DU };
-function Y0(e, t, n, s, r, a, i, o, u) {
+var X0 = At((e, t) => e === t ? 1 : 0);
+var Y0 = Gt(mo, X0, null, "bool");
+var LU = { kernelName: mo, backendName: "cpu", kernelFunc: Y0 };
+var Q0 = Rr((e) => Math.exp(e));
+var Z0 = tu(za, Q0, "float32");
+var BU = { kernelName: za, backendName: "cpu", kernelFunc: Z0 };
+var J0 = Rr((e) => Math.expm1(e));
+var VU = tu(bo, J0);
+var WU = { kernelName: bo, backendName: "cpu", kernelFunc: VU };
+var eC = Rr((e) => Math.floor(e));
+var UU = tu(Ma, eC);
+var GU = { kernelName: Ma, backendName: "cpu", kernelFunc: UU };
+function tC(e, t, n, s, r, a, i, o, u) {
   let l = De([s, a], n);
   for (let c = 0; c < s; c++) {
     let p = [], d = 0;
@@ -20314,7 +20372,7 @@ function Y0(e, t, n, s, r, a, i, o, u) {
   }
   return l;
 }
-function Q0(e, t, n) {
+function nC(e, t, n) {
   let s = De(n, e.dtype);
   for (let r = 0; r < s.size; ++r) {
     let i = s.indexToLoc(r).slice(), o = i[0], u = i[2], l = t.locToIndex([o, u]);
@@ -20324,29 +20382,29 @@ function Q0(e, t, n) {
   }
   return s;
 }
-var Z0 = At((e, t) => e > t ? 1 : 0);
-var OU = Gt(vo, Z0, null, "bool");
-var PU = { kernelName: vo, backendName: "cpu", kernelFunc: OU };
-var J0 = At((e, t) => e >= t ? 1 : 0);
-var zU = Gt(La, J0, null, "bool");
-var MU = { kernelName: La, backendName: "cpu", kernelFunc: zU };
-var eC = At((e, t) => e < t ? 1 : 0);
-var LU = Gt(xo, eC, null, "bool");
-var BU = { kernelName: xo, backendName: "cpu", kernelFunc: LU };
-var tC = At((e, t) => e <= t ? 1 : 0);
-var VU = Gt(wo, tC, null, "bool");
-var WU = { kernelName: wo, backendName: "cpu", kernelFunc: VU };
-function nC(e, t, n) {
+var sC = At((e, t) => e > t ? 1 : 0);
+var HU = Gt(wo, sC, null, "bool");
+var qU = { kernelName: wo, backendName: "cpu", kernelFunc: HU };
+var rC = At((e, t) => e >= t ? 1 : 0);
+var jU = Gt(Va, rC, null, "bool");
+var KU = { kernelName: Va, backendName: "cpu", kernelFunc: jU };
+var aC = At((e, t) => e < t ? 1 : 0);
+var XU = Gt(ko, aC, null, "bool");
+var YU = { kernelName: ko, backendName: "cpu", kernelFunc: XU };
+var iC = At((e, t) => e <= t ? 1 : 0);
+var QU = Gt(Io, iC, null, "bool");
+var ZU = { kernelName: Io, backendName: "cpu", kernelFunc: QU };
+function oC(e, t, n) {
   let s = (t - e) / (n - 1), r = w.makeZerosTypedArray(n, "float32");
   r[0] = e;
   for (let a = 1; a < r.length; a++)
     r[a] = r[a - 1] + s;
   return r;
 }
-var sC = Er((e) => Math.log(e));
-var UU = eu(Wa, sC);
-var GU = { kernelName: Wa, backendName: "cpu", kernelFunc: UU };
-function rC(e, t, n, s) {
+var uC = Rr((e) => Math.log(e));
+var JU = tu(Ga, uC);
+var eG = { kernelName: Ga, backendName: "cpu", kernelFunc: JU };
+function lC(e, t, n, s) {
   let r = w.getTypedArrayFromDType(s, w.sizeFromShape(n));
   for (let a = 0; a < r.length; ++a) {
     let i = a * t, o = e[i];
@@ -20358,31 +20416,31 @@ function rC(e, t, n, s) {
   }
   return r;
 }
-var aC = At((e, t) => Math.max(e, t));
-var HU = Gt(Ga, aC);
-var qU = { kernelName: Ga, backendName: "cpu", kernelFunc: HU };
-var iC = At((e, t) => Math.min(e, t));
-var jU = Gt(Ka, iC);
-var KU = { kernelName: Ka, backendName: "cpu", kernelFunc: jU };
-var nv = At((e, t) => e * t);
-var XU = Jy((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
-var Hp = Gt(Ya, nv, XU);
-var YU = { kernelName: Ya, backendName: "cpu", kernelFunc: Hp };
-function oC(e, t, n) {
+var cC = At((e, t) => Math.max(e, t));
+var tG = Gt(qa, cC);
+var nG = { kernelName: qa, backendName: "cpu", kernelFunc: tG };
+var dC = At((e, t) => Math.min(e, t));
+var sG = Gt(Ya, dC);
+var rG = { kernelName: Ya, backendName: "cpu", kernelFunc: sG };
+var rv = At((e, t) => e * t);
+var aG = tv((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
+var jp = Gt(Za, rv, aG);
+var iG = { kernelName: Za, backendName: "cpu", kernelFunc: jp };
+function pC(e, t, n) {
   let s = w.createScalarValue(-1, n);
-  return nv([], t, s, e, n);
+  return rv([], t, s, e, n);
 }
-function QU(e) {
+function oG(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   be(s, "neg");
-  let r = n.data.get(s.dataId).values, [a, i] = oC(r, s.shape, s.dtype);
+  let r = n.data.get(s.dataId).values, [a, i] = pC(r, s.shape, s.dtype);
   return n.makeTensorInfo(i, s.dtype, a);
 }
-var ZU = { kernelName: Io, backendName: "cpu", kernelFunc: QU };
-var uC = At((e, t) => e !== t ? 1 : 0);
-var JU = Gt(So, uC, null, "bool");
-var eG = { kernelName: So, backendName: "cpu", kernelFunc: JU };
-function sv(e, t, n, s, r) {
+var uG = { kernelName: Co, backendName: "cpu", kernelFunc: oG };
+var hC = At((e, t) => e !== t ? 1 : 0);
+var lG = Gt(No, hC, null, "bool");
+var cG = { kernelName: No, backendName: "cpu", kernelFunc: lG };
+function av(e, t, n, s, r) {
   let a = t.length, i = w.sizeFromShape(t), o = w.computeStrides(t), u = w.computeStrides(r), l = w.getTypedArrayFromDType(n, w.sizeFromShape(r));
   for (let c = 0; c < i; ++c) {
     let p = w.indexToLoc(c, a, o), d = new Array(p.length);
@@ -20399,11 +20457,11 @@ function wn(e) {
   let i = r.shape.length, o = new Array(i);
   for (let p = 0; p < o.length; p++)
     o[p] = r.shape[a[p]];
-  let u = s.data.get(r.dataId).values, l = sv(u, r.shape, r.dtype, a, o);
+  let u = s.data.get(r.dataId).values, l = av(u, r.shape, r.dtype, a, o);
   return { dataId: s.write(l, o, r.dtype), shape: o, dtype: r.dtype };
 }
-var tG = { kernelName: pi, backendName: "cpu", kernelFunc: wn };
-function lC(e, t, n, s) {
+var dG = { kernelName: mi, backendName: "cpu", kernelFunc: wn };
+function fC(e, t, n, s) {
   let [r, a] = S.computeOutAndReduceShapes(e, s), i = cn(t, "int32"), o = w.makeZerosTypedArray(w.sizeFromShape(r), i), u = w.sizeFromShape(a);
   for (let l = 0; l < o.length; ++l) {
     let c = l * u, p = 1;
@@ -20413,16 +20471,16 @@ function lC(e, t, n, s) {
   }
   return { outVals: o, outShape: r, outDtype: i };
 }
-function nG(e) {
+function pG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   be(r, "prod");
   let o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = S.getAxesPermutation(u, o), c = u, p = r, d = [];
   l != null && (p = wn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), d.push(p), c = S.getInnerMostAxes(c.length, o));
-  let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = lC(p.shape, p.dtype, h, c), b = m;
+  let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = fC(p.shape, p.dtype, h, c), b = m;
   return i && (b = S.expandShapeToKeepDim(m, u)), d.forEach((y) => n.disposeIntermediateTensorInfo(y)), n.makeTensorInfo(b, g, f);
 }
-var sG = { kernelName: Ao, backendName: "cpu", kernelFunc: nG };
-function rv(e, t, n, s) {
+var hG = { kernelName: ni, backendName: "cpu", kernelFunc: pG };
+function iv(e, t, n, s) {
   let r = e === t, a = e < t && n < 0, i = t < e && n > 1;
   if (r || a || i)
     return w.makeZerosTypedArray(0, s);
@@ -20432,13 +20490,13 @@ function rv(e, t, n, s) {
     u[l] = u[l - 1] + n;
   return u;
 }
-var cC = Er((e) => 1 / Math.sqrt(e));
-var rG = eu(si, cC);
-var aG = { kernelName: si, backendName: "cpu", kernelFunc: rG };
-var iG = Er((e) => 1 / (1 + Math.exp(-e)));
-var dC = st(ai, (e) => 1 / (1 + Math.exp(-e)));
-var oG = { kernelName: ai, backendName: "cpu", kernelFunc: dC };
-function Fd(e, t, n, s, r) {
+var mC = Rr((e) => 1 / Math.sqrt(e));
+var fG = tu(ii, mC);
+var mG = { kernelName: ii, backendName: "cpu", kernelFunc: fG };
+var gG = Rr((e) => 1 / (1 + Math.exp(-e)));
+var gC = st(ui, (e) => 1 / (1 + Math.exp(-e)));
+var bG = { kernelName: ui, backendName: "cpu", kernelFunc: gC };
+function Pd(e, t, n, s, r) {
   let a = wt.isSliceContinous(s, t, n), i = w.sizeFromShape(n), o = w.computeStrides(s);
   if (a) {
     let p = wt.computeFlatOffset(t, o);
@@ -20451,16 +20509,16 @@ function Fd(e, t, n, s, r) {
   }
   return r === "string" ? S.fromStringArrayToUint8(c.values) : c.values;
 }
-function ga(e) {
+function ba(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s;
   be(r, "slice");
   let [o, u] = wt.parseSliceParams(r, a, i);
   wt.assertParamsValid(r, o, u);
-  let l = n.data.get(r.dataId).values, c = Fd(l, o, u, r.shape, r.dtype);
+  let l = n.data.get(r.dataId).values, c = Pd(l, o, u, r.shape, r.dtype);
   return n.makeTensorInfo(u, r.dtype, c);
 }
-var uG = { kernelName: Po, backendName: "cpu", kernelFunc: ga };
-function pC(e, t, n, s, r, a, i) {
+var yG = { kernelName: zo, backendName: "cpu", kernelFunc: ba };
+function bC(e, t, n, s, r, a, i) {
   let o = t[0], u = a[0], l = new Array(u), c = new Array(o), p = t[1];
   if (u === 0) {
     if (o !== 0)
@@ -20507,7 +20565,7 @@ function pC(e, t, n, s, r, a, i) {
     return [b, [g, p], y, l, c];
   }
 }
-function hC(e, t, n, s, r) {
+function yC(e, t, n, s, r) {
   let a = w.sizeFromShape(s), i = t[0], o = r.length, u = [], l = 1, c = -1;
   for (let g = 0; g < o; ++g) {
     let b = r[g];
@@ -20553,7 +20611,7 @@ function hC(e, t, n, s, r) {
   }
   return [m, [i, o], u];
 }
-function av(e, t, n, s, r, a = false, i = 0) {
+function ov(e, t, n, s, r, a = false, i = 0) {
   let o = s.length, u = [t[0], e.length / t[0]], l = u[1], p = o > 0 ? r[o - 1] + 1 : 0;
   if (p < 0)
     throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
@@ -20593,16 +20651,16 @@ function av(e, t, n, s, r, a = false, i = 0) {
   }
   return b < p && f.fill(i, b * l, p * l), [f, d];
 }
-var lG = Er((e) => Math.sqrt(e));
-var cG = st(ii, (e) => Math.sqrt(e));
-var dG = { kernelName: ii, backendName: "cpu", kernelFunc: cG };
-var fC = At((e, t) => {
+var vG = Rr((e) => Math.sqrt(e));
+var xG = st(li, (e) => Math.sqrt(e));
+var wG = { kernelName: li, backendName: "cpu", kernelFunc: xG };
+var vC = At((e, t) => {
   let n = e - t;
   return n * n;
 });
-var pG = Gt(li, fC);
-var hG = { kernelName: li, backendName: "cpu", kernelFunc: pG };
-function mC(e, t, n, s) {
+var kG = Gt(pi, vC);
+var IG = { kernelName: pi, backendName: "cpu", kernelFunc: kG };
+function xC(e, t, n, s) {
   let r = De(e, t.dtype);
   for (let a = 0; a < r.size; a++) {
     let i = r.indexToLoc(a), o = new Array(i.length);
@@ -20612,7 +20670,7 @@ function mC(e, t, n, s) {
   }
   return r;
 }
-var fG = class {
+var SG = class {
   constructor(e, t, n, s, r, a) {
     this.separator = w.encodeString(e), this.nGramWidths = t, this.leftPad = w.encodeString(n), this.rightPad = w.encodeString(s), this.padWidth = r, this.preserveShort = a;
   }
@@ -20692,10 +20750,10 @@ var fG = class {
     return [i, a];
   }
 };
-function gC(e, t, n, s, r, a, i, o) {
-  return new fG(n, s, r, a, i, o).compute(e, t);
+function wC(e, t, n, s, r, a, i, o) {
+  return new SG(n, s, r, a, i, o).compute(e, t);
 }
-function mG(e, t, n, s) {
+function CG(e, t, n, s) {
   if (!e.length)
     return;
   if (t.length === 0) {
@@ -20719,11 +20777,11 @@ function mG(e, t, n, s) {
       (!n || i.length !== 0) && s.push(i), r = a + 1;
     }
 }
-function bC(e, t, n) {
+function kC(e, t, n) {
   let s = e.length, r = [], a = 0, i = 0, o = new Array(s);
   for (let d = 0; d < s; ++d) {
     let h = r.length;
-    mG(e[d], t, n, r);
+    CG(e[d], t, n, r);
     let f = r.length - h;
     o[d] = f, a += f, i = Math.max(i, f);
   }
@@ -20733,17 +20791,17 @@ function bC(e, t, n) {
       u[p * 2] = d, u[p * 2 + 1] = h, l[p] = r[p], ++p;
   return [u, l, c];
 }
-function yC(e, t) {
+function IC(e, t) {
   let n = w.getArrayFromDType("int32", e.length);
   for (let s = 0; s < e.length; ++s)
     n[s] = w.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();
   return n;
 }
-var vC = At((e, t) => e - t);
-var gG = Jy((e, t, n, s) => ({ real: e - n, imag: t - s }));
-var iv = Gt(ci, vC, gG);
-var bG = { kernelName: ci, backendName: "cpu", kernelFunc: iv };
-function xC(e, t) {
+var SC = At((e, t) => e - t);
+var NG = tv((e, t, n, s) => ({ real: e - n, imag: t - s }));
+var uv = Gt(hi, SC, NG);
+var TG = { kernelName: hi, backendName: "cpu", kernelFunc: uv };
+function CC(e, t) {
   let n = new Array(e.rank);
   for (let r = 0; r < n.length; r++)
     n[r] = e.shape[r] * t[r];
@@ -20757,31 +20815,31 @@ function xC(e, t) {
   }
   return s;
 }
-var Ru = (e, t) => {
+var Du = (e, t) => {
   let n = t.value - e.value;
   return n === 0 ? e.index - t.index : n;
 };
-function wC(e, t, n = 0, s = e.length - 1) {
+function NC(e, t, n = 0, s = e.length - 1) {
   for (; s > n; ) {
     if (s - n > 600) {
       let o = s - n + 1, u = t - n + 1, l = Math.log(o), c = 0.5 * Math.exp(2 * l / 3), p = 0.5 * Math.sqrt(l * c * (o - c) / o) * Math.sign(u - o / 2), d = Math.max(n, Math.floor(t - u * c / o + p)), h = Math.min(s, Math.floor(t + (o - u) * c / o + p));
-      wC(e, t, d, h);
+      NC(e, t, d, h);
     }
     let r = e[t], a = n, i = s;
-    for (w.swap(e, n, t), Ru(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
-      for (w.swap(e, a, i), a++, i--; Ru(e[a], r) < 0; )
+    for (w.swap(e, n, t), Du(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
+      for (w.swap(e, a, i), a++, i--; Du(e[a], r) < 0; )
         a = a + 1;
-      for (; Ru(e[i], r) > 0; )
+      for (; Du(e[i], r) > 0; )
         i = i - 1;
     }
-    Ru(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
+    Du(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
   }
 }
-function kC(e, t, n, s, r) {
+function TC(e, t, n, s, r) {
   let a = t[t.length - 1], [i, o] = [e.length / a, a], u = w.getTypedArrayFromDType(n, i * s), l = w.getTypedArrayFromDType("int32", i * s);
   for (let p = 0; p < i; p++) {
     let d = p * o, h = e.subarray(d, d + o), f = new Array(h.length);
-    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (wC(f, s), f = f.slice(0, s)), r && f.sort(Ru);
+    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (NC(f, s), f = f.slice(0, s)), r && f.sort(Du);
     let m = p * s, g = u.subarray(m, m + s), b = l.subarray(m, m + s);
     for (let y = 0; y < s; y++)
       g[y] = f[y].value, b[y] = f[y].index;
@@ -20789,7 +20847,7 @@ function kC(e, t, n, s, r) {
   let c = t.slice();
   return c[c.length - 1] = s, [De(c, n, u), De(c, "int32", l)];
 }
-function IC(e, t, n, s) {
+function $C(e, t, n, s) {
   let r = w.parseAxisParam(t, n)[0], a = [1, n[0], 1];
   for (let f = 0; f < r; f++)
     a[0] *= n[f];
@@ -20826,11 +20884,11 @@ function IC(e, t, n, s) {
   let h = n.slice();
   return h[r] = p[1], { outputValues: d.values, outputShape: h, indices: o };
 }
-var Ope = "0.0.0";
-pp("cpu", () => new L0(), 1);
-var SC = st(Fa, (e) => e >= 0 ? e : Math.exp(e) - 1);
-var yG = { kernelName: Fa, backendName: "cpu", kernelFunc: SC };
-function CC(e) {
+var Xpe = "0.0.0";
+fp("cpu", () => new G0(), 1);
+var _C = st(Pa, (e) => e >= 0 ? e : Math.exp(e) - 1);
+var $G = { kernelName: Pa, backendName: "cpu", kernelFunc: _C };
+function AC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s;
   be([r], "leakyRelu");
   let i = w.sizeFromShape(r.shape), o = n.data.get(r.dataId).values, u = w.getTypedArrayFromDType("float32", i);
@@ -20838,34 +20896,34 @@ function CC(e) {
     u[l] = o[l] < 0 ? a * o[l] : o[l];
   return n.makeTensorInfo(r.shape, "float32", u);
 }
-var vG = { kernelName: Va, backendName: "cpu", kernelFunc: CC };
-var xG = At((e, t) => e < 0 ? t * e : e);
-function NC(e) {
+var _G = { kernelName: Ua, backendName: "cpu", kernelFunc: AC };
+var AG = At((e, t) => e < 0 ? t * e : e);
+function EC(e) {
   let { inputs: t, backend: n } = e, { x: s, alpha: r } = t;
   be([s, r], "prelu");
-  let a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, [o, u] = xG(s.shape, r.shape, a, i, "float32");
+  let a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, [o, u] = AG(s.shape, r.shape, a, i, "float32");
   return n.makeTensorInfo(u, "float32", o);
 }
-var wG = { kernelName: Ja, backendName: "cpu", kernelFunc: NC };
-var TC = st(ei, (e) => Math.max(0, e));
-var kG = { kernelName: ei, backendName: "cpu", kernelFunc: TC };
-var $C = st(ni, (e) => Math.min(Math.max(0, e), 6));
-var IG = { kernelName: ni, backendName: "cpu", kernelFunc: $C };
-function ov(e, t, n, s, r) {
+var EG = { kernelName: ti, backendName: "cpu", kernelFunc: EC };
+var RC = st(si, (e) => Math.max(0, e));
+var RG = { kernelName: si, backendName: "cpu", kernelFunc: RC };
+var DC = st(ai, (e) => Math.min(Math.max(0, e), 6));
+var DG = { kernelName: ai, backendName: "cpu", kernelFunc: DC };
+function lv(e, t, n, s, r) {
   if (n === "linear")
     return Os({ inputs: { x: t }, backend: e });
   if (n === "relu")
-    return TC({ inputs: { x: t }, backend: e });
+    return RC({ inputs: { x: t }, backend: e });
   if (n === "elu")
-    return SC({ inputs: { x: t }, backend: e });
+    return _C({ inputs: { x: t }, backend: e });
   if (n === "relu6")
-    return $C({ inputs: { x: t }, backend: e });
+    return DC({ inputs: { x: t }, backend: e });
   if (n === "prelu")
-    return NC({ inputs: { x: t, alpha: s }, backend: e });
+    return EC({ inputs: { x: t, alpha: s }, backend: e });
   if (n === "leakyrelu")
-    return CC({ inputs: { x: t }, backend: e, attrs: { alpha: r } });
+    return AC({ inputs: { x: t }, backend: e, attrs: { alpha: r } });
   if (n === "sigmoid")
-    return dC({ inputs: { x: t }, backend: e });
+    return gC({ inputs: { x: t }, backend: e });
   throw new Error(`Activation ${n} has not been implemented for the CPU backend.`);
 }
 function mt(e) {
@@ -20878,11 +20936,11 @@ function mt(e) {
   }
   return { dataId: r.dataId, shape: o, dtype: r.dtype };
 }
-var SG = { kernelName: Eo, backendName: "cpu", kernelFunc: mt };
-function _C(e) {
+var FG = { kernelName: Ro, backendName: "cpu", kernelFunc: mt };
+function FC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
   be([r, a], "matMul");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Ko.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
   w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
   let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], T = mt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), N = mt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = i ? T.shape[1] : T.shape[2], A = i ? T.shape[2] : T.shape[1], P = o ? N.shape[1] : N.shape[2], R = Math.max(g, b), F = n.data.get(T.dataId).values, $ = n.data.get(N.dataId).values, z = w.computeStrides(T.shape), W = w.computeStrides(N.shape), [q, K, Y] = i ? [z[0], 1, z[1]] : [z[0], z[1], 1], [Z, te, ee] = o ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = A * P, ne = De([R, A, P], T.dtype), oe = ne.values, re = n.blockSize;
   for (let le = 0; le < R; le++)
@@ -20902,20 +20960,20 @@ function _C(e) {
         }
   return n.disposeIntermediateTensorInfo(T), n.disposeIntermediateTensorInfo(N), n.makeTensorInfo(v, ne.dtype, ne.values);
 }
-var CG = { kernelName: Ca, backendName: "cpu", kernelFunc: _C };
-function NG(e) {
+var OG = { kernelName: Na, backendName: "cpu", kernelFunc: FC };
+function PG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d, h, f, m = [];
-  d = _C({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = Kl({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = ov(n, d, c, o, p), m.push(d), d = f);
+  d = FC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = Kl({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = lv(n, d, c, o, p), m.push(d), d = f);
   for (let b of m)
     n.disposeIntermediateTensorInfo(b);
   return d;
 }
-var TG = { kernelName: ra, backendName: "cpu", kernelFunc: NG };
-var $G = st(sl, (e) => Math.acos(e));
-var _G = { kernelName: sl, backendName: "cpu", kernelFunc: $G };
-var AG = st(rl, (e) => Math.acosh(e));
-var EG = { kernelName: rl, backendName: "cpu", kernelFunc: AG };
-function RG(e) {
+var zG = { kernelName: aa, backendName: "cpu", kernelFunc: PG };
+var MG = st(rl, (e) => Math.acos(e));
+var LG = { kernelName: rl, backendName: "cpu", kernelFunc: MG };
+var BG = st(al, (e) => Math.acosh(e));
+var VG = { kernelName: al, backendName: "cpu", kernelFunc: BG };
+function WG(e) {
   let { inputs: t, backend: n } = e, s = t;
   be(t, "addN");
   let r = s.map((o) => n.data.get(o.dataId).values), a = De(s[0].shape, s[0].dtype), i = a.values;
@@ -20926,8 +20984,8 @@ function RG(e) {
   }
   return n.makeTensorInfo(a.shape, a.dtype, a.values);
 }
-var DG = { kernelName: ka, backendName: "cpu", kernelFunc: RG };
-function FG(e) {
+var UG = { kernelName: Ia, backendName: "cpu", kernelFunc: WG };
+function GG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   be(r, "all");
   let o = w.parseAxisParam(a, r.shape), u = o, l = S.getAxesPermutation(u, r.shape.length), c = r;
@@ -20949,8 +21007,8 @@ function FG(e) {
   }
   return g;
 }
-var OG = { kernelName: al, backendName: "cpu", kernelFunc: FG };
-function PG(e) {
+var HG = { kernelName: il, backendName: "cpu", kernelFunc: GG };
+function qG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   be(r, "any");
   let o = w.parseAxisParam(a, r.shape), u = o, l = S.getAxesPermutation(u, r.shape.length), c = r;
@@ -20972,8 +21030,8 @@ function PG(e) {
   }
   return g;
 }
-var zG = { kernelName: il, backendName: "cpu", kernelFunc: PG };
-function MG(e) {
+var jG = { kernelName: ol, backendName: "cpu", kernelFunc: qG };
+function KG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
   be(r, "argMax");
   let i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
@@ -20989,8 +21047,8 @@ function MG(e) {
   }
   return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
 }
-var LG = { kernelName: Ia, backendName: "cpu", kernelFunc: MG };
-function BG(e) {
+var XG = { kernelName: Sa, backendName: "cpu", kernelFunc: KG };
+function YG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
   be(r, "argMin");
   let i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
@@ -21006,19 +21064,19 @@ function BG(e) {
   }
   return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
 }
-var VG = { kernelName: ol, backendName: "cpu", kernelFunc: BG };
-var WG = st(ul, (e) => Math.asin(e));
-var UG = { kernelName: ul, backendName: "cpu", kernelFunc: WG };
-var GG = st(ll, (e) => Math.asinh(e));
-var HG = { kernelName: ll, backendName: "cpu", kernelFunc: GG };
-var qG = st(cl, (e) => Math.atan(e));
-var jG = { kernelName: cl, backendName: "cpu", kernelFunc: qG };
-var KG = At((e, t) => Math.atan2(e, t));
-var XG = Gt(pl, KG);
-var YG = { kernelName: pl, backendName: "cpu", kernelFunc: XG };
-var QG = st(dl, (e) => Math.atanh(e));
-var ZG = { kernelName: dl, backendName: "cpu", kernelFunc: QG };
-function uv(e, t, n, s, r, a) {
+var QG = { kernelName: ul, backendName: "cpu", kernelFunc: YG };
+var ZG = st(ll, (e) => Math.asin(e));
+var JG = { kernelName: ll, backendName: "cpu", kernelFunc: ZG };
+var eH = st(cl, (e) => Math.asinh(e));
+var tH = { kernelName: cl, backendName: "cpu", kernelFunc: eH };
+var nH = st(dl, (e) => Math.atan(e));
+var sH = { kernelName: dl, backendName: "cpu", kernelFunc: nH };
+var rH = At((e, t) => Math.atan2(e, t));
+var aH = Gt(hl, rH);
+var iH = { kernelName: hl, backendName: "cpu", kernelFunc: aH };
+var oH = st(pl, (e) => Math.atanh(e));
+var uH = { kernelName: pl, backendName: "cpu", kernelFunc: oH };
+function cv(e, t, n, s, r, a) {
   let i = r.strideHeight, o = r.strideWidth, u = r.dilationHeight, l = r.dilationWidth, c = r.effectiveFilterHeight, p = r.effectiveFilterWidth, d = r.padInfo.top, h = r.padInfo.left, f = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, m = De(r.outShape, n), g = m.values, b = r.outShape[1] * r.outShape[2] * r.outShape[3], y = r.outShape[2] * r.outShape[3], v = r.outShape[3];
   for (let x = 0; x < r.batchSize; ++x) {
     let k = x * b, T = x * s[0];
@@ -21043,7 +21101,7 @@ function uv(e, t, n, s, r, a) {
   }
   return m;
 }
-function AC(e, t, n, s, r = false, a = false) {
+function OC(e, t, n, s, r = false, a = false) {
   let i = De(s.outShape, "int32"), o = s.strideHeight, u = s.strideWidth, l = s.dilationHeight, c = s.dilationWidth, p = s.effectiveFilterHeight, d = s.effectiveFilterWidth, h = s.padInfo.top, f = s.padInfo.left, m = De(t, n, e);
   for (let g = 0; g < s.batchSize; ++g)
     for (let b = 0; b < s.inChannels; ++b)
@@ -21069,7 +21127,7 @@ function AC(e, t, n, s, r = false, a = false) {
       }
   return i;
 }
-function EC(e, t, n, s, r, a) {
+function PC(e, t, n, s, r, a) {
   let i = r.strideDepth, o = r.strideHeight, u = r.strideWidth, l = r.dilationDepth, c = r.dilationHeight, p = r.dilationWidth, d = r.effectiveFilterDepth, h = r.effectiveFilterHeight, f = r.effectiveFilterWidth, m = r.padInfo.front, g = r.padInfo.top, b = r.padInfo.left, y = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, v = De(r.outShape, n), x = v.values, k = r.outShape[1] * r.outShape[2] * r.outShape[3] * r.outShape[4], T = r.outShape[2] * r.outShape[3] * r.outShape[4], N = r.outShape[3] * r.outShape[4], E = r.outShape[4];
   for (let A = 0; A < r.batchSize; ++A) {
     let P = A * k, R = A * s[0];
@@ -21112,7 +21170,7 @@ function EC(e, t, n, s, r, a) {
   }
   return v;
 }
-function JG(e, t) {
+function lH(e, t) {
   let n = De(t.outShape, "int32"), s = t.strideDepth, r = t.strideHeight, a = t.strideWidth, i = t.dilationDepth, o = t.dilationHeight, u = t.dilationWidth, l = t.effectiveFilterDepth, c = t.effectiveFilterHeight, p = t.effectiveFilterWidth, d = t.padInfo.front, h = t.padInfo.top, f = t.padInfo.left;
   for (let m = 0; m < t.batchSize; ++m)
     for (let g = 0; g < t.inChannels; ++g)
@@ -21147,7 +21205,7 @@ function JG(e, t) {
       }
   return n;
 }
-function eH(e) {
+function cH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
   be(r, "avgPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
@@ -21156,20 +21214,20 @@ function eH(e) {
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     p = Os({ inputs: { x: r }, backend: n });
   else {
-    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = uv(d, r.shape, r.dtype, h, c, "avg");
+    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = cv(d, r.shape, r.dtype, h, c, "avg");
     p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
   }
   return p;
 }
-var tH = { kernelName: Sa, backendName: "cpu", kernelFunc: eH };
-function nH(e) {
+var dH = { kernelName: Ca, backendName: "cpu", kernelFunc: cH };
+function pH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
   be(r, "avgPool3d");
-  let c = S.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = EC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
+  let c = S.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = PC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
   return n.makeTensorInfo(d.shape, "float32", d.values);
 }
-var sH = { kernelName: qd, backendName: "cpu", kernelFunc: nH };
-function rH(e) {
+var hH = { kernelName: Kd, backendName: "cpu", kernelFunc: pH };
+function fH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
   be([r, a], "avgPool3DGrad");
   let c = S.computePool3DInfo(a.shape, i, o, 1, u, l), p = c.strideDepth, d = c.strideHeight, h = c.strideWidth, f = c.filterDepth, m = c.filterHeight, g = c.filterWidth, b = c.dilationDepth, y = c.dilationHeight, v = c.dilationWidth, x = c.effectiveFilterDepth, k = c.effectiveFilterHeight, T = c.effectiveFilterWidth, N = x - 1 - c.padInfo.front, E = T - 1 - c.padInfo.left, A = k - 1 - c.padInfo.top, P = De(a.shape, "float32"), R = 1 / (f * m * g), F = n.bufferSync(r);
@@ -21197,8 +21255,8 @@ function rH(e) {
           }
   return n.makeTensorInfo(P.shape, P.dtype, P.values);
 }
-var aH = { kernelName: og, backendName: "cpu", kernelFunc: rH };
-function iH(e) {
+var mH = { kernelName: lg, backendName: "cpu", kernelFunc: fH };
+function gH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
   be([r, a], "avgPoolGrad");
   let { filterSize: o, strides: u, pad: l } = s, c = S.computePool2DInfo(i.shape, o, u, 1, l), p = c.strideHeight, d = c.strideWidth, h = c.filterHeight, f = c.filterWidth, m = c.dilationHeight, g = c.dilationWidth, b = c.effectiveFilterHeight, y = c.effectiveFilterWidth, v = y - 1 - c.padInfo.left, x = b - 1 - c.padInfo.top, k = De(i.shape, "float32"), T = 1 / (h * f), N = n.data.get(r.dataId).values, E = De(r.shape, "float32", N);
@@ -21221,8 +21279,8 @@ function iH(e) {
         }
   return n.makeTensorInfo(k.shape, k.dtype, k.values);
 }
-var oH = { kernelName: ig, backendName: "cpu", kernelFunc: iH };
-function uH(e) {
+var bH = { kernelName: ug, backendName: "cpu", kernelFunc: gH };
+function yH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, scale: a, offset: i, mean: o, variance: u } = t;
   w.assert(o.shape.length === u.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(i == null || o.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(a == null || o.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), be([r, o, u, a, i], "batchNorm");
   let { varianceEpsilon: l } = s;
@@ -21232,30 +21290,30 @@ function uH(e) {
     m[E] = f[x++] + (c[E] - p[k++]) * h[T++] / Math.sqrt(d[N++] + l), x >= g && (x = 0), k >= v && (k = 0), T >= b && (T = 0), N >= y && (N = 0);
   return n.makeTensorInfo(r.shape, r.dtype, m);
 }
-var lH = { kernelName: Ma, backendName: "cpu", kernelFunc: uH };
-function cH(e) {
+var vH = { kernelName: Ba, backendName: "cpu", kernelFunc: yH };
+function xH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
   be([r], "batchToSpaceND");
-  let o = a.reduce((b, y) => b * y), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = mt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ga({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
+  let o = a.reduce((b, y) => b * y), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = mt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ba({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
 }
-var dH = { kernelName: oo, backendName: "cpu", kernelFunc: cH };
-function pH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = ev(o, u, a.dtype, a.shape, i);
+var wH = { kernelName: lo, backendName: "cpu", kernelFunc: xH };
+function kH(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = nv(o, u, a.dtype, a.shape, i);
   return n.makeTensorInfo([i], a.dtype, l);
 }
-var hH = { kernelName: ug, backendName: "cpu", kernelFunc: pH };
-function fH(e) {
+var IH = { kernelName: cg, backendName: "cpu", kernelFunc: kH };
+function SH(e) {
   let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = S.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
   return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
 }
-var mH = { kernelName: lg, backendName: "cpu", kernelFunc: fH };
-var gH = st(Sr, (e, t) => {
+var CH = { kernelName: dg, backendName: "cpu", kernelFunc: SH };
+var NH = st(Cr, (e, t) => {
   let n = t;
   return e > n.clipValueMax ? n.clipValueMax : e < n.clipValueMin ? n.clipValueMin : e;
 });
-var bH = { kernelName: Sr, backendName: "cpu", kernelFunc: gH };
-var yH = (e) => {
+var TH = { kernelName: Cr, backendName: "cpu", kernelFunc: NH };
+var $H = (e) => {
   let { x: t } = e.inputs, n = e.backend, s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId), a = r.complexTensorInfos.real, i = r.complexTensorInfos.imag, o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values;
   for (let l = 0; l < o.length; l++) {
     let c = o[l], p = u[l];
@@ -21263,13 +21321,13 @@ var yH = (e) => {
   }
   return n.makeOutput(s, t.shape, "float32");
 };
-var vH = { kernelName: Kd, backendName: "cpu", kernelFunc: yH };
-function no(e) {
+var _H = { kernelName: Yd, backendName: "cpu", kernelFunc: $H };
+function ro(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.imag, a = n.data.get(r.dataId).values;
   return n.makeTensorInfo(r.shape, r.dtype, a);
 }
-var xH = { kernelName: Zd, backendName: "cpu", kernelFunc: no };
-function so(e) {
+var AH = { kernelName: ep, backendName: "cpu", kernelFunc: ro };
+function ao(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = S.computeOutShape(t.map((m) => m.shape), a);
   if (w.sizeFromShape(i) === 0)
     return n.makeTensorInfo(i, t[0].dtype, []);
@@ -21278,7 +21336,7 @@ function so(e) {
     return Os({ inputs: { x: o[0] }, backend: n });
   let u = o.map((m) => m.shape);
   if (S.assertParamsConsistent(u, a), o[0].dtype === "complex64") {
-    let m = o.map((x) => ma({ inputs: { input: x }, backend: n })), g = o.map((x) => no({ inputs: { input: x }, backend: n })), b = so({ inputs: m, backend: n, attrs: { axis: a } }), y = so({ inputs: g, backend: n, attrs: { axis: a } }), v = Rn({ inputs: { real: b, imag: y }, backend: n });
+    let m = o.map((x) => ga({ inputs: { input: x }, backend: n })), g = o.map((x) => ro({ inputs: { input: x }, backend: n })), b = ao({ inputs: m, backend: n, attrs: { axis: a } }), y = ao({ inputs: g, backend: n, attrs: { axis: a } }), v = Rn({ inputs: { real: b, imag: y }, backend: n });
     return m.forEach((x) => n.disposeIntermediateTensorInfo(x)), g.forEach((x) => n.disposeIntermediateTensorInfo(x)), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), v;
   }
   let l = o.map((m) => {
@@ -21286,11 +21344,11 @@ function so(e) {
     return mt({ inputs: { x: m }, backend: n, attrs: { shape: [-1, g] } });
   }), c = l.map((m) => ({ vals: n.data.get(m.dataId).values, shape: m.shape }));
   i = S.computeOutShape(l.map((m) => m.shape), 1);
-  let p = l[0].shape[0] === 1, d = tv(c, i, t[0].dtype, p), h = S.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
+  let p = l[0].shape[0] === 1, d = sv(c, i, t[0].dtype, p), h = S.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
   return l.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var wH = { kernelName: uo, backendName: "cpu", kernelFunc: so };
-function RC(e) {
+var EH = { kernelName: co, backendName: "cpu", kernelFunc: ao };
+function zC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s;
   be([r, a], "conv2d");
   let p = S.convertConv2DDataFormat(u), d = S.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p), h = d.filterHeight, f = d.filterWidth, m = d.dilationHeight, g = d.dilationWidth, b = d.padInfo.left, y = d.padInfo.top, v = d.dataFormat === "channelsLast", x = new Vt(d.outShape, r.dtype), k = w.computeStrides(r.shape), T = w.computeStrides(a.shape), N = k[0], E = v ? k[1] : k[2], A = v ? k[2] : 1, P = v ? 1 : k[1], R = x.strides[0], F = v ? x.strides[1] : x.strides[2], $ = v ? x.strides[2] : 1, z = v ? 1 : x.strides[1], W = n.data.get(r.dataId).values, q = n.data.get(a.dataId).values, K = x.values;
@@ -21323,8 +21381,8 @@ function RC(e) {
   }
   return n.makeTensorInfo(x.shape, x.dtype, K);
 }
-var kH = { kernelName: $a, backendName: "cpu", kernelFunc: RC };
-function IH(e) {
+var RH = { kernelName: _a, backendName: "cpu", kernelFunc: zC };
+function DH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s;
   be([r, a], "conv2dBackpropFilter");
   let p = S.convertConv2DDataFormat(u), d = S.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), { strideHeight: h, strideWidth: f, filterHeight: m, filterWidth: g } = d, b = d.dataFormat === "channelsLast", y = new Vt(d.filterShape, "float32"), v = d.padInfo.left, x = d.padInfo.top, k = n.data.get(r.dataId).values, T = n.data.get(a.dataId).values, N = new Vt(r.shape, r.dtype, k), E = new Vt(a.shape, a.dtype, T);
@@ -21349,8 +21407,8 @@ function IH(e) {
   }
   return n.makeTensorInfo(y.shape, y.dtype, y.values);
 }
-var SH = { kernelName: cg, backendName: "cpu", kernelFunc: IH };
-function CH(e) {
+var FH = { kernelName: pg, backendName: "cpu", kernelFunc: DH };
+function OH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s;
   be([r, a], "conv2dBackpropInput");
   let p = w.computeStrides(a.shape), d = w.computeStrides(r.shape), h = S.convertConv2DDataFormat(l), f = S.computeConv2DInfo(i, a.shape, o, 1, u, c, false, h), m = new Vt(f.inShape, "float32"), g = m.values, b = n.data.get(r.dataId).values, y = n.data.get(a.dataId).values, [v, x, k] = p, { batchSize: T, filterHeight: N, filterWidth: E, inChannels: A, inHeight: P, inWidth: R, outChannels: F, outHeight: $, outWidth: z, strideHeight: W, strideWidth: q } = f;
@@ -21378,8 +21436,8 @@ function CH(e) {
       }
   return n.makeTensorInfo(m.shape, m.dtype, m.values);
 }
-var NH = { kernelName: _a, backendName: "cpu", kernelFunc: CH };
-function TH(e) {
+var PH = { kernelName: Aa, backendName: "cpu", kernelFunc: OH };
+function zH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s;
   be([r, a], "conv3d");
   let l = S.computeConv3DInfo(r.shape, a.shape, i, u, o), { filterDepth: c, filterHeight: p, filterWidth: d, dilationDepth: h, dilationHeight: f, dilationWidth: m, padInfo: g } = l, b = g.front, y = g.left, v = g.top, x = new Vt(l.outShape, r.dtype), k = n.data.get(r.dataId).values, T = n.data.get(a.dataId).values, N = x.values, E = w.computeStrides(r.shape), A = w.computeStrides(a.shape);
@@ -21421,8 +21479,8 @@ function TH(e) {
   }
   return n.makeTensorInfo(x.shape, x.dtype, x.values);
 }
-var $H = { kernelName: Xd, backendName: "cpu", kernelFunc: TH };
-function _H(e) {
+var MH = { kernelName: Qd, backendName: "cpu", kernelFunc: zH };
+function LH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s;
   be([r, a], "conv3dBackpropFilterV2");
   let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = S.computeConv3DInfo(r.shape, u, i, 1, o), d = p.strideDepth, h = p.strideHeight, f = p.strideWidth, m = p.filterDepth, g = p.filterHeight, b = p.filterWidth, y = new Vt(p.filterShape, "float32"), v = y.values, [x, k, T, N] = y.strides, E = n.data.get(a.dataId).values, [A, P, R, F] = c, $ = n.data.get(r.dataId).values, [z, W, q, K] = l, Y = p.padInfo.front, Z = p.padInfo.left, te = p.padInfo.top;
@@ -21443,8 +21501,8 @@ function _H(e) {
                 for (let Nn = le; Nn < me; ++Nn) {
                   let Yt = (re + Nn * h - te) * q + Et, Dn = Nn * R + en;
                   for (let tn = Ee; tn < Pe; ++tn) {
-                    let Ls = (Se + tn * f - Z) * K + Yt, ki = tn * F + Dn;
-                    Ce += $[Ls + Je] * E[ki + tt];
+                    let Ls = (Se + tn * f - Z) * K + Yt, Si = tn * F + Dn;
+                    Ce += $[Ls + Je] * E[Si + tt];
                   }
                 }
               }
@@ -21457,8 +21515,8 @@ function _H(e) {
   }
   return n.makeTensorInfo(y.shape, y.dtype, y.values);
 }
-var AH = { kernelName: dg, backendName: "cpu", kernelFunc: _H };
-function EH(e) {
+var BH = { kernelName: hg, backendName: "cpu", kernelFunc: LH };
+function VH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s;
   be([r], "conv3dBackpropInputV2");
   let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = S.computeConv3DInfo(u, a.shape, o, 1, i), d = new Vt(p.inShape, "float32"), h = d.values, [f, m, g, b] = d.strides, y = n.data.get(r.dataId).values, [v, x, k, T] = l, N = n.data.get(a.dataId).values, [E, A, P, R] = c, { batchSize: F, filterDepth: $, filterHeight: z, filterWidth: W, inChannels: q, inDepth: K, inHeight: Y, inWidth: Z, outChannels: te, outDepth: ee, outHeight: se, outWidth: ne, strideDepth: oe, strideHeight: re, strideWidth: le } = p, me = $ - 1 - p.padInfo.front, ke = z - 1 - p.padInfo.top, Se = W - 1 - p.padInfo.left;
@@ -21475,10 +21533,10 @@ function EH(e) {
               for (let Dn = at; Dn < Jt; ++Dn) {
                 let tn = Dn * re - ut;
                 for (let Ms = Et; Ms < en; ++Ms) {
-                  let Ls = Ms * le - Cn, ki = v * Ee + x * Tn + k * Dn + T * Ms, Js = E * ($ - 1 - Yt) + A * (z - 1 - tn) + P * (W - 1 - Ls) + R * Pe;
+                  let Ls = Ms * le - Cn, Si = v * Ee + x * Tn + k * Dn + T * Ms, er = E * ($ - 1 - Yt) + A * (z - 1 - tn) + P * (W - 1 - Ls) + R * Pe;
                   for (let Bs = 0; Bs < te; ++Bs) {
-                    let pu = y[ki + Bs], Ii = N[Js + Bs];
-                    Nn += pu * Ii;
+                    let hu = y[Si + Bs], Ci = N[er + Bs];
+                    Nn += hu * Ci;
                   }
                 }
               }
@@ -21489,12 +21547,12 @@ function EH(e) {
       }
   return n.makeTensorInfo(d.shape, d.dtype, d.values);
 }
-var RH = { kernelName: pg, backendName: "cpu", kernelFunc: EH };
-var DH = st(Aa, (e) => Math.cos(e));
-var FH = { kernelName: Aa, backendName: "cpu", kernelFunc: DH };
-var OH = st(Ea, (e) => Math.cosh(e));
-var PH = { kernelName: Ea, backendName: "cpu", kernelFunc: OH };
-function zH(e) {
+var WH = { kernelName: fg, backendName: "cpu", kernelFunc: VH };
+var UH = st(Ea, (e) => Math.cos(e));
+var GH = { kernelName: Ea, backendName: "cpu", kernelFunc: UH };
+var HH = st(Ra, (e) => Math.cosh(e));
+var qH = { kernelName: Ra, backendName: "cpu", kernelFunc: HH };
+function jH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, [c, p, d, h] = r.shape, f = a.shape[0], [m, g] = o, b = De([f, m, g, h], "float32"), y = n.data.get(a.dataId).values, v = n.data.get(i.dataId).values, x = n.data.get(r.dataId).values, k = w.computeStrides(r.shape), T = w.computeStrides(b.shape);
   for (let N = 0; N < f; N++) {
     let E = N * 4, A = y[E], P = y[E + 1], R = y[E + 2], F = y[E + 3], $ = v[N];
@@ -21554,8 +21612,8 @@ function zH(e) {
   }
   return n.makeTensorInfo(b.shape, b.dtype, b.values);
 }
-var MH = { kernelName: co, backendName: "cpu", kernelFunc: zH };
-function LH(e) {
+var KH = { kernelName: ho, backendName: "cpu", kernelFunc: jH };
+function XH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
   be(r, "cumprod");
   let u = S.getAxesPermutation([a], r.shape.length), l = r;
@@ -21581,8 +21639,8 @@ function LH(e) {
   }
   return g;
 }
-var BH = { kernelName: hl, backendName: "cpu", kernelFunc: LH };
-function VH(e) {
+var YH = { kernelName: po, backendName: "cpu", kernelFunc: XH };
+function QH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
   be(r, "cumsum");
   let u = S.getAxesPermutation([a], r.shape.length), l = r;
@@ -21608,20 +21666,20 @@ function VH(e) {
   }
   return g;
 }
-var WH = { kernelName: lo, backendName: "cpu", kernelFunc: VH };
-function UH(e) {
+var ZH = { kernelName: Da, backendName: "cpu", kernelFunc: QH };
+function JH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
   if (r.shape.length === 1) {
-    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = ev(u, l, a.dtype, a.shape, i);
+    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = nv(u, l, a.dtype, a.shape, i);
     return n.makeTensorInfo([i], a.dtype, c);
   } else if (r.shape.length === 2) {
-    let u = n.bufferSync(r), l = n.bufferSync(a), c = W0(u, l, i, o);
+    let u = n.bufferSync(r), l = n.bufferSync(a), c = j0(u, l, i, o);
     return n.makeTensorInfo(c.shape, a.dtype, c.values);
   }
   throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
 }
-var GH = { kernelName: hg, backendName: "cpu", kernelFunc: UH };
-function HH(e) {
+var e6 = { kernelName: mg, backendName: "cpu", kernelFunc: JH };
+function t6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s;
   w.assert(i === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);
   let o = r.shape[0], u = r.shape[1], l = r.shape[2], c = r.shape[3], p = u * a, d = l * a, h = c / (a * a), f = n.data.get(r.dataId).values, m = new Float32Array(o * p * d * h), g = 0;
@@ -21638,8 +21696,8 @@ function HH(e) {
     }
   return n.makeTensorInfo([o, p, d, h], r.dtype, m);
 }
-var qH = { kernelName: po, backendName: "cpu", kernelFunc: HH };
-function DC(e) {
+var n6 = { kernelName: fo, backendName: "cpu", kernelFunc: t6 };
+function MC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s;
   be([r, a], "depthwiseConv2DNative");
   let c = w.computeStrides(r.shape), p = w.computeStrides(a.shape), d = u;
@@ -21674,8 +21732,8 @@ function DC(e) {
   }
   return n.makeTensorInfo(T.shape, T.dtype, T.values);
 }
-var jH = { kernelName: Ra, backendName: "cpu", kernelFunc: DC };
-function KH(e) {
+var s6 = { kernelName: Fa, backendName: "cpu", kernelFunc: MC };
+function r6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s;
   be([r, a], "depthwiseConv2dNativeBackpropFilter");
   let p = S.computeConv2DInfo(r.shape, c, i, o, u, l, true), { strideHeight: d, strideWidth: h, filterHeight: f, filterWidth: m } = p, g = new Vt(p.filterShape, "float32"), b = p.padInfo.left, y = p.padInfo.top, v = p.outChannels / p.inChannels, x = n.data.get(r.dataId).values, k = new Vt(r.shape, r.dtype, x), T = n.data.get(a.dataId).values, N = new Vt(a.shape, a.dtype, T);
@@ -21699,8 +21757,8 @@ function KH(e) {
   }
   return n.makeTensorInfo(g.shape, g.dtype, g.values);
 }
-var XH = { kernelName: fg, backendName: "cpu", kernelFunc: KH };
-function YH(e) {
+var a6 = { kernelName: gg, backendName: "cpu", kernelFunc: r6 };
+function i6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s;
   be([r, a], "depthwiseConv2DNativeBackpropInput");
   let p = w.computeStrides(r.shape), d = w.computeStrides(a.shape), h = S.computeConv2DInfo(c, a.shape, i, o, u, l, true), f = new Vt(h.inShape, "float32"), m = f.values, [g, b, y] = f.strides, v = n.data.get(r.dataId).values, [x, k, T] = p, N = n.data.get(a.dataId).values, [E, A, P] = d, { batchSize: R, filterHeight: F, filterWidth: $, inChannels: z, inHeight: W, inWidth: q, outChannels: K, outHeight: Y, outWidth: Z, strideHeight: te, strideWidth: ee } = h, se = F - 1 - h.padInfo.top, ne = $ - 1 - h.padInfo.left, oe = K / z;
@@ -21725,16 +21783,16 @@ function YH(e) {
       }
   return n.makeTensorInfo(f.shape, f.dtype, f.values);
 }
-var QH = { kernelName: mg, backendName: "cpu", kernelFunc: YH };
-function ZH(e) {
+var o6 = { kernelName: bg, backendName: "cpu", kernelFunc: i6 };
+function u6(e) {
   let { inputs: t, backend: n } = e, { x: s } = t, r = w.sizeFromShape(s.shape), a = n.data.get(s.dataId).values, i = De([r, r], s.dtype), o = i.values;
   for (let l = 0; l < a.length; l++)
     o[l * r + l] = a[l];
   let u = [...s.shape, ...s.shape];
   return n.makeTensorInfo(u, i.dtype, i.values);
 }
-var JH = { kernelName: gg, backendName: "cpu", kernelFunc: ZH };
-var e6 = { kernelName: Yd, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+var l6 = { kernelName: yg, backendName: "cpu", kernelFunc: u6 };
+var c6 = { kernelName: Zd, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s, filter: r } = e, { strides: a, pad: i, dilations: o } = n, u = t, l = u.data.get(s.dataId).values, c = s.shape.length, p = u.data.get(r.dataId).values, d = r.shape.length, { batchSize: h, inHeight: f, inWidth: m, inChannels: g, outHeight: b, outWidth: y, padInfo: v, strideHeight: x, strideWidth: k, filterHeight: T, filterWidth: N, dilationHeight: E, dilationWidth: A, outShape: P } = S.computeDilation2DInfo(s.shape, r.shape, a, i, "NHWC", o), R = w.sizeFromShape(P), F = P.length, $ = w.getArrayFromDType(s.dtype, R);
   for (let W = 0; W < h; ++W)
     for (let q = 0; q < b; ++q) {
@@ -21761,9 +21819,9 @@ var e6 = { kernelName: Yd, backendName: "cpu", kernelFunc: ({ inputs: e, backend
     }
   return { dataId: u.write(w.toTypedArray($, s.dtype), P, s.dtype), shape: P, dtype: s.dtype };
 } };
-var t6 = { kernelName: Qf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+var d6 = { kernelName: Zf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: T, dilationHeight: N, dilationWidth: E, outShape: A } = S.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
-  w.assert(a.rank === A.length, () => `Error in ${Qf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
+  w.assert(a.rank === A.length, () => `Error in ${Zf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
   let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(r.shape, r.dtype);
   for (let $ = 0; $ < d; ++$)
     for (let z = 0; z < g; ++z) {
@@ -21789,9 +21847,9 @@ var t6 = { kernelName: Qf, backendName: "cpu", kernelFunc: ({ inputs: e, backend
     }
   return { dataId: l.write(w.toTypedArray(R, s.dtype), r.shape, r.dtype), shape: r.shape, dtype: r.dtype };
 } };
-var n6 = { kernelName: Yf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+var p6 = { kernelName: Qf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: T, dilationHeight: N, dilationWidth: E, outShape: A } = S.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
-  w.assert(a.rank === A.length, () => `Error in ${Yf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
+  w.assert(a.rank === A.length, () => `Error in ${Qf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
   let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(s.shape, s.dtype);
   for (let $ = 0; $ < d; ++$)
     for (let z = 0; z < g; ++z) {
@@ -21821,10 +21879,10 @@ function Xl(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   be(r, "sum");
   let o;
-  r.dtype === "bool" ? o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "int32" } }) : o = Os({ inputs: { x: r }, backend: n });
+  r.dtype === "bool" ? o = wr({ inputs: { x: r }, backend: n, attrs: { dtype: "int32" } }) : o = Os({ inputs: { x: r }, backend: n });
   let u = o.shape.length, l = w.parseAxisParam(a, o.shape), c = S.getAxesPermutation(l, u), p = l, d = o;
   c != null && (d = wn({ inputs: { x: o }, backend: n, attrs: { perm: c } }), p = S.getInnerMostAxes(p.length, u)), S.assertAxesAreInnerMostDims("sum", p, d.shape.length);
-  let [h, f] = S.computeOutAndReduceShapes(d.shape, p), m = S.upcastType(d.dtype, "int32"), g = Dd(n, h, m), b = w.sizeFromShape(f), y = n.data.get(g.dataId).values, v = n.data.get(d.dataId).values;
+  let [h, f] = S.computeOutAndReduceShapes(d.shape, p), m = S.upcastType(d.dtype, "int32"), g = Od(n, h, m), b = w.sizeFromShape(f), y = n.data.get(g.dataId).values, v = n.data.get(d.dataId).values;
   for (let x = 0; x < y.length; ++x) {
     let k = x * b, T = 0;
     for (let N = 0; N < b; ++N)
@@ -21837,8 +21895,8 @@ function Xl(e) {
   }
   return n.disposeIntermediateTensorInfo(o), c != null && n.disposeIntermediateTensorInfo(d), g;
 }
-var s6 = { kernelName: oi, backendName: "cpu", kernelFunc: Xl };
-function r6(e) {
+var h6 = { kernelName: ci, backendName: "cpu", kernelFunc: Xl };
+function f6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = S.decodeEinsumEquation(r, a.length);
   S.checkEinsumDimSizes(i.length, u, a);
   let { path: l, steps: c } = S.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
@@ -21849,7 +21907,7 @@ function r6(e) {
       let x = v.shape.slice();
       for (let k = 0; k < y.length; ++k)
         x.splice(y[k], 0, 1);
-      w.arraysEqual(v.shape, x) || (v = mt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Hp({ inputs: { a: v, b: d }, backend: n }), f.push(d));
+      w.arraysEqual(v.shape, x) || (v = mt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = jp({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
     m < p - 1 && (l[m] >= 0 && (d = Xl({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
   }
@@ -21857,8 +21915,8 @@ function r6(e) {
     m !== d && n.disposeIntermediateTensorInfo(m);
   return d;
 }
-var a6 = { kernelName: Qd, backendName: "cpu", kernelFunc: r6 };
-function i6(e) {
+var m6 = { kernelName: Jd, backendName: "cpu", kernelFunc: f6 };
+function g6(e) {
   let { inputs: t, backend: n } = e, { dy: s, y: r } = t;
   be([s, r], "eluGrad");
   let a = new Float32Array(w.sizeFromShape(r.shape)), i = n.data.get(r.dataId).values, o = n.data.get(s.dataId).values;
@@ -21868,30 +21926,30 @@ function i6(e) {
   }
   return n.makeTensorInfo(r.shape, "float32", a);
 }
-var o6 = { kernelName: bg, backendName: "cpu", kernelFunc: i6 };
-var u6 = S.ERF_P;
-var l6 = S.ERF_A1;
-var c6 = S.ERF_A2;
-var d6 = S.ERF_A3;
-var p6 = S.ERF_A4;
-var h6 = S.ERF_A5;
-var f6 = st(fl, (e) => {
-  let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + u6 * n);
-  return t * (1 - ((((h6 * s + p6) * s + d6) * s + c6) * s + l6) * s * Math.exp(-n * n));
+var b6 = { kernelName: vg, backendName: "cpu", kernelFunc: g6 };
+var y6 = S.ERF_P;
+var v6 = S.ERF_A1;
+var x6 = S.ERF_A2;
+var w6 = S.ERF_A3;
+var k6 = S.ERF_A4;
+var I6 = S.ERF_A5;
+var S6 = st(fl, (e) => {
+  let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + y6 * n);
+  return t * (1 - ((((I6 * s + k6) * s + w6) * s + x6) * s + v6) * s * Math.exp(-n * n));
 });
-var m6 = { kernelName: fl, backendName: "cpu", kernelFunc: f6 };
-function Od(e) {
+var C6 = { kernelName: fl, backendName: "cpu", kernelFunc: S6 };
+function zd(e) {
   let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, i = r.shape.length, o = r.shape.slice(), u = a;
   return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), mt({ inputs: { x: r }, backend: n, attrs: { shape: o } });
 }
-var g6 = { kernelName: fo, backendName: "cpu", kernelFunc: Od };
-var b6 = At((e, t) => e / t);
-var lv = Gt(Da, b6);
-var Mm = { kernelName: Da, backendName: "cpu", kernelFunc: lv };
-function FC(e, t, n) {
+var N6 = { kernelName: go, backendName: "cpu", kernelFunc: zd };
+var T6 = At((e, t) => e / t);
+var dv = Gt(Oa, T6);
+var Bm = { kernelName: Oa, backendName: "cpu", kernelFunc: dv };
+function LC(e, t, n) {
   let s = e.shape, r = s[0], a = s[1], i = n.data.get(e.dataId), o = i.complexTensorInfos.real, u = i.complexTensorInfos.imag, l = [r, a], c = w.sizeFromShape(l), p = w.getTypedArrayFromDType("float32", c), d = w.getTypedArrayFromDType("float32", c);
   for (let g = 0; g < r; g++) {
-    let b = ga({ inputs: { x: o }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), y = ga({ inputs: { x: u }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), v = Rn({ inputs: { real: b, imag: y }, backend: n }), { real: x, imag: k } = y6(v, t, n), T = S.mergeRealAndImagArrays(x, k);
+    let b = ba({ inputs: { x: o }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), y = ba({ inputs: { x: u }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), v = Rn({ inputs: { real: b, imag: y }, backend: n }), { real: x, imag: k } = $6(v, t, n), T = S.mergeRealAndImagArrays(x, k);
     for (let N = 0; N < a; N++) {
       let E = S.getComplexWithIndex(T, N);
       p[g * a + N] = E.real, d[g * a + N] = E.imag;
@@ -21901,30 +21959,30 @@ function FC(e, t, n) {
   let h = n.makeTensorInfo(l, "float32", p), f = n.makeTensorInfo(l, "float32", d), m = Rn({ inputs: { real: h, imag: f }, backend: n });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), m;
 }
-function y6(e, t, n) {
+function $6(e, t, n) {
   let s = w.sizeFromShape(e.shape), r = n.data.get(e.dataId), a = n.data.get(r.complexTensorInfos.real.dataId).values, i = n.data.get(r.complexTensorInfos.imag.dataId).values;
-  if (v6(s)) {
-    let o = Lm(a, i, s, t, n), u = [e.shape[0], e.shape[1]];
+  if (_6(s)) {
+    let o = Vm(a, i, s, t, n), u = [e.shape[0], e.shape[1]];
     if (t) {
-      let l = n.makeTensorInfo(u, "float32", o.real), c = n.makeTensorInfo(u, "float32", o.imag), p = n.makeTensorInfo([], "float32", w.createScalarValue(s, "float32")), d = Os({ inputs: { x: p }, backend: n }), h = Mm.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = Mm.kernelFunc({ inputs: { a: c, b: d }, backend: n }), m = n.data.get(h.dataId).values, g = n.data.get(f.dataId).values;
+      let l = n.makeTensorInfo(u, "float32", o.real), c = n.makeTensorInfo(u, "float32", o.imag), p = n.makeTensorInfo([], "float32", w.createScalarValue(s, "float32")), d = Os({ inputs: { x: p }, backend: n }), h = Bm.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = Bm.kernelFunc({ inputs: { a: c, b: d }, backend: n }), m = n.data.get(h.dataId).values, g = n.data.get(f.dataId).values;
       return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), { real: m, imag: g };
     }
     return o;
   } else {
-    let o = S.mergeRealAndImagArrays(a, i), u = x6(o, s, t);
+    let o = S.mergeRealAndImagArrays(a, i), u = A6(o, s, t);
     return S.splitRealAndImagArrays(u);
   }
 }
-function v6(e) {
+function _6(e) {
   return (e & e - 1) === 0;
 }
-function Lm(e, t, n, s, r) {
+function Vm(e, t, n, s, r) {
   if (n === 1)
     return { real: e, imag: t };
-  let a = S.mergeRealAndImagArrays(e, t), i = n / 2, o = S.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = Rn({ inputs: { real: p, imag: d }, backend: r }), f = S.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = Rn({ inputs: { real: y, imag: v }, backend: r }), k = Lm(u, l, i, s, r), T = k.real, N = k.imag, E = [T.length], A = r.makeTensorInfo(E, "float32", T), P = r.makeTensorInfo(E, "float32", N), R = Rn({ inputs: { real: A, imag: P }, backend: r }), F = Lm(m, g, i, s, r), $ = F.real, z = F.imag, W = [$.length], q = r.makeTensorInfo(W, "float32", $), K = r.makeTensorInfo(W, "float32", z), Y = Rn({ inputs: { real: q, imag: K }, backend: r }), Z = S.exponents(n, s), te = [Z.real.length], ee = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = Rn({ inputs: { real: ee, imag: se }, backend: r }), oe = Hp({ inputs: { a: ne, b: Y }, backend: r }), re = Kl({ inputs: { a: R, b: oe }, backend: r }), le = iv({ inputs: { a: R, b: oe }, backend: r }), me = ma({ inputs: { input: re }, backend: r }), ke = ma({ inputs: { input: le }, backend: r }), Se = no({ inputs: { input: re }, backend: r }), Ee = no({ inputs: { input: le }, backend: r }), Pe = so({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = so({ inputs: [Se, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
+  let a = S.mergeRealAndImagArrays(e, t), i = n / 2, o = S.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = Rn({ inputs: { real: p, imag: d }, backend: r }), f = S.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = Rn({ inputs: { real: y, imag: v }, backend: r }), k = Vm(u, l, i, s, r), T = k.real, N = k.imag, E = [T.length], A = r.makeTensorInfo(E, "float32", T), P = r.makeTensorInfo(E, "float32", N), R = Rn({ inputs: { real: A, imag: P }, backend: r }), F = Vm(m, g, i, s, r), $ = F.real, z = F.imag, W = [$.length], q = r.makeTensorInfo(W, "float32", $), K = r.makeTensorInfo(W, "float32", z), Y = Rn({ inputs: { real: q, imag: K }, backend: r }), Z = S.exponents(n, s), te = [Z.real.length], ee = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = Rn({ inputs: { real: ee, imag: se }, backend: r }), oe = jp({ inputs: { a: ne, b: Y }, backend: r }), re = Kl({ inputs: { a: R, b: oe }, backend: r }), le = uv({ inputs: { a: R, b: oe }, backend: r }), me = ga({ inputs: { input: re }, backend: r }), ke = ga({ inputs: { input: le }, backend: r }), Se = ro({ inputs: { input: re }, backend: r }), Ee = ro({ inputs: { input: le }, backend: r }), Pe = ao({ inputs: [me, ke], backend: r, attrs: { axis: 0 } }), Xe = ao({ inputs: [Se, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
   return r.disposeIntermediateTensorInfo(p), r.disposeIntermediateTensorInfo(d), r.disposeIntermediateTensorInfo(h), r.disposeIntermediateTensorInfo(y), r.disposeIntermediateTensorInfo(v), r.disposeIntermediateTensorInfo(x), r.disposeIntermediateTensorInfo(A), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(R), r.disposeIntermediateTensorInfo(q), r.disposeIntermediateTensorInfo(K), r.disposeIntermediateTensorInfo(Y), r.disposeIntermediateTensorInfo(ee), r.disposeIntermediateTensorInfo(se), r.disposeIntermediateTensorInfo(ne), r.disposeIntermediateTensorInfo(oe), r.disposeIntermediateTensorInfo(re), r.disposeIntermediateTensorInfo(le), r.disposeIntermediateTensorInfo(me), r.disposeIntermediateTensorInfo(Se), r.disposeIntermediateTensorInfo(ke), r.disposeIntermediateTensorInfo(Ee), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
 }
-function x6(e, t, n) {
+function A6(e, t, n) {
   let s = new Float32Array(t * 2);
   for (let r = 0; r < t; r++) {
     let a = 0, i = 0;
@@ -21936,20 +21994,20 @@ function x6(e, t, n) {
   }
   return s;
 }
-function w6(e) {
-  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = FC(o, false, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
+function E6(e) {
+  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = LC(o, false, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
 }
-var k6 = { kernelName: yg, backendName: "cpu", kernelFunc: w6 };
-function cv(e) {
+var R6 = { kernelName: xg, backendName: "cpu", kernelFunc: E6 };
+function pv(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r, dtype: a } = n, i = a || w.inferDtype(r), o = w.getArrayFromDType(i, w.sizeFromShape(s));
-  return S6(o, r, i), t.makeTensorInfo(s, i, o);
+  return F6(o, r, i), t.makeTensorInfo(s, i, o);
 }
-var I6 = { kernelName: ml, backendName: "cpu", kernelFunc: cv };
-function S6(e, t, n) {
+var D6 = { kernelName: ml, backendName: "cpu", kernelFunc: pv };
+function F6(e, t, n) {
   e.fill(t);
 }
-var C6 = { kernelName: go, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var O6 = { kernelName: yo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, r = n, a = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [i, o, u, l] = s.shape, c = r.data.get(s.dataId).values;
   for (let d = 0; d < i; d++) {
     let h = d * u * o * l;
@@ -21970,44 +22028,44 @@ var C6 = { kernelName: go, backendName: "cpu", kernelFunc: ({ inputs: e, attrs:
   }
   return { dataId: r.write(a, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
-var N6 = At((e, t) => Math.floor(e / t));
-var T6 = Gt(za, N6, null, "int32");
-var $6 = { kernelName: za, backendName: "cpu", kernelFunc: T6 };
-function _6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = RC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
+var P6 = At((e, t) => Math.floor(e / t));
+var z6 = Gt(La, P6, null, "int32");
+var M6 = { kernelName: La, backendName: "cpu", kernelFunc: z6 };
+function L6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = zC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
   if (i) {
     let g = m;
     m = Kl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
   }
   if (h) {
     let g = m;
-    m = ov(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
+    m = lv(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
   }
   return m;
 }
-var A6 = { kernelName: aa, backendName: "cpu", kernelFunc: _6 };
-function E6(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = DC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
+var B6 = { kernelName: ia, backendName: "cpu", kernelFunc: L6 };
+function V6(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = MC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
   if (i) {
     let g = m;
     m = Kl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
   }
   if (h) {
     let g = m;
-    m = ov(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
+    m = lv(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
   }
   return m;
 }
-var R6 = { kernelName: ia, backendName: "cpu", kernelFunc: E6 };
-function D6(e) {
+var W6 = { kernelName: oa, backendName: "cpu", kernelFunc: V6 };
+function U6(e) {
   let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = w.sizeFromShape(s.shape), i = r.shape, o = i[i.length - 1], [u, l, c, p] = S.prepareAndValidate(s, r);
   if (l === 0)
     return n.makeTensorInfo(u, s.dtype, []);
-  let d = n.data.get(r.dataId).values, h = n.bufferSync(s), f = Y0(d, h, s.dtype, l, o, c, p, s.shape, a);
+  let d = n.data.get(r.dataId).values, h = n.bufferSync(s), f = tC(d, h, s.dtype, l, o, c, p, s.shape, a);
   return n.makeTensorInfo(u, s.dtype, f.values);
 }
-var F6 = { kernelName: yo, backendName: "cpu", kernelFunc: D6 };
-function O6(e) {
+var G6 = { kernelName: xo, backendName: "cpu", kernelFunc: U6 };
+function H6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s;
   be([r, a], "gatherV2");
   let u = w.parseAxisParam(i, r.shape)[0], l = n.data.get(a.dataId).values, c = r.shape[u];
@@ -22017,37 +22075,37 @@ function O6(e) {
   }
   let p = o;
   o == null && (p = 0);
-  let d = w.sizeFromShape(a.shape), h = S.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = mt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = mt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = Q0(y, b, g);
+  let d = w.sizeFromShape(a.shape), h = S.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = mt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = mt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = nC(y, b, g);
   return n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), n.makeTensorInfo(h.outputShape, v.dtype, v.values);
 }
-var P6 = { kernelName: bo, backendName: "cpu", kernelFunc: O6 };
-function z6(e) {
-  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = FC(o, true, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
+var q6 = { kernelName: vo, backendName: "cpu", kernelFunc: H6 };
+function j6(e) {
+  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = LC(o, true, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
 }
-var M6 = { kernelName: vg, backendName: "cpu", kernelFunc: z6 };
-var L6 = st(gl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
-var B6 = { kernelName: gl, backendName: "cpu", kernelFunc: L6 };
-var V6 = st(bl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
-var W6 = { kernelName: bl, backendName: "cpu", kernelFunc: V6 };
-var U6 = st(yl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
-var G6 = { kernelName: yl, backendName: "cpu", kernelFunc: U6 };
-function H6(e) {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = nC(s, r, a);
+var K6 = { kernelName: wg, backendName: "cpu", kernelFunc: j6 };
+var X6 = st(gl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
+var Y6 = { kernelName: gl, backendName: "cpu", kernelFunc: X6 };
+var Q6 = st(bl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
+var Z6 = { kernelName: bl, backendName: "cpu", kernelFunc: Q6 };
+var J6 = st(yl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
+var eq = { kernelName: yl, backendName: "cpu", kernelFunc: J6 };
+function tq(e) {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = oC(s, r, a);
   return t.makeTensorInfo([i.length], "float32", i);
 }
-var q6 = { kernelName: xg, backendName: "cpu", kernelFunc: H6 };
-var j6 = st(vl, (e) => Math.log1p(e));
-var K6 = { kernelName: vl, backendName: "cpu", kernelFunc: j6 };
-var X6 = At((e, t) => e && t);
-var Y6 = Gt(ko, X6, null, "bool");
-var Q6 = { kernelName: ko, backendName: "cpu", kernelFunc: Y6 };
-var Z6 = st(xl, (e) => e ? 0 : 1, "bool");
-var J6 = { kernelName: xl, backendName: "cpu", kernelFunc: Z6 };
-var eq = At((e, t) => e || t);
-var tq = Gt(Jd, eq, null, "bool");
-var nq = { kernelName: Jd, backendName: "cpu", kernelFunc: tq };
-function sq(e) {
+var nq = { kernelName: kg, backendName: "cpu", kernelFunc: tq };
+var sq = st(vl, (e) => Math.log1p(e));
+var rq = { kernelName: vl, backendName: "cpu", kernelFunc: sq };
+var aq = At((e, t) => e && t);
+var iq = Gt(So, aq, null, "bool");
+var oq = { kernelName: So, backendName: "cpu", kernelFunc: iq };
+var uq = st(xl, (e) => e ? 0 : 1, "bool");
+var lq = { kernelName: xl, backendName: "cpu", kernelFunc: uq };
+var cq = At((e, t) => e || t);
+var dq = Gt(tp, cq, null, "bool");
+var pq = { kernelName: tp, backendName: "cpu", kernelFunc: dq };
+function hq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s;
   be(r, "LRN");
   let l = r.shape[3], c = l - 1, p = n.data.get(r.dataId).values, d = w.sizeFromShape(r.shape), h = new Float32Array(d);
@@ -22065,8 +22123,8 @@ function sq(e) {
   }
   return n.makeTensorInfo(r.shape, r.dtype, h);
 }
-var rq = { kernelName: ep, backendName: "cpu", kernelFunc: sq };
-function aq(e) {
+var fq = { kernelName: np, backendName: "cpu", kernelFunc: hq };
+function mq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s;
   be(i, "LRNGrad");
   let p = w.sizeFromShape(i.shape), d = i.shape[3], h = n.data.get(i.dataId).values, f = n.data.get(r.dataId).values, m = n.data.get(a.dataId).values, g = new Float32Array(p), b = p;
@@ -22082,21 +22140,21 @@ function aq(e) {
   }
   return n.makeTensorInfo(i.shape, r.dtype, g);
 }
-var iq = { kernelName: wg, backendName: "cpu", kernelFunc: aq };
-function OC(e) {
+var gq = { kernelName: Ig, backendName: "cpu", kernelFunc: mq };
+function BC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s, o = n, u = r.shape, l = u.length, c = w.parseAxisParam(a, u), p = c, d = S.getAxesPermutation(p, l), h = o.data.get(r.dataId).values;
   if (d != null) {
     let x = new Array(l);
     for (let k = 0; k < x.length; k++)
       x[k] = u[d[k]];
-    h = sv(h, u, r.dtype, d, x), p = S.getInnerMostAxes(p.length, l), u = x;
+    h = av(h, u, r.dtype, d, x), p = S.getInnerMostAxes(p.length, l), u = x;
   }
   be(r, "max"), S.assertAxesAreInnerMostDims("max", p, l);
-  let [f, m] = S.computeOutAndReduceShapes(u, p), g = w.sizeFromShape(m), b = rC(h, g, f, r.dtype), y = o.write(b, f, r.dtype), v = f;
+  let [f, m] = S.computeOutAndReduceShapes(u, p), g = w.sizeFromShape(m), b = lC(h, g, f, r.dtype), y = o.write(b, f, r.dtype), v = f;
   return i && (v = S.expandShapeToKeepDim(f, c)), { dataId: y, shape: v, dtype: r.dtype };
 }
-var oq = { kernelName: Ua, backendName: "cpu", kernelFunc: OC };
-function uq(e) {
+var bq = { kernelName: Ha, backendName: "cpu", kernelFunc: BC };
+function yq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
   be(r, "maxPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
@@ -22105,23 +22163,23 @@ function uq(e) {
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     p = Os({ inputs: { x: r }, backend: n });
   else {
-    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = uv(d, r.shape, r.dtype, h, c, "max");
+    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = cv(d, r.shape, r.dtype, h, c, "max");
     p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
   }
   return p;
 }
-var lq = { kernelName: Ha, backendName: "cpu", kernelFunc: uq };
-function cq(e) {
+var vq = { kernelName: ja, backendName: "cpu", kernelFunc: yq };
+function xq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
   be(r, "maxPool3d");
-  let c = S.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = EC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
+  let c = S.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = PC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
   return n.makeTensorInfo(d.shape, "float32", d.values);
 }
-var dq = { kernelName: tp, backendName: "cpu", kernelFunc: cq };
-function pq(e) {
+var wq = { kernelName: sp, backendName: "cpu", kernelFunc: xq };
+function kq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
   be([r, a], "maxPool3DGrad");
-  let c = S.computePool3DInfo(a.shape, i, o, 1, u, l), p = n.bufferSync(a), d = JG(p, c), h = c.strideDepth, f = c.strideHeight, m = c.strideWidth, g = c.dilationDepth, b = c.dilationHeight, y = c.dilationWidth, v = c.effectiveFilterDepth, x = c.effectiveFilterHeight, k = c.effectiveFilterWidth, T = v - 1 - c.padInfo.front, N = k - 1 - c.padInfo.left, E = x - 1 - c.padInfo.top, A = De(a.shape, "float32"), P = n.bufferSync(r);
+  let c = S.computePool3DInfo(a.shape, i, o, 1, u, l), p = n.bufferSync(a), d = lH(p, c), h = c.strideDepth, f = c.strideHeight, m = c.strideWidth, g = c.dilationDepth, b = c.dilationHeight, y = c.dilationWidth, v = c.effectiveFilterDepth, x = c.effectiveFilterHeight, k = c.effectiveFilterWidth, T = v - 1 - c.padInfo.front, N = k - 1 - c.padInfo.left, E = x - 1 - c.padInfo.top, A = De(a.shape, "float32"), P = n.bufferSync(r);
   for (let R = 0; R < c.batchSize; ++R)
     for (let F = 0; F < c.inChannels; ++F)
       for (let $ = 0; $ < c.inDepth; ++$)
@@ -22149,11 +22207,11 @@ function pq(e) {
           }
   return n.makeTensorInfo(A.shape, A.dtype, A.values);
 }
-var hq = { kernelName: Ig, backendName: "cpu", kernelFunc: pq };
-function fq(e) {
+var Iq = { kernelName: Cg, backendName: "cpu", kernelFunc: kq };
+function Sq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
   be([a, i], "maxPoolGrad");
-  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = S.computePool2DInfo(o.shape, u, l, 1, c, p), h = n.data.get(o.dataId).values, f = De(d.outShape, o.dtype, AC(h, o.shape, o.dtype, d).values), m = d.strideHeight, g = d.strideWidth, b = d.dilationHeight, y = d.dilationWidth, v = d.effectiveFilterHeight, x = d.effectiveFilterWidth, k = x - 1 - d.padInfo.left, T = v - 1 - d.padInfo.top, N = De(o.shape, "float32"), E = n.data.get(r.dataId).values, A = De(r.shape, "float32", E);
+  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = S.computePool2DInfo(o.shape, u, l, 1, c, p), h = n.data.get(o.dataId).values, f = De(d.outShape, o.dtype, OC(h, o.shape, o.dtype, d).values), m = d.strideHeight, g = d.strideWidth, b = d.dilationHeight, y = d.dilationWidth, v = d.effectiveFilterHeight, x = d.effectiveFilterWidth, k = x - 1 - d.padInfo.left, T = v - 1 - d.padInfo.top, N = De(o.shape, "float32"), E = n.data.get(r.dataId).values, A = De(r.shape, "float32", E);
   for (let P = 0; P < d.batchSize; ++P)
     for (let R = 0; R < d.inChannels; ++R)
       for (let F = 0; F < d.inHeight; ++F)
@@ -22176,29 +22234,29 @@ function fq(e) {
         }
   return n.makeTensorInfo(N.shape, N.dtype, N.values);
 }
-var mq = { kernelName: kg, backendName: "cpu", kernelFunc: fq };
-function gq(e, t, n, s, r) {
-  let a = w.computeStrides(t), i = uv(e, t, n, a, r, "max"), o = AC(e, t, n, r, true, s);
+var Cq = { kernelName: Sg, backendName: "cpu", kernelFunc: Sq };
+function Nq(e, t, n, s, r) {
+  let a = w.computeStrides(t), i = cv(e, t, n, a, r, "max"), o = OC(e, t, n, r, true, s);
   return [i.values, o.values];
 }
-var bq = { kernelName: Sg, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var Tq = { kernelName: Ng, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
   be(s, "MaxPoolWithArgmax");
-  let l = u.data.get(s.dataId).values, c = S.computePool2DInfo(s.shape, r, a, [1, 1], i), [p, d] = gq(l, s.shape, s.dtype, o, c), h = u.write(p, c.outShape, s.dtype), f = u.write(d, c.outShape, s.dtype);
+  let l = u.data.get(s.dataId).values, c = S.computePool2DInfo(s.shape, r, a, [1, 1], i), [p, d] = Nq(l, s.shape, s.dtype, o, c), h = u.write(p, c.outShape, s.dtype), f = u.write(d, c.outShape, s.dtype);
   return [{ dataId: h, shape: c.outShape, dtype: s.dtype }, { dataId: f, shape: c.outShape, dtype: "int32" }];
 } };
-function yq(e) {
+function $q(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = w.parseAxisParam(a, r.shape), l = S.computeOutAndReduceShapes(r.shape, o)[1], c = w.sizeFromShape(l), p = [], d = n.makeTensorInfo([], "float32", new Float32Array([c]));
   p.push(d);
-  let h = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
+  let h = wr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
   p.push(h);
-  let f = lv({ inputs: { a: h, b: d }, backend: n });
+  let f = dv({ inputs: { a: h, b: d }, backend: n });
   p.push(f);
   let m = Xl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: i } });
   return p.forEach((g) => n.disposeIntermediateTensorInfo(g)), m;
 }
-var vq = { kernelName: qa, backendName: "cpu", kernelFunc: yq };
-function xq(e) {
+var _q = { kernelName: Ka, backendName: "cpu", kernelFunc: $q };
+function Aq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   be(r, "min");
   let o = w.parseAxisParam(a, r.shape), u = o, l = S.getAxesPermutation(u, r.shape.length), c = r;
@@ -22220,8 +22278,8 @@ function xq(e) {
   }
   return g;
 }
-var wq = { kernelName: ja, backendName: "cpu", kernelFunc: xq };
-function kq(e) {
+var Eq = { kernelName: Xa, backendName: "cpu", kernelFunc: Aq };
+function Rq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, mode: i } = s;
   be(r, "mirrorPad");
   let o = a.map((v, x) => v[0] + r.shape[x] + v[1]), u = a.map((v) => v[0]), l = a.map((v, x) => v[0] + r.shape[x]), c = i === "reflect" ? 0 : 1, p = n.data.get(r.dataId).values, d = r.shape.length, h = w.computeStrides(r.shape), f = w.sizeFromShape(o), m = o.length, g = w.computeStrides(o), b = w.getTypedArrayFromDType(r.dtype, f);
@@ -22235,32 +22293,32 @@ function kq(e) {
   }
   return { dataId: n.write(b, o, r.dtype), shape: o, dtype: r.dtype };
 }
-var Iq = { kernelName: Xa, backendName: "cpu", kernelFunc: kq };
-var Sq = At((e, t) => {
+var Dq = { kernelName: Qa, backendName: "cpu", kernelFunc: Rq };
+var Fq = At((e, t) => {
   let n = e % t;
   return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;
 });
-var Cq = Gt(wl, Sq);
-var Nq = { kernelName: wl, backendName: "cpu", kernelFunc: Cq };
-var Tq = xa(Wd());
-function PC(e) {
+var Oq = Gt(wl, Fq);
+var Pq = { kernelName: wl, backendName: "cpu", kernelFunc: Oq };
+var zq = wa(Gd());
+function VC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = r.shape.length, o = a;
   if (o === -1 && (o = i - 1), o !== i - 1)
     throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);
-  let u = w.parseAxisParam([o], r.shape), l = OC({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = S.expandShapeToKeepDim(l.shape, u), p = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = iv({ inputs: { a: r, b: p }, backend: n }), h = j0({ inputs: { x: d }, backend: n }), f = Xl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = lv({ inputs: { a: h, b: m }, backend: n });
+  let u = w.parseAxisParam([o], r.shape), l = BC({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = S.expandShapeToKeepDim(l.shape, u), p = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = uv({ inputs: { a: r, b: p }, backend: n }), h = Z0({ inputs: { x: d }, backend: n }), f = Xl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = dv({ inputs: { a: h, b: m }, backend: n });
   return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
 }
-var $q = { kernelName: ui, backendName: "cpu", kernelFunc: PC };
-function _q(e) {
+var Mq = { kernelName: di, backendName: "cpu", kernelFunc: VC };
+function Lq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s;
   be(r, "multinomial");
-  let u = o ? r : PC({ inputs: { logits: r }, backend: n, attrs: { dim: -1 } }), l = u.shape[0], c = u.shape[1], p = n.data.get(u.dataId).values, d = [l, a], h = w.makeZerosTypedArray(w.sizeFromShape(d), "int32");
+  let u = o ? r : VC({ inputs: { logits: r }, backend: n, attrs: { dim: -1 } }), l = u.shape[0], c = u.shape[1], p = n.data.get(u.dataId).values, d = [l, a], h = w.makeZerosTypedArray(w.sizeFromShape(d), "int32");
   for (let f = 0; f < l; ++f) {
     let m = f * c, g = new Float32Array(c - 1);
     g[0] = p[m];
     for (let v = 1; v < g.length; ++v)
       g[v] = g[v - 1] + p[m + v];
-    let b = Tq.alea(i.toString()), y = f * a;
+    let b = zq.alea(i.toString()), y = f * a;
     for (let v = 0; v < a; ++v) {
       let x = b();
       h[y + v] = g.length;
@@ -22273,32 +22331,32 @@ function _q(e) {
   }
   return o || n.disposeIntermediateTensorInfo(u), n.makeTensorInfo(d, "int32", h);
 }
-var Aq = { kernelName: Cg, backendName: "cpu", kernelFunc: _q };
-var Eq = xs.nonMaxSuppressionV3Impl;
-function Rq(e) {
+var Bq = { kernelName: Tg, backendName: "cpu", kernelFunc: Lq };
+var Vq = xs.nonMaxSuppressionV3Impl;
+function Wq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s;
   be(r, "NonMaxSuppression");
-  let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = Eq(l, c, i, o, u);
+  let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = Vq(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var Dq = { kernelName: Co, backendName: "cpu", kernelFunc: Rq };
-var Fq = xs.nonMaxSuppressionV4Impl;
-function Oq(e) {
+var Uq = { kernelName: To, backendName: "cpu", kernelFunc: Wq };
+var Gq = xs.nonMaxSuppressionV4Impl;
+function Hq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s;
   be(r, "NonMaxSuppressionPadded");
-  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = Fq(c, p, i, o, u, l);
+  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = Gq(c, p, i, o, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var Pq = { kernelName: kl, backendName: "cpu", kernelFunc: Oq };
-var zq = xs.nonMaxSuppressionV5Impl;
-function Mq(e) {
+var qq = { kernelName: kl, backendName: "cpu", kernelFunc: Hq };
+var jq = xs.nonMaxSuppressionV5Impl;
+function Kq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s;
   be(r, "NonMaxSuppressionWithScore");
-  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = zq(c, p, d, h, f, m);
+  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = jq(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var Lq = { kernelName: No, backendName: "cpu", kernelFunc: Mq };
-function Bq(e) {
+var Xq = { kernelName: $o, backendName: "cpu", kernelFunc: Kq };
+function Yq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s;
   be(r, "oneHot");
   let u = w.sizeFromShape(r.shape), l = new Float32Array(u * a);
@@ -22308,45 +22366,45 @@ function Bq(e) {
     c[p] >= 0 && c[p] < a && (l[p * a + c[p]] = i);
   return n.makeTensorInfo([...r.shape, a], "int32", l);
 }
-var Vq = { kernelName: $o, backendName: "cpu", kernelFunc: Bq };
-function Pd(e) {
+var Qq = { kernelName: Ao, backendName: "cpu", kernelFunc: Yq };
+function Md(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("zerosLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ma({ inputs: { input: s }, backend: n }), a = Pd({ inputs: { x: r }, backend: n }), i = no({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
+    let r = ga({ inputs: { input: s }, backend: n }), a = Md({ inputs: { x: r }, backend: n }), i = ro({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
-    return cv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
+    return pv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
 }
-var Wq = { kernelName: Ho, backendName: "cpu", kernelFunc: Pd };
-function zC(e) {
+var Zq = { kernelName: qo, backendName: "cpu", kernelFunc: Md };
+function WC(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ma({ inputs: { input: s }, backend: n }), a = zC({ inputs: { x: r }, backend: n }), i = no({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
+    let r = ga({ inputs: { input: s }, backend: n }), a = WC({ inputs: { x: r }, backend: n }), i = ro({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = Rn({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
-    return cv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
+    return pv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
 }
-var Uq = { kernelName: To, backendName: "cpu", kernelFunc: zC };
-function MC(e) {
+var Jq = { kernelName: _o, backendName: "cpu", kernelFunc: WC };
+function UC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Od({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return zd({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = Od({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = zd({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
-  }), l = so({ inputs: u, backend: n, attrs: { axis: r } });
+  }), l = ao({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var Gq = { kernelName: _o, backendName: "cpu", kernelFunc: MC };
-function Hq(e) {
+var ej = { kernelName: Eo, backendName: "cpu", kernelFunc: UC };
+function tj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
   be(r, "pad");
   let o = a.map((y, v) => y[0] + r.shape[v] + y[1]), u = a.map((y) => y[0]), l = n.data.get(r.dataId).values, c = w.sizeFromShape(r.shape), p = r.shape.length, d = w.computeStrides(r.shape), h = w.sizeFromShape(o), f = o.length, m = w.computeStrides(o), g = w.getTypedArrayFromDType(r.dtype, h);
@@ -22357,18 +22415,18 @@ function Hq(e) {
   }
   return { dataId: n.write(g, o, r.dtype), shape: o, dtype: r.dtype };
 }
-var LC = { kernelName: Qa, backendName: "cpu", kernelFunc: Hq };
-var qq = At((e, t) => Math.pow(e, t));
-var jq = Gt(Za, qq);
-var Kq = { kernelName: Za, backendName: "cpu", kernelFunc: jq };
-function Xq(e) {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = rv(s, r, i, a);
+var GC = { kernelName: Ja, backendName: "cpu", kernelFunc: tj };
+var nj = At((e, t) => Math.pow(e, t));
+var sj = Gt(ei, nj);
+var rj = { kernelName: ei, backendName: "cpu", kernelFunc: sj };
+function aj(e) {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = iv(s, r, i, a);
   return t.makeTensorInfo([o.length], a, o);
 }
-var Yq = { kernelName: Il, backendName: "cpu", kernelFunc: Xq };
-var Qq = st(Sl, (e) => 1 / e);
-var Zq = { kernelName: Sl, backendName: "cpu", kernelFunc: Qq };
-function Jq(e) {
+var ij = { kernelName: Il, backendName: "cpu", kernelFunc: aj };
+var oj = st(Sl, (e) => 1 / e);
+var uj = { kernelName: Sl, backendName: "cpu", kernelFunc: oj };
+function lj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
   be(r, "resizeBilinear");
   let u = w.computeStrides(r.shape), [l, c] = o, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(w.sizeFromShape([p, l, c, f])), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = 0, x = b[0] / y[0], k = b[1] / y[1];
@@ -22389,8 +22447,8 @@ function Jq(e) {
     }
   return n.makeTensorInfo([p, l, c, f], "float32", g);
 }
-var ej = { kernelName: ti, backendName: "cpu", kernelFunc: Jq };
-function tj(e) {
+var cj = { kernelName: ri, backendName: "cpu", kernelFunc: lj };
+function dj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
   be([a, r], "resizeBilinearGrad");
   let o = w.computeStrides(r.shape), [u, l, c, p] = r.shape, [, d, h] = a.shape, f = new Float32Array(u * l * c * p), m = [i && d > 1 ? l - 1 : l, i && h > 1 ? c - 1 : c], g = [i && d > 1 ? d - 1 : d, i && h > 1 ? h - 1 : h], b = m[0] / g[0], y = m[1] / g[1], v = n.data.get(a.dataId).values, x = 0;
@@ -22409,8 +22467,8 @@ function tj(e) {
   }
   return n.makeTensorInfo([u, c, l, p], "float32", f);
 }
-var nj = { kernelName: Tg, backendName: "cpu", kernelFunc: tj };
-function sj(e) {
+var pj = { kernelName: _g, backendName: "cpu", kernelFunc: dj };
+function hj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
   be(r, "resizeNearestNeighbor");
   let u = w.computeStrides(r.shape), [l, c] = o, [p, d, h, f] = r.shape, m = n.data.get(r.dataId).values, g = new Float32Array(p * l * c * f), b = [a && l > 1 ? d - 1 : d, a && c > 1 ? h - 1 : h], y = [a && l > 1 ? l - 1 : l, a && c > 1 ? c - 1 : c], v = b[0] / y[0], x = b[1] / y[1], k = 0;
@@ -22433,8 +22491,8 @@ function sj(e) {
   }
   return n.makeTensorInfo([p, l, c, f], r.dtype, g);
 }
-var rj = { kernelName: Cl, backendName: "cpu", kernelFunc: sj };
-function aj(e) {
+var fj = { kernelName: Cl, backendName: "cpu", kernelFunc: hj };
+function mj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
   be([a, r], "resizeNearestNeighborGrad");
   let o = w.computeStrides(r.shape), u = w.computeStrides(a.shape), [l, c, p, d] = r.shape, [, h, f] = a.shape, m = new Float32Array(l * c * p * d), g = n.data.get(a.dataId).values, b = [i && h > 1 ? c - 1 : c, i && f > 1 ? p - 1 : p], y = [i && h > 1 ? h - 1 : h, i && f > 1 ? f - 1 : f], v = b[0] / y[0], x = b[1] / y[1], k = 1 / v, T = 1 / x, N = Math.ceil(k) * 2 + 2, E = Math.ceil(T) * 2 + 2;
@@ -22467,8 +22525,8 @@ function aj(e) {
   }
   return n.makeTensorInfo(r.shape, r.dtype, m);
 }
-var ij = { kernelName: Ng, backendName: "cpu", kernelFunc: aj };
-function oj(e) {
+var gj = { kernelName: $g, backendName: "cpu", kernelFunc: mj };
+function bj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s;
   be(r, "reverse");
   let i = r.shape.length, o = w.parseAxisParam(a, r.shape);
@@ -22481,8 +22539,8 @@ function oj(e) {
   }
   return n.makeTensorInfo(u.shape, u.dtype, u.values);
 }
-var uj = { kernelName: Ro, backendName: "cpu", kernelFunc: oj };
-var lj = { kernelName: qo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var yj = { kernelName: Do, backendName: "cpu", kernelFunc: bj };
+var vj = { kernelName: jo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [l, c, p, d] = s.shape, [h, f] = S.getImageCenter(i, c, p), m = 255, g = Math.sin(r), b = Math.cos(r), y = o.data.get(s.dataId).values;
   for (let x = 0; x < l; x++) {
     let k = x * p * c * d;
@@ -22506,12 +22564,12 @@ var lj = { kernelName: qo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs:
   }
   return { dataId: o.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
-var cj = st(Do, (e) => {
+var xj = st(Fo, (e) => {
   let t = Math.floor(e);
   return e - t < 0.5 ? Math.floor(e) : e - t > 0.5 ? Math.ceil(e) : t % 2 === 0 ? t : t + 1;
 });
-var dj = { kernelName: Do, backendName: "cpu", kernelFunc: cj };
-function BC(e, t, n, s, r, a, i, o, u, l) {
+var wj = { kernelName: Fo, backendName: "cpu", kernelFunc: xj };
+function HC(e, t, n, s, r, a, i, o, u, l) {
   let c = [s / r, r], p = e.values, d = t.values;
   if (s === 0)
     return De(n, t.dtype);
@@ -22530,12 +22588,12 @@ function BC(e, t, n, s, r, a, i, o, u, l) {
   }
   return h;
 }
-function pj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = S.calculateShapes(a, r, i), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = BC(h, f, i, p, l, u, o, c, 0, d);
+function kj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = S.calculateShapes(a, r, i), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = HC(h, f, i, p, l, u, o, c, 0, d);
   return n.makeTensorInfo(i, m.dtype, m.values);
 }
-var hj = { kernelName: Fo, backendName: "cpu", kernelFunc: pj };
-function fj(e) {
+var Ij = { kernelName: Oo, backendName: "cpu", kernelFunc: kj };
+function Sj(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t;
   be([s, r, a], "select");
   let i = s.shape.length, o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = cn(r.dtype, a.dtype), p = w.makeZerosTypedArray(w.sizeFromShape(r.shape), c), d = 0, h = i === 0 || i > 1 || r.shape.length === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
@@ -22544,36 +22602,36 @@ function fj(e) {
       o[f] === 1 ? p[d++] = u[f] : p[d++] = l[f];
   return n.makeTensorInfo(r.shape, c, p);
 }
-var mj = { kernelName: Oo, backendName: "cpu", kernelFunc: fj };
-var gj = S.SELU_SCALEALPHA;
-var bj = S.SELU_SCALE;
-var yj = st(Nl, (e) => e >= 0 ? bj * e : gj * (Math.exp(e) - 1));
-var vj = { kernelName: Nl, backendName: "cpu", kernelFunc: yj };
-var xj = st(Tl, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
-var wj = { kernelName: Tl, backendName: "cpu", kernelFunc: xj };
-var kj = st(ri, (e) => Math.sin(e));
-var Ij = { kernelName: ri, backendName: "cpu", kernelFunc: kj };
-var Sj = st(zo, (e) => Math.sinh(e));
-var Cj = { kernelName: zo, backendName: "cpu", kernelFunc: Sj };
-var Nj = 11920928955078125e-23;
-var nw = Math.log(Nj) + 2;
-var Tj = st($l, (e) => {
-  let t = e > -nw, n = e < nw, s = Math.exp(e), r;
+var Cj = { kernelName: Po, backendName: "cpu", kernelFunc: Sj };
+var Nj = S.SELU_SCALEALPHA;
+var Tj = S.SELU_SCALE;
+var $j = st(Nl, (e) => e >= 0 ? Tj * e : Nj * (Math.exp(e) - 1));
+var _j = { kernelName: Nl, backendName: "cpu", kernelFunc: $j };
+var Aj = st(Tl, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
+var Ej = { kernelName: Tl, backendName: "cpu", kernelFunc: Aj };
+var Rj = st(oi, (e) => Math.sin(e));
+var Dj = { kernelName: oi, backendName: "cpu", kernelFunc: Rj };
+var Fj = st(Mo, (e) => Math.sinh(e));
+var Oj = { kernelName: Mo, backendName: "cpu", kernelFunc: Fj };
+var Pj = 11920928955078125e-23;
+var sw = Math.log(Pj) + 2;
+var zj = st($l, (e) => {
+  let t = e > -sw, n = e < sw, s = Math.exp(e), r;
   return n ? r = s : t ? r = e : r = Math.log(1 + s), r;
 });
-var $j = { kernelName: $l, backendName: "cpu", kernelFunc: Tj };
-function _j(e) {
+var Mj = { kernelName: $l, backendName: "cpu", kernelFunc: zj };
+function Lj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   be([r], "spaceToBatchND");
   let o = w.sizeFromShape(a), u = [[0, 0]];
   u.push(...i);
   for (let T = 1 + a.length; T < r.shape.length; ++T)
     u.push([0, 0]);
-  let l = LC.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = S.getReshaped(l.shape, a, o, false), p = S.getPermuted(c.length, a.length, false), d = S.getReshapedPermuted(l.shape, a, o, false), m = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = mt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
+  let l = GC.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = S.getReshaped(l.shape, a, o, false), p = S.getPermuted(c.length, a.length, false), d = S.getReshapedPermuted(l.shape, a, o, false), m = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = mt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(y), k;
 }
-var Aj = { kernelName: Mo, backendName: "cpu", kernelFunc: _j };
-function Ej(e) {
+var Bj = { kernelName: Lo, backendName: "cpu", kernelFunc: Lj };
+function Vj(e) {
   let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
   if (a.shape.length !== 1)
     throw new Error(`Dense shape must be a vector, saw:
@@ -22587,11 +22645,11 @@ function Ej(e) {
   if (i.shape.length !== 0)
     throw new Error(`Default value must be a scalar, saw:
         ${i.shape}`);
-  let o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = n.data.get(i.dataId).values[0], [p, d, h, f, m] = pC(o, s.shape, s.dtype, u, r.dtype, l, c);
+  let o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = n.data.get(i.dataId).values[0], [p, d, h, f, m] = bC(o, s.shape, s.dtype, u, r.dtype, l, c);
   return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
 }
-var Rj = { kernelName: sp, backendName: "cpu", kernelFunc: Ej };
-function Dj(e) {
+var Wj = { kernelName: ap, backendName: "cpu", kernelFunc: Vj };
+function Uj(e) {
   let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
   if (s.shape.length !== 2)
     throw new Error(`Input indices should be a matrix but received shape
@@ -22601,11 +22659,11 @@ function Dj(e) {
         ${r.shape}`);
   if (a.shape.length !== 1)
     throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
-  let i = Array.from(n.data.get(r.dataId).values), o = n.data.get(s.dataId).values, u = Array.from(n.data.get(a.dataId).values), [l, c, p] = hC(o, s.shape, s.dtype, i, u);
+  let i = Array.from(n.data.get(r.dataId).values), o = n.data.get(s.dataId).values, u = Array.from(n.data.get(a.dataId).values), [l, c, p] = yC(o, s.shape, s.dtype, i, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var Fj = { kernelName: _l, backendName: "cpu", kernelFunc: Dj };
-function Oj(e) {
+var Gj = { kernelName: _l, backendName: "cpu", kernelFunc: Uj };
+function Hj(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -22617,11 +22675,11 @@ function Oj(e) {
           ${a.shape}`);
   if (r.shape[0] !== a.shape[0])
     throw new Error("segmentIds and indices should have same size.");
-  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = av(i, s.shape, s.dtype, o, u, true);
+  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = ov(i, s.shape, s.dtype, o, u, true);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var Pj = { kernelName: rp, backendName: "cpu", kernelFunc: Oj };
-function zj(e) {
+var qj = { kernelName: ip, backendName: "cpu", kernelFunc: Hj };
+function jj(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -22633,26 +22691,26 @@ function zj(e) {
          ${a.shape}`);
   if (r.shape[0] !== a.shape[0])
     throw new Error("segmentIds and indices should have same size.");
-  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = av(i, s.shape, s.dtype, o, u);
+  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = ov(i, s.shape, s.dtype, o, u);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var Mj = { kernelName: ap, backendName: "cpu", kernelFunc: zj };
-function Lj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = S.calculateShapes(a, r, o), h = false, f = n.bufferSync(r), m = n.bufferSync(a), g = n.data.get(i.dataId).values[0], b = BC(f, m, o, d, c, l, u, p, g, h);
+var Kj = { kernelName: op, backendName: "cpu", kernelFunc: jj };
+function Xj(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = S.calculateShapes(a, r, o), h = false, f = n.bufferSync(r), m = n.bufferSync(a), g = n.data.get(i.dataId).values[0], b = HC(f, m, o, d, c, l, u, p, g, h);
   return n.makeTensorInfo(o, b.dtype, b.values);
 }
-var Bj = { kernelName: ip, backendName: "cpu", kernelFunc: Lj };
-function Vj(e) {
+var Yj = { kernelName: up, backendName: "cpu", kernelFunc: Xj };
+function Qj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = S.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
   return u.map((p) => {
     let d = [...c];
     d[o] = p;
-    let h = ga({ inputs: { x: r }, backend: n, attrs: { begin: l, size: d } });
+    let h = ba({ inputs: { x: r }, backend: n, attrs: { begin: l, size: d } });
     return l[o] += p, h;
   });
 }
-var Wj = { kernelName: Lo, backendName: "cpu", kernelFunc: Vj };
-var Uj = { kernelName: Al, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
+var Zj = { kernelName: Bo, backendName: "cpu", kernelFunc: Qj };
+var Jj = { kernelName: Al, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
   let { x: n } = e, s = t;
   be(n, "square");
   let r = s.data.get(n.dataId).values, a = new Float32Array(r.length);
@@ -22662,12 +22720,12 @@ var Uj = { kernelName: Al, backendName: "cpu", kernelFunc: ({ inputs: e, backend
   }
   return { dataId: s.write(a, n.shape, n.dtype), shape: n.shape, dtype: n.dtype };
 } };
-var Gj = st(hi, (e, t) => {
+var e5 = st(gi, (e, t) => {
   let n = t;
   return isNaN(e) ? NaN : e > 0 ? 1 : n.alpha;
 });
-var Hj = { kernelName: hi, backendName: "cpu", kernelFunc: Gj };
-function qj(e) {
+var t5 = { kernelName: gi, backendName: "cpu", kernelFunc: e5 };
+function n5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s;
   be(r, "stridedSlice");
   let { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
@@ -22675,21 +22733,21 @@ function qj(e) {
     k = mt({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let T = wt.computeOutShape(y, v, x), N = ga({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
+    let T = wt.computeOutShape(y, v, x), N = ba({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
     k = mt({ inputs: { x: N }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(N);
   } else {
-    let T = n.bufferSync(r), N = mC(h, T, x, y);
+    let T = n.bufferSync(r), N = xC(h, T, x, y);
     k = n.makeTensorInfo(f, N.dtype, N.values);
   }
   return k;
 }
-var jj = { kernelName: Bo, backendName: "cpu", kernelFunc: qj };
-function Kj(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.data.get(c.dataId).values, h = n.data.get(p.dataId).values, [f, m] = gC(d, h, r, a, i, o, u, l);
+var s5 = { kernelName: Vo, backendName: "cpu", kernelFunc: n5 };
+function r5(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.data.get(c.dataId).values, h = n.data.get(p.dataId).values, [f, m] = wC(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var Xj = { kernelName: op, backendName: "cpu", kernelFunc: Kj };
-function Yj(e) {
+var a5 = { kernelName: lp, backendName: "cpu", kernelFunc: r5 };
+function i5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
@@ -22697,39 +22755,39 @@ function Yj(e) {
     throw new Error(`Input must be a vector, got shape: ${a.shape}`);
   if (i.shape.length !== 0)
     throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);
-  let o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values[0], [l, c, p] = bC(o, u, r), d = c.length;
+  let o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values[0], [l, c, p] = kC(o, u, r), d = c.length;
   return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
 }
-var Qj = { kernelName: $g, backendName: "cpu", kernelFunc: Yj };
-function Zj(e) {
+var o5 = { kernelName: Ag, backendName: "cpu", kernelFunc: i5 };
+function u5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
   if (r <= 0)
     throw new Error("Number of buckets must be at least 1");
-  let i = n.data.get(a.dataId).values, o = yC(i, r);
+  let i = n.data.get(a.dataId).values, o = IC(i, r);
   return n.makeTensorInfo(a.shape, "int32", o);
 }
-var Jj = { kernelName: _g, backendName: "cpu", kernelFunc: Zj };
-var e5 = st(Vo, (e) => Math.tan(e));
-var t5 = { kernelName: Vo, backendName: "cpu", kernelFunc: e5 };
-var n5 = st(di, (e) => Math.tanh(e));
-var s5 = { kernelName: di, backendName: "cpu", kernelFunc: n5 };
-function r5(e) {
+var l5 = { kernelName: Eg, backendName: "cpu", kernelFunc: u5 };
+var c5 = st(Wo, (e) => Math.tan(e));
+var d5 = { kernelName: Wo, backendName: "cpu", kernelFunc: c5 };
+var p5 = st(fi, (e) => Math.tanh(e));
+var h5 = { kernelName: fi, backendName: "cpu", kernelFunc: p5 };
+function f5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
   be(r, "tile");
-  let i = xC(n.bufferSync(r), a);
+  let i = CC(n.bufferSync(r), a);
   return n.makeTensorInfo(i.shape, i.dtype, i.values);
 }
-var a5 = { kernelName: Cr, backendName: "cpu", kernelFunc: r5 };
-function i5(e) {
+var m5 = { kernelName: Nr, backendName: "cpu", kernelFunc: f5 };
+function g5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s;
   be(r, "topk");
-  let o = n.data.get(r.dataId).values, [u, l] = kC(o, r.shape, r.dtype, a, i);
+  let o = n.data.get(r.dataId).values, [u, l] = TC(o, r.shape, r.dtype, a, i);
   return [n.makeTensorInfo(u.shape, u.dtype, u.values), n.makeTensorInfo(l.shape, l.dtype, l.values)];
 }
-var o5 = { kernelName: Wo, backendName: "cpu", kernelFunc: i5 };
-function u5(e) {
+var b5 = { kernelName: Uo, backendName: "cpu", kernelFunc: g5 };
+function y5(e) {
   let { inputs: t, attrs: n, backend: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = n, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = w.computeStrides(r.shape), y = b[0], v = b[1], x = b[2], k = w.getTypedArrayFromDType(r.dtype, w.sizeFromShape(g));
   k.fill(u);
   let T = s.data.get(r.dataId).values, N = s.data.get(a.dataId).values;
@@ -22741,13 +22799,13 @@ function u5(e) {
           let z, W = P[6] * F + P[7] * R + 1;
           if (W === 0)
             continue;
-          let q = (P[0] * F + P[1] * R + P[2]) / W, K = (P[3] * F + P[4] * R + P[5]) / W, Y = sw(q, d, o), Z = sw(K, p, o);
+          let q = (P[0] * F + P[1] * R + P[2]) / W, K = (P[3] * F + P[4] * R + P[5]) / W, Y = rw(q, d, o), Z = rw(K, p, o);
           switch (i) {
             case "nearest":
-              z = f5(T, p, d, y, v, x, A, Z, Y, $, u);
+              z = S5(T, p, d, y, v, x, A, Z, Y, $, u);
               break;
             case "bilinear":
-              z = m5(T, p, d, y, v, x, A, Z, Y, $, u);
+              z = C5(T, p, d, y, v, x, A, Z, Y, $, u);
               break;
             default:
               throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`);
@@ -22759,21 +22817,21 @@ function u5(e) {
   }
   return { dataId: s.write(k, g, r.dtype), shape: r.shape, dtype: r.dtype };
 }
-var l5 = { kernelName: Uo, backendName: "cpu", kernelFunc: u5 };
-function sw(e, t, n) {
+var v5 = { kernelName: Go, backendName: "cpu", kernelFunc: y5 };
+function rw(e, t, n) {
   switch (n) {
     case "reflect":
-      return c5(e, t);
+      return x5(e, t);
     case "wrap":
-      return d5(e, t);
+      return w5(e, t);
     case "nearest":
-      return h5(e, t);
+      return I5(e, t);
     case "constant":
     default:
-      return p5(e, t);
+      return k5(e, t);
   }
 }
-function c5(e, t) {
+function x5(e, t) {
   let n = e;
   if (n < 0)
     if (t <= 1)
@@ -22791,7 +22849,7 @@ function c5(e, t) {
     }
   return w.clamp(0, n, t - 1);
 }
-function d5(e, t) {
+function w5(e, t) {
   let n = e;
   if (n < 0)
     if (t <= 1)
@@ -22809,32 +22867,32 @@ function d5(e, t) {
     }
   return w.clamp(0, n, t - 1);
 }
-function p5(e, t) {
+function k5(e, t) {
   return e;
 }
-function h5(e, t) {
+function I5(e, t) {
   return w.clamp(0, e, t - 1);
 }
-function Du(e, t, n, s, r, a, i, o, u, l, c) {
+function Fu(e, t, n, s, r, a, i, o, u, l, c) {
   let p = i * s + o * r + u * a + l;
   return 0 <= o && o < t && 0 <= u && u < n ? e[p] : c;
 }
-function f5(e, t, n, s, r, a, i, o, u, l, c) {
+function S5(e, t, n, s, r, a, i, o, u, l, c) {
   let p = Math.round(o), d = Math.round(u);
-  return Du(e, t, n, s, r, a, i, p, d, l, c);
+  return Fu(e, t, n, s, r, a, i, p, d, l, c);
 }
-function m5(e, t, n, s, r, a, i, o, u, l, c) {
-  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Du(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Du(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Du(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Du(e, t, n, s, r, a, i, h, f, l, c);
+function C5(e, t, n, s, r, a, i, o, u, l, c) {
+  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Fu(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Fu(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Fu(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Fu(e, t, n, s, r, a, i, h, f, l, c);
   return (h - o) * m + (o - p) * g;
 }
-function g5(e) {
+function N5(e) {
   let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
   be(a, "unique");
-  let i = s.data.get(a.dataId).values, { outputValues: o, outputShape: u, indices: l } = IC(i, r, a.shape, a.dtype);
+  let i = s.data.get(a.dataId).values, { outputValues: o, outputShape: u, indices: l } = $C(i, r, a.shape, a.dtype);
   return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
 }
-var b5 = { kernelName: Ag, backendName: "cpu", kernelFunc: g5 };
-function y5(e) {
+var T5 = { kernelName: Rg, backendName: "cpu", kernelFunc: N5 };
+function $5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
   let i = r.shape.length, o = r.shape[a], u = new Array(i - 1), l = 0;
@@ -22845,100 +22903,100 @@ function y5(e) {
   let d = new Array(o);
   for (let h = 0; h < d.length; h++) {
     c[a] = h;
-    let f = ga({ inputs: { x: r }, backend: n, attrs: { begin: c, size: p } });
+    let f = ba({ inputs: { x: r }, backend: n, attrs: { begin: c, size: p } });
     d[h] = mt({ inputs: { x: f }, backend: n, attrs: { shape: u } }), n.disposeIntermediateTensorInfo(f);
   }
   return d;
 }
-var v5 = { kernelName: Go, backendName: "cpu", kernelFunc: y5 };
-function x5(e) {
+var _5 = { kernelName: Ho, backendName: "cpu", kernelFunc: $5 };
+function A5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s;
   be(r, "unsortedSegmentSum");
   let o = r.shape.length, u = a.shape.length, l = [], c = [], p = o - u, d = a;
   for (let f = 0; f < p; ++f) {
-    let m = Od({ inputs: { input: d }, backend: n, attrs: { dim: f + 1 } });
+    let m = zd({ inputs: { input: d }, backend: n, attrs: { dim: f + 1 } });
     d = m, c.push(m);
   }
   for (let f = 0; f < i; ++f) {
-    let m = w.createScalarValue(f, "int32"), g = n.makeTensorInfo([], "int32", m), b = H0({ inputs: { a: g, b: d }, backend: n }), y = xr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = Hp({ inputs: { a: y, b: r }, backend: n }), x = Xl({ inputs: { x: v }, backend: n, attrs: { axis: 0, keepDims: false } });
+    let m = w.createScalarValue(f, "int32"), g = n.makeTensorInfo([], "int32", m), b = Y0({ inputs: { a: g, b: d }, backend: n }), y = wr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = jp({ inputs: { a: y, b: r }, backend: n }), x = Xl({ inputs: { x: v }, backend: n, attrs: { axis: 0, keepDims: false } });
     l.push(x), c.push(g), c.push(b), c.push(y), c.push(v), c.push(x);
   }
-  let h = MC({ inputs: l, backend: n, attrs: { axis: 0 } });
+  let h = UC({ inputs: l, backend: n, attrs: { axis: 0 } });
   return c.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
 }
-var w5 = { kernelName: up, backendName: "cpu", kernelFunc: x5 };
-var k5 = [TG, xU, _G, EG, NU, DG, OG, zG, LG, VG, UG, HG, jG, YG, ZG, tH, sH, aH, oH, CG, lH, dH, hH, mH, SU, $U, bH, wU, vH, wH, kH, SH, NH, $H, AH, RH, FH, PH, MH, BH, WH, GH, qH, jH, XH, QH, JH, e6, t6, n6, a6, yG, o6, _U, m6, AU, g6, RU, k6, I6, C6, FU, $6, A6, R6, F6, P6, PU, MU, kU, M6, xH, B6, W6, G6, vG, BU, WU, q6, GU, K6, Q6, J6, nq, rq, iq, oq, qU, lq, dq, hq, mq, bq, vq, wq, KU, Iq, Nq, Aq, YU, ZU, Dq, Pq, Lq, eG, Vq, Uq, Gq, LC, Kq, wG, sG, Yq, IU, Mm, Zq, kG, IG, SG, ej, nj, rj, ij, uj, lj, dj, aG, hj, mj, vj, oG, wj, Ij, Cj, uG, $q, $j, Aj, Rj, Fj, Pj, Mj, Bj, Wj, dG, Uj, hG, Hj, jj, Xj, Qj, Jj, bG, s6, t5, s5, a5, o5, l5, tG, b5, v5, w5, Wq];
-for (let e of k5)
+var E5 = { kernelName: cp, backendName: "cpu", kernelFunc: A5 };
+var R5 = [zG, AU, LG, VG, PU, UG, HG, jG, XG, QG, JG, tH, sH, iH, uH, dH, hH, mH, bH, OG, vH, wH, IH, CH, FU, MU, TH, EU, _H, EH, RH, FH, PH, MH, BH, WH, GH, qH, KH, YH, ZH, e6, n6, s6, a6, o6, l6, c6, d6, p6, m6, $G, b6, LU, C6, BU, N6, WU, R6, D6, O6, GU, M6, B6, W6, G6, q6, qU, KU, RU, K6, AH, Y6, Z6, eq, _G, YU, ZU, nq, eG, rq, oq, lq, pq, fq, gq, bq, nG, vq, wq, Iq, Cq, Tq, _q, Eq, rG, Dq, Pq, Bq, iG, uG, Uq, qq, Xq, cG, Qq, Jq, ej, GC, rj, EG, hG, ij, DU, Bm, uj, RG, DG, FG, cj, pj, fj, gj, yj, vj, wj, mG, Ij, Cj, _j, bG, Ej, Dj, Oj, yG, Mq, Mj, Bj, Wj, Gj, qj, Kj, Yj, Zj, wG, Jj, IG, t5, s5, a5, o5, l5, TG, h6, d5, h5, m5, b5, v5, dG, T5, _5, E5, Zq];
+for (let e of R5)
   El(e);
-var I5 = {};
-Ae(I5, { assertNotComplex: () => nu, bindCanvasToFramebuffer: () => O5, bindColorTextureToFramebuffer: () => ad, bindTextureToProgramUniformSampler: () => t1, bindTextureUnit: () => ZC, bindVertexBufferToProgramAttribute: () => Bm, callAndCheck: () => fe, canBeRepresented: () => VC, createFragmentShader: () => GC, createFramebuffer: () => QC, createProgram: () => HC, createStaticIndexBuffer: () => KC, createStaticVertexBuffer: () => jC, createTexture: () => XC, createVertexShader: () => UC, getBatchDim: () => ba, getExtensionOrThrow: () => Fu, getFramebufferErrorMessage: () => n1, getMaxTexturesInShader: () => i1, getNumChannels: () => D5, getProgramUniformLocation: () => e1, getProgramUniformLocationOrThrow: () => JC, getRowsCols: () => ya, getShapeAs3D: () => id, getTextureShapeFromLogicalShape: () => r1, getWebGLDisjointQueryTimerVersion: () => o1, getWebGLErrorMessage: () => WC, getWebGLMaxTextureSize: () => a1, hasExtension: () => Ln, isCapableOfRenderingToFloatTexture: () => u1, isDownloadFloatTextureEnabled: () => l1, isReshapeFree: () => el, isWebGLFenceEnabled: () => c1, isWebGLVersionEnabled: () => Wm, linkProgram: () => qC, logShaderSourceAndInfoLog: () => pv, resetMaxTextureSize: () => P5, resetMaxTexturesInShader: () => z5, unbindColorTextureFromFramebuffer: () => Vm, unbindTextureUnit: () => F5, validateFramebuffer: () => Ou, validateProgram: () => rd, validateTextureSize: () => YC });
-var Xr = {};
-var jf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
-function S5(e, t) {
-  Xr[e] = t;
+var D5 = {};
+Ae(D5, { assertNotComplex: () => su, bindCanvasToFramebuffer: () => H5, bindColorTextureToFramebuffer: () => ad, bindTextureToProgramUniformSampler: () => i1, bindTextureUnit: () => s1, bindVertexBufferToProgramAttribute: () => Wm, callAndCheck: () => fe, canBeRepresented: () => qC, createFragmentShader: () => XC, createFramebuffer: () => n1, createProgram: () => YC, createStaticIndexBuffer: () => JC, createStaticVertexBuffer: () => ZC, createTexture: () => e1, createVertexShader: () => KC, getBatchDim: () => ya, getExtensionOrThrow: () => Ou, getFramebufferErrorMessage: () => o1, getMaxTexturesInShader: () => d1, getNumChannels: () => U5, getProgramUniformLocation: () => a1, getProgramUniformLocationOrThrow: () => r1, getRowsCols: () => va, getShapeAs3D: () => id, getTextureShapeFromLogicalShape: () => l1, getWebGLDisjointQueryTimerVersion: () => p1, getWebGLErrorMessage: () => jC, getWebGLMaxTextureSize: () => c1, hasExtension: () => Ln, isCapableOfRenderingToFloatTexture: () => h1, isDownloadFloatTextureEnabled: () => f1, isReshapeFree: () => tl, isWebGLFenceEnabled: () => m1, isWebGLVersionEnabled: () => Gm, linkProgram: () => QC, logShaderSourceAndInfoLog: () => fv, resetMaxTextureSize: () => q5, resetMaxTexturesInShader: () => j5, unbindColorTextureFromFramebuffer: () => Um, unbindTextureUnit: () => G5, validateFramebuffer: () => Pu, validateProgram: () => rd, validateTextureSize: () => t1 });
+var Yr = {};
+var Kf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
+function F5(e, t) {
+  Yr[e] = t;
 }
 function vs(e, t) {
-  if (!(e in Xr) || t != null) {
-    let s = N5(e, t);
+  if (!(e in Yr) || t != null) {
+    let s = P5(e, t);
     if (s !== null)
-      Xr[e] = s;
+      Yr[e] = s;
     else
       return console.log("Could not get context for WebGL version", e), null;
   }
-  let n = Xr[e];
-  return n == null || n.isContextLost() ? (delete Xr[e], vs(e)) : (n.disable(n.DEPTH_TEST), n.disable(n.STENCIL_TEST), n.disable(n.BLEND), n.disable(n.DITHER), n.disable(n.POLYGON_OFFSET_FILL), n.disable(n.SAMPLE_COVERAGE), n.enable(n.SCISSOR_TEST), n.enable(n.CULL_FACE), n.cullFace(n.BACK), Xr[e]);
+  let n = Yr[e];
+  return n == null || n.isContextLost() ? (delete Yr[e], vs(e)) : (n.disable(n.DEPTH_TEST), n.disable(n.STENCIL_TEST), n.disable(n.BLEND), n.disable(n.DITHER), n.disable(n.POLYGON_OFFSET_FILL), n.disable(n.SAMPLE_COVERAGE), n.enable(n.SCISSOR_TEST), n.enable(n.CULL_FACE), n.cullFace(n.BACK), Yr[e]);
 }
-function C5(e) {
+function O5(e) {
   if (typeof OffscreenCanvas != "undefined" && e === 2)
     return new OffscreenCanvas(300, 150);
   if (typeof document != "undefined")
     return document.createElement("canvas");
   throw new Error("Cannot create a canvas in this context");
 }
-function N5(e, t) {
+function P5(e, t) {
   if (e !== 1 && e !== 2)
     throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
-  let n = t == null ? C5(e) : t;
+  let n = t == null ? O5(e) : t;
   return n.addEventListener("webglcontextlost", (s) => {
-    s.preventDefault(), delete Xr[e];
-  }, false), e === 1 ? n.getContext("webgl", jf) || n.getContext("experimental-webgl", jf) : n.getContext("webgl2", jf);
+    s.preventDefault(), delete Yr[e];
+  }, false), e === 1 ? n.getContext("webgl", Kf) || n.getContext("experimental-webgl", Kf) : n.getContext("webgl2", Kf);
 }
 function Yl(e, t) {
   return [t, e];
 }
-function T5(e, t) {
+function z5(e, t) {
   return e * t;
 }
 function Yc(e) {
   let t = w.sizeFromShape(e), n = Math.ceil(t / 4);
   return w.sizeToSquarishShape(n);
 }
-function tu(e, t) {
+function nu(e, t) {
   return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];
 }
-function $5(e, t) {
-  let [n, s] = tu(e, t);
+function M5(e, t) {
+  let [n, s] = nu(e, t);
   return n * s * 4;
 }
-function dv(e, t) {
+function hv(e, t) {
   let n = e, s, r, a, i, o, u, l, c, p, d;
   return X().getNumber("WEBGL_VERSION") === 2 ? (s = n.R32F, r = n.R16F, a = n.RGBA16F, i = n.RGBA32F, o = n.RED, l = 4, c = 1, p = n.HALF_FLOAT, d = n.FLOAT, u = n.RGBA8) : (s = e.RGBA, r = e.RGBA, a = e.RGBA, i = n.RGBA, o = e.RGBA, l = 4, c = 4, p = t != null ? t.HALF_FLOAT_OES : null, d = e.FLOAT, u = e.RGBA), { internalFormatFloat: s, internalFormatHalfFloat: r, internalFormatPackedHalfFloat: a, internalFormatPackedFloat: i, textureFormatFloat: o, downloadTextureFormat: u, downloadUnpackNumChannels: l, defaultNumChannels: c, textureTypeHalfFloat: p, textureTypeFloat: d };
 }
 function fe(e, t) {
   let n = t();
-  return X().getBool("DEBUG") && _5(e), n;
+  return X().getBool("DEBUG") && L5(e), n;
 }
-function _5(e) {
+function L5(e) {
   let t = e.getError();
   if (t !== e.NO_ERROR)
-    throw new Error("WebGL Error: " + WC(e, t));
+    throw new Error("WebGL Error: " + jC(e, t));
 }
-var A5 = 596e-10;
-var E5 = 65504;
-function VC(e) {
-  return !!(X().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || e === 0 || A5 < Math.abs(e) && Math.abs(e) < E5);
+var B5 = 596e-10;
+var V5 = 65504;
+function qC(e) {
+  return !!(X().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || e === 0 || B5 < Math.abs(e) && Math.abs(e) < V5);
 }
-function WC(e, t) {
+function jC(e, t) {
   switch (t) {
     case e.NO_ERROR:
       return "NO_ERROR";
@@ -22958,26 +23016,26 @@ function WC(e, t) {
       return `Unknown error code ${t}`;
   }
 }
-function Fu(e, t) {
-  return Qs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
+function Ou(e, t) {
+  return Zs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
 }
-function UC(e, t) {
-  let n = Qs(e, () => e.createShader(e.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
+function KC(e, t) {
+  let n = Zs(e, () => e.createShader(e.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
   if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), e.getShaderParameter(n, e.COMPILE_STATUS) === false)
     throw console.log(e.getShaderInfoLog(n)), new Error("Failed to compile vertex shader.");
   return n;
 }
-function GC(e, t) {
-  let n = Qs(e, () => e.createShader(e.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
+function XC(e, t) {
+  let n = Zs(e, () => e.createShader(e.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
   if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), X().get("ENGINE_COMPILE_ONLY"))
     return n;
   if (e.getShaderParameter(n, e.COMPILE_STATUS) === false)
-    throw pv(t, e.getShaderInfoLog(n)), new Error("Failed to compile fragment shader.");
+    throw fv(t, e.getShaderInfoLog(n)), new Error("Failed to compile fragment shader.");
   return n;
 }
-var R5 = /ERROR: [0-9]+:([0-9]+):/g;
-function pv(e, t) {
-  let n = R5.exec(t);
+var W5 = /ERROR: [0-9]+:([0-9]+):/g;
+function fv(e, t) {
+  let n = W5.exec(t);
   if (n == null) {
     console.log(`Couldn't parse line number in error: ${t}`), console.log(e);
     return;
@@ -22992,10 +23050,10 @@ function pv(e, t) {
 `)[0]), console.log(`%c ${w.rightPad(l[0], o)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
 `));
 }
-function HC(e) {
-  return Qs(e, () => e.createProgram(), "Unable to create WebGLProgram.");
+function YC(e) {
+  return Zs(e, () => e.createProgram(), "Unable to create WebGLProgram.");
 }
-function qC(e, t) {
+function QC(e, t) {
   if (fe(e, () => e.linkProgram(t)), !X().get("ENGINE_COMPILE_ONLY") && e.getProgramParameter(t, e.LINK_STATUS) === false)
     throw console.log(e.getProgramInfoLog(t)), new Error("Failed to link vertex and fragment shaders.");
 }
@@ -23003,21 +23061,21 @@ function rd(e, t) {
   if (fe(e, () => e.validateProgram(t)), e.getProgramParameter(t, e.VALIDATE_STATUS) === false)
     throw console.log(e.getProgramInfoLog(t)), new Error("Shader program validation failed.");
 }
-function jC(e, t) {
-  let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
+function ZC(e, t) {
+  let n = Zs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
   return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
 }
-function KC(e, t) {
-  let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
+function JC(e, t) {
+  let n = Zs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
   return fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ELEMENT_ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
 }
-function D5() {
+function U5() {
   return X().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
 }
-function XC(e) {
-  return Qs(e, () => e.createTexture(), "Unable to create WebGLTexture.");
+function e1(e) {
+  return Zs(e, () => e.createTexture(), "Unable to create WebGLTexture.");
 }
-function YC(e, t) {
+function t1(e, t) {
   let n = X().getNumber("WEBGL_MAX_TEXTURE_SIZE");
   if (e <= 0 || t <= 0) {
     let s = `[${e}x${t}]`;
@@ -23028,43 +23086,43 @@ function YC(e, t) {
     throw new Error("Requested texture size " + s + " greater than WebGL maximum on this browser / GPU " + r + ".");
   }
 }
-function QC(e) {
-  return Qs(e, () => e.createFramebuffer(), "Unable to create WebGLFramebuffer.");
+function n1(e) {
+  return Zs(e, () => e.createFramebuffer(), "Unable to create WebGLFramebuffer.");
 }
-function Bm(e, t, n, s, r, a, i) {
+function Wm(e, t, n, s, r, a, i) {
   let o = e.getAttribLocation(t, n);
   return o === -1 ? false : (fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, s)), fe(e, () => e.vertexAttribPointer(o, r, e.FLOAT, false, a, i)), fe(e, () => e.enableVertexAttribArray(o)), true);
 }
-function ZC(e, t, n) {
-  s1(e, n), fe(e, () => e.activeTexture(e.TEXTURE0 + n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
+function s1(e, t, n) {
+  u1(e, n), fe(e, () => e.activeTexture(e.TEXTURE0 + n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
 }
-function F5(e, t) {
-  s1(e, t), fe(e, () => e.activeTexture(e.TEXTURE0 + t)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
+function G5(e, t) {
+  u1(e, t), fe(e, () => e.activeTexture(e.TEXTURE0 + t)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
 }
-function JC(e, t, n) {
-  return Qs(e, () => e.getUniformLocation(t, n), 'uniform "' + n + '" not present in program.');
+function r1(e, t, n) {
+  return Zs(e, () => e.getUniformLocation(t, n), 'uniform "' + n + '" not present in program.');
 }
-function e1(e, t, n) {
+function a1(e, t, n) {
   return e.getUniformLocation(t, n);
 }
-function t1(e, t, n, s) {
-  fe(e, () => ZC(e, t, s)), fe(e, () => e.uniform1i(n, s));
+function i1(e, t, n, s) {
+  fe(e, () => s1(e, t, s)), fe(e, () => e.uniform1i(n, s));
 }
-function O5(e) {
+function H5(e) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), fe(e, () => e.viewport(0, 0, e.canvas.width, e.canvas.height)), fe(e, () => e.scissor(0, 0, e.canvas.width, e.canvas.height));
 }
 function ad(e, t, n) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, n)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, t, 0));
 }
-function Vm(e, t) {
+function Um(e, t) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, t)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0));
 }
-function Ou(e) {
+function Pu(e) {
   let t = e.checkFramebufferStatus(e.FRAMEBUFFER);
   if (t !== e.FRAMEBUFFER_COMPLETE)
-    throw new Error("Error binding framebuffer: " + n1(e, t));
+    throw new Error("Error binding framebuffer: " + o1(e, t));
 }
-function n1(e, t) {
+function o1(e, t) {
   switch (t) {
     case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
       return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
@@ -23078,32 +23136,32 @@ function n1(e, t) {
       return `unknown error ${t}`;
   }
 }
-function Qs(e, t, n) {
+function Zs(e, t, n) {
   let s = fe(e, () => t());
   if (s == null)
     throw new Error(n);
   return s;
 }
-function s1(e, t) {
+function u1(e, t) {
   let n = e.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, s = t + e.TEXTURE0;
   if (s < e.TEXTURE0 || s > n) {
     let r = `[gl.TEXTURE0, gl.TEXTURE${n}]`;
     throw new Error(`textureUnit must be in ${r}.`);
   }
 }
-function ba(e, t = 2) {
+function ya(e, t = 2) {
   return w.sizeFromShape(e.slice(0, e.length - t));
 }
-function ya(e) {
+function va(e) {
   if (e.length === 0)
     throw Error("Cannot get rows and columns of an empty shape array.");
   return [e.length > 1 ? e[e.length - 2] : 1, e[e.length - 1]];
 }
 function id(e) {
   let t = [1, 1, 1];
-  return e.length === 0 || e.length === 1 && e[0] === 1 || (t = [ba(e), ...ya(e)]), t;
+  return e.length === 0 || e.length === 1 && e[0] === 1 || (t = [ya(e), ...va(e)]), t;
 }
-function r1(e, t = false) {
+function l1(e, t = false) {
   let n = X().getNumber("WEBGL_MAX_TEXTURE_SIZE");
   t && (n = n * 2, e = e.map((r, a) => a >= e.length - 2 ? w.nearestLargerEven(e[a]) : e[a]), e.length === 1 && (e = [2, e[0]])), e.length !== 2 && (e = w.squeezeShape(e).newShape);
   let s = w.sizeFromShape(e);
@@ -23120,15 +23178,15 @@ function r1(e, t = false) {
   if (e.length === 4 && e[0] <= n && e[1] * e[2] * e[3] <= n)
     return [e[0], e[1] * e[2] * e[3]];
   if (t) {
-    let r = ba(e), a = 2, i = 2;
-    return e.length && ([a, i] = ya(e)), s = r * (a / 2) * (i / 2), w.sizeToSquarishShape(s).map((o) => o * 2);
+    let r = ya(e), a = 2, i = 2;
+    return e.length && ([a, i] = va(e)), s = r * (a / 2) * (i / 2), w.sizeToSquarishShape(s).map((o) => o * 2);
   }
   return w.sizeToSquarishShape(s);
 }
 function Qc(e) {
   return e % 2 === 0;
 }
-function el(e, t) {
+function tl(e, t) {
   if (e = e.slice(-2), t = t.slice(-2), w.arraysEqual(e, t) || !e.length || !t.length || e[0] === 0 || e[1] === 0 || t[0] === 0 || t[1] === 0)
     return true;
   if (e.length !== t.length) {
@@ -23140,27 +23198,27 @@ function el(e, t) {
 }
 var od;
 var ud;
-function a1(e) {
+function c1(e) {
   if (od == null) {
     let t = vs(e);
     od = t.getParameter(t.MAX_TEXTURE_SIZE);
   }
   return od;
 }
-function P5() {
+function q5() {
   od = null;
 }
-function z5() {
+function j5() {
   ud = null;
 }
-function i1(e) {
+function d1(e) {
   if (ud == null) {
     let t = vs(e);
     ud = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
   }
   return Math.min(16, ud);
 }
-function o1(e) {
+function p1(e) {
   if (e === 0)
     return 0;
   let t, n = vs(e);
@@ -23169,7 +23227,7 @@ function o1(e) {
 function Ln(e, t) {
   return e.getExtension(t) != null;
 }
-function Wm(e) {
+function Gm(e) {
   try {
     if (vs(e) != null)
       return true;
@@ -23178,7 +23236,7 @@ function Wm(e) {
   }
   return false;
 }
-function u1(e) {
+function h1(e) {
   if (e === 0)
     return false;
   let t = vs(e);
@@ -23187,9 +23245,9 @@ function u1(e) {
       return false;
   } else if (!Ln(t, "EXT_color_buffer_float"))
     return false;
-  return Um(t);
+  return Hm(t);
 }
-function l1(e) {
+function f1(e) {
   if (e === 0)
     return false;
   let t = vs(e);
@@ -23198,18 +23256,18 @@ function l1(e) {
       return false;
   } else {
     if (Ln(t, "EXT_color_buffer_float"))
-      return Um(t);
+      return Hm(t);
     let s = "EXT_color_buffer_half_float";
     if (Ln(t, s)) {
       let r = t.getExtension(s);
-      return M5(t, r);
+      return K5(t, r);
     }
     return false;
   }
-  return Um(t);
+  return Hm(t);
 }
-function Um(e) {
-  let t = dv(e), n = e.createTexture();
+function Hm(e) {
+  let t = hv(e), n = e.createTexture();
   e.bindTexture(e.TEXTURE_2D, n);
   let s = 1, r = 1;
   e.texImage2D(e.TEXTURE_2D, 0, t.internalFormatFloat, s, r, 0, t.textureFormatFloat, t.textureTypeFloat, null);
@@ -23218,8 +23276,8 @@ function Um(e) {
   let i = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
   return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(n), e.deleteFramebuffer(a), i;
 }
-function M5(e, t) {
-  let n = dv(e, t), s = e.createTexture();
+function K5(e, t) {
+  let n = hv(e, t), s = e.createTexture();
   e.bindTexture(e.TEXTURE_2D, s);
   let r = 1, a = 1;
   e.texImage2D(e.TEXTURE_2D, 0, n.internalFormatHalfFloat, r, a, 0, n.textureFormatFloat, n.textureTypeHalfFloat, null);
@@ -23228,17 +23286,17 @@ function M5(e, t) {
   let o = e.checkFramebufferStatus(e.FRAMEBUFFER) === e.FRAMEBUFFER_COMPLETE;
   return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(s), e.deleteFramebuffer(i), o;
 }
-function c1(e) {
+function m1(e) {
   return e !== 2 ? false : vs(e).fenceSync != null;
 }
-function nu(e, t) {
+function su(e, t) {
   Array.isArray(e) || (e = [e]), e.forEach((n) => {
     n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the WebGL backend.`);
   });
 }
 var Ne = X();
 Ne.registerFlag("HAS_WEBGL", () => Ne.getNumber("WEBGL_VERSION") > 0);
-Ne.registerFlag("WEBGL_VERSION", () => Wm(2) ? 2 : Wm(1) ? 1 : 0);
+Ne.registerFlag("WEBGL_VERSION", () => Gm(2) ? 2 : Gm(1) ? 1 : 0);
 Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
 Ne.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Ne.get("WEBGL_VERSION") === 2);
 Ne.registerFlag("WEBGL_CPU_FORWARD", () => true);
@@ -23254,23 +23312,23 @@ Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => Ne.getBool("WEBGL_PACK"));
 Ne.registerFlag("WEBGL_PACK_REDUCE", () => Ne.getBool("WEBGL_PACK"));
 Ne.registerFlag("WEBGL_LAZILY_UNPACK", () => Ne.getBool("WEBGL_PACK"));
 Ne.registerFlag("WEBGL_CONV_IM2COL", () => Ne.getBool("WEBGL_PACK"));
-Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => a1(Ne.getNumber("WEBGL_VERSION")));
-Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => i1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => c1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => d1(Ne.getNumber("WEBGL_VERSION")));
 Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
   let e = Ne.getNumber("WEBGL_VERSION");
-  return e === 0 ? 0 : o1(e);
+  return e === 0 ? 0 : p1(e);
 });
-Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !dp.isMobile());
-Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => u1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !hp.isMobile());
+Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => h1(Ne.getNumber("WEBGL_VERSION")));
 Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Ne.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));
-Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => l1(Ne.getNumber("WEBGL_VERSION")));
-Ne.registerFlag("WEBGL_FENCE_API_ENABLED", () => c1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => f1(Ne.getNumber("WEBGL_VERSION")));
+Ne.registerFlag("WEBGL_FENCE_API_ENABLED", () => m1(Ne.getNumber("WEBGL_VERSION")));
 Ne.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => Ne.getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? 4 : 0);
 Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (e) => {
   if (e < 0 && e !== -1)
     throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`);
 });
-Ne.registerFlag("WEBGL_FLUSH_THRESHOLD", () => dp.isMobile() ? 1 : -1, (e) => {
+Ne.registerFlag("WEBGL_FLUSH_THRESHOLD", () => hp.isMobile() ? 1 : -1, (e) => {
   if (e < 0 && e !== -1)
     throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`);
 });
@@ -23328,35 +23386,35 @@ function fn() {
       }
     `), { version: e, attribute: t, varyingVs: n, varyingFs: s, texture2D: r, output: a, defineOutput: i, defineSpecialNaN: o, defineSpecialInf: u, defineRound: l };
 }
-function yi(e, t, n = "index") {
+function wi(e, t, n = "index") {
   let s = w.computeStrides(t);
   return s.map((r, a) => {
     let i = `int ${e[a]} = ${n} / ${r}`, o = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * ${r}` : `index -= ${e[a]} * ${r}`;
     return `${i}; ${o};`;
   }).join("");
 }
-function qp(e, t, n = "index") {
+function Kp(e, t, n = "index") {
   let s = w.computeStrides(t);
   return s.map((r, a) => {
     let i = `int ${e[a]} = ${n} / outShapeStrides[${a}]`, o = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * outShapeStrides[${a}]` : `index -= ${e[a]} * outShapeStrides[${a}]`;
     return `${i}; ${o};`;
   }).join("");
 }
-function L5(e, t) {
+function X5(e, t) {
   let n = e.length, s = e.map((a) => `${t}[${a}]`), r = new Array(n - 1);
   r[n - 2] = s[n - 1];
   for (let a = n - 3; a >= 0; --a)
     r[a] = `(${r[a + 1]} * ${s[a + 1]})`;
   return r;
 }
-function B5(e, t, n = "index") {
-  let s = e.map((a, i) => i), r = L5(s, t);
+function Y5(e, t, n = "index") {
+  let s = e.map((a, i) => i), r = X5(s, t);
   return r.map((a, i) => {
     let o = `int ${e[i]} = ${n} / ${r[i]}`, u = i === r.length - 1 ? `int ${e[i + 1]} = ${n} - ${e[i]} * ${r[i]}` : `index -= ${e[i]} * ${r[i]}`;
     return `${o}; ${u};`;
   }).join("");
 }
-function hv(e) {
+function mv(e) {
   let t = w.computeStrides(e).map((n) => n.toString());
   return `
   int getFlatIndex(ivec3 coords) {
@@ -23364,14 +23422,14 @@ function hv(e) {
   }
 `;
 }
-function fv() {
+function gv() {
   return `
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
 `;
 }
-var d1 = `
+var g1 = `
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -23411,13 +23469,13 @@ var d1 = `
     return c / 255.0;
   }
 `;
-var { getBroadcastDims: p1 } = S;
-function V5(e, t, n) {
+var { getBroadcastDims: b1 } = S;
+function Q5(e, t, n) {
   let s = [];
   if (e.forEach((h) => {
     let f = w.sizeFromShape(h.shapeInfo.logicalShape);
     if (h.shapeInfo.isUniform ? s.push(`uniform float ${h.name}${f > 1 ? `[${f}]` : ""};`) : (s.push(`uniform sampler2D ${h.name};`), s.push(`uniform int offset${h.name};`)), n.enableShapeUniforms) {
-      let { uniformShape: m } = mv(n.packedInputs, h.shapeInfo.logicalShape, h.shapeInfo.texShape);
+      let { uniformShape: m } = bv(n.packedInputs, h.shapeInfo.logicalShape, h.shapeInfo.texShape);
       switch (m.length) {
         case 1:
           s.push(`uniform int ${h.name}Shape;`);
@@ -23459,108 +23517,108 @@ function V5(e, t, n) {
     s.push(`uniform ${h.type} ${h.name}${h.arrayIndex ? `[${h.arrayIndex}]` : ""};`);
   });
   let r = s.join(`
-`), a = e.map((h) => W5(h, t, n.packedInputs, n.enableShapeUniforms)).join(`
-`), i = t.texShape, o = fn(), u = H5(o), l, c, p = K5(o);
-  return t.isPacked ? (l = U5(t.logicalShape, i, n.enableShapeUniforms), c = j5(o)) : (l = G5(t.logicalShape, i, n.enableShapeUniforms), c = q5(o)), n.packedInputs && (p += Z5), [p, u, c, r, l, a, n.userCode].join(`
+`), a = e.map((h) => Z5(h, t, n.packedInputs, n.enableShapeUniforms)).join(`
+`), i = t.texShape, o = fn(), u = tK(o), l, c, p = rK(o);
+  return t.isPacked ? (l = J5(t.logicalShape, i, n.enableShapeUniforms), c = sK(o)) : (l = eK(t.logicalShape, i, n.enableShapeUniforms), c = nK(o)), n.packedInputs && (p += uK), [p, u, c, r, l, a, n.userCode].join(`
 `);
 }
-function su(e, t = false) {
+function ru(e, t = false) {
   let n = e.shapeInfo.logicalShape;
   switch (n.length) {
     case 0:
-      return cK(e, t);
+      return xK(e, t);
     case 1:
-      return pK(e, t);
+      return kK(e, t);
     case 2:
-      return fK(e, t);
+      return SK(e, t);
     case 3:
-      return gK(e, t);
+      return NK(e, t);
     case 4:
-      return yK(e, t);
+      return $K(e, t);
     case 5:
-      return vK(e);
+      return _K(e);
     case 6:
-      return xK(e);
+      return AK(e);
     default:
       throw new Error(`${n.length}-D input sampling is not yet supported`);
   }
 }
-function h1(e, t) {
+function y1(e, t) {
   switch (e.shapeInfo.logicalShape.length) {
     case 0:
-      return lK(e);
+      return vK(e);
     case 1:
-      return dK(e, t);
+      return wK(e, t);
     case 2:
-      return hK(e, t);
+      return IK(e, t);
     case 3:
-      return mK(e, t);
+      return CK(e, t);
     default:
-      return bK(e, t);
+      return TK(e, t);
   }
 }
-function W5(e, t, n = false, s) {
+function Z5(e, t, n = false, s) {
   let r = "";
-  n ? r += h1(e, s) : r += su(e, s);
+  n ? r += y1(e, s) : r += ru(e, s);
   let a = e.shapeInfo.logicalShape, i = t.logicalShape;
-  return a.length <= i.length && (n ? r += wK(e, t) : r += kK(e, t)), r;
+  return a.length <= i.length && (n ? r += EK(e, t) : r += RK(e, t)), r;
 }
-function U5(e, t, n) {
+function J5(e, t, n) {
   switch (e.length) {
     case 0:
-      return f1();
+      return v1();
     case 1:
-      return J5(e, t, n);
+      return lK(e, t, n);
     case 2:
-      return oK(e, t, n);
+      return bK(e, t, n);
     case 3:
-      return tK(e, t, n);
+      return dK(e, t, n);
     default:
-      return sK(e, t, n);
+      return hK(e, t, n);
   }
 }
-function G5(e, t, n) {
+function eK(e, t, n) {
   switch (e.length) {
     case 0:
-      return f1();
+      return v1();
     case 1:
-      return eK(e, t, n);
+      return cK(e, t, n);
     case 2:
-      return uK(e, t, n);
+      return yK(e, t, n);
     case 3:
-      return nK(e, t, n);
+      return pK(e, t, n);
     case 4:
-      return rK(e, t, n);
+      return fK(e, t, n);
     case 5:
-      return aK(e, t);
+      return mK(e, t);
     case 6:
-      return iK(e, t);
+      return gK(e, t);
     default:
       throw new Error(`${e.length}-D output sampling is not yet supported`);
   }
 }
-function H5(e) {
+function tK(e) {
   return `
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
   `;
 }
-function q5(e) {
+function nK(e) {
   return `
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
   `;
 }
-function j5(e) {
+function sK(e) {
   return `
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
   `;
 }
-function K5(e) {
+function rK(e) {
   return `${e.version}
     precision highp float;
     precision highp int;
@@ -23616,12 +23674,12 @@ function K5(e) {
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${X5}
-    ${Y5}
-    ${Q5}
+    ${aK}
+    ${iK}
+    ${oK}
   `;
 }
-var X5 = `
+var aK = `
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -23634,7 +23692,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
 `;
-var Y5 = `
+var iK = `
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -23643,7 +23701,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
 `;
-var Q5 = `
+var oK = `
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -23653,7 +23711,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
 `;
-var Z5 = `
+var uK = `
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -23665,14 +23723,14 @@ var Z5 = `
     return modCoord == 0. ? frag.r : frag.g;
   }
 `;
-function f1() {
+function v1() {
   return `
     int getOutputCoords() {
       return 0;
     }
   `;
 }
-function J5(e, t, n) {
+function lK(e, t, n) {
   let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];
   return s[0] === 1 ? n ? `
       int getOutputCoords() {
@@ -23705,7 +23763,7 @@ function J5(e, t, n) {
     }
   `;
 }
-function eK(e, t, n) {
+function cK(e, t, n) {
   return t[0] === 1 ? n ? `
       int getOutputCoords() {
         return int(resultUV.x * float(outTexShape[1]));
@@ -23736,7 +23794,7 @@ function eK(e, t, n) {
     }
   `;
 }
-function tK(e, t, n) {
+function dK(e, t, n) {
   if (n)
     return `
     ivec3 getOutputCoords() {
@@ -23773,18 +23831,18 @@ function tK(e, t, n) {
     }
   `;
 }
-function nK(e, t, n) {
+function pK(e, t, n) {
   if (n)
     return `
   ivec3 getOutputCoords() {
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${qp(["r", "c", "d"], e)}
+    ${Kp(["r", "c", "d"], e)}
     return ivec3(r, c, d);
   }
 `;
-  let s = yi(["r", "c", "d"], e);
+  let s = wi(["r", "c", "d"], e);
   return `
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -23795,7 +23853,7 @@ function nK(e, t, n) {
     }
   `;
 }
-function sK(e, t, n) {
+function hK(e, t, n) {
   if (n)
     return `
     ivec4 getOutputCoords() {
@@ -23844,18 +23902,18 @@ function sK(e, t, n) {
     }
   `;
 }
-function rK(e, t, n) {
+function fK(e, t, n) {
   if (n)
     return `
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${qp(["r", "c", "d", "d2"], e)}
+      ${Kp(["r", "c", "d", "d2"], e)}
       return ivec4(r, c, d, d2);
     }
   `;
-  let s = yi(["r", "c", "d", "d2"], e);
+  let s = wi(["r", "c", "d", "d2"], e);
   return `
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -23866,8 +23924,8 @@ function rK(e, t, n) {
     }
   `;
 }
-function aK(e, t) {
-  let n = yi(["r", "c", "d", "d2", "d3"], e);
+function mK(e, t) {
+  let n = wi(["r", "c", "d", "d2", "d3"], e);
   return `
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
@@ -23882,8 +23940,8 @@ function aK(e, t) {
     }
   `;
 }
-function iK(e, t) {
-  let n = yi(["r", "c", "d", "d2", "d3", "d4"], e);
+function gK(e, t) {
+  let n = wi(["r", "c", "d", "d2", "d3", "d4"], e);
   return `
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -23897,7 +23955,7 @@ function iK(e, t) {
     }
   `;
 }
-function oK(e, t, n) {
+function bK(e, t, n) {
   let s = [Math.ceil(t[0] / 2), Math.ceil(t[1] / 2)];
   if (w.arraysEqual(e, t))
     return n ? `
@@ -23937,7 +23995,7 @@ function oK(e, t, n) {
     }
   `;
 }
-function uK(e, t, n) {
+function yK(e, t, n) {
   return w.arraysEqual(e, t) ? n ? `
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
@@ -23994,10 +24052,10 @@ function uK(e, t, n) {
     }
   `;
 }
-function vi(e) {
+function ki(e) {
   return `offset${e}`;
 }
-function lK(e) {
+function vK(e) {
   let t = e.name, n = "get" + t.charAt(0).toUpperCase() + t.slice(1), s = fn();
   return `
     vec4 ${n}() {
@@ -24005,7 +24063,7 @@ function lK(e) {
     }
   `;
 }
-function cK(e, t) {
+function xK(e, t) {
   let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
   if (e.shapeInfo.isUniform)
     return `float ${s}() {return ${n};}`;
@@ -24016,7 +24074,7 @@ function cK(e, t) {
         return sampleTexture(${n}, halfCR);
       }
     `;
-  let i = vi(n);
+  let i = ki(n);
   if (t)
     return `
     float ${s}() {
@@ -24032,7 +24090,7 @@ function cK(e, t) {
     }
   `;
 }
-function dK(e, t) {
+function wK(e, t) {
   let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = e.shapeInfo.texShape, a = fn();
   if (t)
     return `
@@ -24052,12 +24110,12 @@ function dK(e, t) {
     }
   `;
 }
-function pK(e, t) {
+function kK(e, t) {
   let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
   if (e.shapeInfo.isUniform)
     return `
       float ${s}(int index) {
-        ${ru(e)}
+        ${au(e)}
       }
     `;
   let r = e.shapeInfo.texShape, a = r[0], i = r[1];
@@ -24067,7 +24125,7 @@ function pK(e, t) {
         return sampleTexture(${n}, halfCR);
       }
     `;
-  let o = vi(n);
+  let o = ki(n);
   return i === 1 ? t ? `
       float ${s}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
@@ -24100,7 +24158,7 @@ function pK(e, t) {
     }
   `;
 }
-function hK(e, t) {
+function IK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = a[0], o = a[1], u = fn();
   if (a != null && w.arraysEqual(n, a))
     return t ? `
@@ -24133,7 +24191,7 @@ function hK(e, t) {
     }
   `;
 }
-function fK(e, t) {
+function SK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape;
   if (a != null && w.arraysEqual(n, a)) {
     if (t)
@@ -24153,11 +24211,11 @@ function fK(e, t) {
   }
   let { newShape: i, keptDims: o } = w.squeezeShape(n), u = i;
   if (u.length < n.length) {
-    let d = au(e, u), h = ["row", "col"];
+    let d = iu(e, u), h = ["row", "col"];
     return `
-      ${su(d, t)}
+      ${ru(d, t)}
       float ${r}(int row, int col) {
-        return ${r}(${iu(h, o)});
+        return ${r}(${ou(h, o)});
       }
     `;
   }
@@ -24165,10 +24223,10 @@ function fK(e, t) {
     return `
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
     `;
-  let l = a[0], c = a[1], p = vi(s);
+  let l = a[0], c = a[1], p = ki(s);
   return c === 1 ? t ? `
       float ${r}(int row, int col) {
         float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
@@ -24209,14 +24267,14 @@ function fK(e, t) {
   }
 `;
 }
-function mK(e, t) {
+function CK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)];
   if (n[0] === 1) {
-    let d = n.slice(1), h = [1, 2], f = au(e, d), m = ["b", "row", "col"];
+    let d = n.slice(1), h = [1, 2], f = iu(e, d), m = ["b", "row", "col"];
     return `
-        ${h1(f, t)}
+        ${y1(f, t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${iu(m, h)});
+          return ${r}(${ou(m, h)});
         }
       `;
   }
@@ -24241,14 +24299,14 @@ function mK(e, t) {
     }
   `;
 }
-function gK(e, t) {
+function NK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[1] * n[2], i = n[2], { newShape: o, keptDims: u } = w.squeezeShape(n), l = o;
   if (l.length < n.length) {
-    let m = au(e, l), g = ["row", "col", "depth"];
+    let m = iu(e, l), g = ["row", "col", "depth"];
     return `
-        ${su(m, t)}
+        ${ru(m, t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${iu(g, u)});
+          return ${r}(${ou(g, u)});
         }
       `;
   }
@@ -24257,7 +24315,7 @@ function gK(e, t) {
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${i}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
     `;
   let c = e.shapeInfo.texShape, p = c[0], d = c[1], h = e.shapeInfo.flatOffset;
@@ -24296,7 +24354,7 @@ function gK(e, t) {
       return sampleTexture(${s}, uv);
     }
   `;
-  let f = vi(s);
+  let f = ki(s);
   return t ? `
     float ${r}(int row, int col, int depth) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -24315,7 +24373,7 @@ function gK(e, t) {
       }
   `;
 }
-function bK(e, t) {
+function TK(e, t) {
   let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = fn();
   if (t)
     return `
@@ -24344,14 +24402,14 @@ function bK(e, t) {
     }
   `;
 }
-function yK(e, t) {
+function $K(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[3], i = n[2] * a, o = n[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(n);
   if (u.length < n.length) {
-    let y = au(e, u), v = ["row", "col", "depth", "depth2"];
+    let y = iu(e, u), v = ["row", "col", "depth", "depth2"];
     return `
-      ${su(y, t)}
+      ${ru(y, t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${iu(v, l)});
+        return ${r}(${ou(v, l)});
       }
     `;
   }
@@ -24360,7 +24418,7 @@ function yK(e, t) {
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${a}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1], f = `int stride2 = ${s}Shape[3];`, m = `int stride1 = ${s}Shape[2] * stride2;`, g = `int stride0 = ${s}Shape[1] * stride1;`;
@@ -24408,7 +24466,7 @@ function yK(e, t) {
         return sampleTexture(${s}, uv);
       }
     `;
-  let b = vi(s);
+  let b = ki(s);
   return t ? `
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -24430,14 +24488,14 @@ function yK(e, t) {
     }
   `;
 }
-function vK(e) {
+function _K(e) {
   let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = t[4], a = t[3] * r, i = t[2] * a, o = t[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(t);
   if (u.length < t.length) {
-    let m = au(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
+    let m = iu(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
     return `
-      ${su(m)}
+      ${ru(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${iu(g, l)});
+        return ${s}(${ou(g, l)});
       }
     `;
   }
@@ -24448,7 +24506,7 @@ function vK(e) {
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${a}, ${r})) +
           depth3;
-        ${ru(e)}
+        ${au(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1];
@@ -24476,7 +24534,7 @@ function vK(e) {
         return sampleTexture(${n}, uv);
       }
     `;
-  let f = vi(n);
+  let f = ki(n);
   return `
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -24487,15 +24545,15 @@ function vK(e) {
     }
   `;
 }
-function xK(e) {
+function AK(e) {
   let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), { newShape: r, keptDims: a } = w.squeezeShape(t);
   if (r.length < t.length) {
-    let g = au(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
+    let g = iu(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
     return `
-      ${su(g)}
+      ${ru(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${iu(b, a)});
+        return ${s}(${ou(b, a)});
       }
     `;
   }
@@ -24510,7 +24568,7 @@ function xK(e) {
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
     `;
   let p = e.shapeInfo.flatOffset, d = e.shapeInfo.texShape, h = d[0], f = d[1];
@@ -24542,7 +24600,7 @@ function xK(e) {
         return sampleTexture(${n}, uv);
       }
     `;
-  let m = vi(n);
+  let m = ki(n);
   return `
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
@@ -24554,7 +24612,7 @@ function xK(e) {
     }
   `;
 }
-function ru(e) {
+function au(e) {
   let t = e.name, n = w.sizeFromShape(e.shapeInfo.logicalShape);
   return n < 2 ? `return ${t};` : `
     for (int i = 0; i < ${n}; i++) {
@@ -24564,8 +24622,8 @@ function ru(e) {
     }
   `;
 }
-function wK(e, t) {
-  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = e.shapeInfo.logicalShape.length, i = t.logicalShape.length, o = p1(e.shapeInfo.logicalShape, t.logicalShape), u = rt(i), l = i - a, c, p = ["x", "y", "z", "w", "u", "v"];
+function EK(e, t) {
+  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = e.shapeInfo.logicalShape.length, i = t.logicalShape.length, o = b1(e.shapeInfo.logicalShape, t.logicalShape), u = rt(i), l = i - a, c, p = ["x", "y", "z", "w", "u", "v"];
   a === 0 ? c = "" : i < 2 && o.length >= 1 ? c = "coords = 0;" : c = o.map((y) => `coords.${p[y + l]} = 0;`).join(`
 `);
   let d = "";
@@ -24594,7 +24652,7 @@ function wK(e, t) {
     }
   `;
 }
-function kK(e, t) {
+function RK(e, t) {
   let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = t.texShape, i = e.shapeInfo.texShape, o = e.shapeInfo.logicalShape.length, u = t.logicalShape.length;
   if (!e.shapeInfo.isUniform && o === u && e.shapeInfo.flatOffset == null && w.arraysEqual(i, a))
     return `
@@ -24602,7 +24660,7 @@ function kK(e, t) {
         return sampleTexture(${n}, resultUV);
       }
     `;
-  let l = rt(u), c = p1(e.shapeInfo.logicalShape, t.logicalShape), p = u - o, d, h = ["x", "y", "z", "w", "u", "v"];
+  let l = rt(u), c = b1(e.shapeInfo.logicalShape, t.logicalShape), p = u - o, d, h = ["x", "y", "z", "w", "u", "v"];
   o === 0 ? d = "" : u < 2 && c.length >= 1 ? d = "coords = 0;" : d = c.map((m) => `coords.${h[m + p]} = 0;`).join(`
 `);
   let f = "";
@@ -24629,25 +24687,25 @@ function rt(e) {
     return "ivec6";
   throw Error(`GPU for rank ${e} is not yet supported`);
 }
-function mv(e, t, n) {
+function bv(e, t, n) {
   let { newShape: s, keptDims: r } = w.squeezeShape(t), a = t.length, i = e && a === 3 && t[0] === 1, o = i ? t.slice(1) : s, u = !e && a > 1 && !w.arraysEqual(t, n) && s.length < a || i;
   return { useSqueezeShape: u, uniformShape: u ? o : t, keptDims: r };
 }
-function au(e, t) {
+function iu(e, t) {
   let n = JSON.parse(JSON.stringify(e));
   return n.shapeInfo.logicalShape = t, n;
 }
-function iu(e, t) {
+function ou(e, t) {
   return t.map((n) => e[n]).join(", ");
 }
-function IK(e, t, n, s) {
+function DK(e, t, n, s) {
   let r = n.map((c, p) => {
     let d = { logicalShape: c.shape, texShape: c.isUniform ? null : c.texData.texShape, isUniform: c.isUniform, isPacked: c.isUniform ? false : c.texData.isPacked, flatOffset: null };
     return c.texData != null && c.texData.slice != null && c.texData.slice.flatOffset > 0 && (d.flatOffset = c.texData.slice.flatOffset), { name: t.variableNames[p], shapeInfo: d };
-  }), a = r.map((c) => c.shapeInfo), i = { logicalShape: s.shape, texShape: s.texData.texShape, isUniform: false, isPacked: s.texData.isPacked, flatOffset: null }, o = V5(r, i, t), u = GC(e.gl, o), l = e.createProgram(u);
-  return X().get("ENGINE_COMPILE_ONLY") ? { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, ...m1(e, t, l) };
+  }), a = r.map((c) => c.shapeInfo), i = { logicalShape: s.shape, texShape: s.texData.texShape, isUniform: false, isPacked: s.texData.isPacked, flatOffset: null }, o = Q5(r, i, t), u = XC(e.gl, o), l = e.createProgram(u);
+  return X().get("ENGINE_COMPILE_ONLY") ? { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, uniformLocations: null, customUniformLocations: null, infLoc: null, nanLoc: null, inShapesLocations: null, inTexShapesLocations: null, outShapeLocation: null, outShapeStridesLocation: null, outTexShapeLocation: null } : { program: t, fragmentShader: u, source: o, webGLProgram: l, inShapeInfos: a, outShapeInfo: i, ...x1(e, t, l) };
 }
-function m1(e, t, n) {
+function x1(e, t, n) {
   let s = {}, r = {}, a = {}, i = [], o, u, l, c = null, p = null;
   p = e.getUniformLocation(n, "NAN", false), X().getNumber("WEBGL_VERSION") === 1 && (c = e.getUniformLocation(n, "INFINITY", false));
   let d = false;
@@ -24659,7 +24717,7 @@ function m1(e, t, n) {
     i[f] = e.getUniformLocation(n, h.name, d);
   }), { uniformLocations: s, customUniformLocations: i, infLoc: c, nanLoc: p, inShapesLocations: r, inTexShapesLocations: a, outShapeLocation: o, outShapeStridesLocation: l, outTexShapeLocation: u };
 }
-function rw(e, t) {
+function aw(e, t) {
   if (e.length !== t.length)
     throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);
   e.forEach((n, s) => {
@@ -24673,13 +24731,13 @@ function rw(e, t) {
       throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`);
   });
 }
-function SK(e, t, n, s, r) {
-  t.program.enableShapeUniforms || (rw(t.inShapeInfos, n), rw([t.outShapeInfo], [s]));
+function FK(e, t, n, s, r) {
+  t.program.enableShapeUniforms || (aw(t.inShapeInfos, n), aw([t.outShapeInfo], [s]));
   let a = s.texData.texture, i = s.texData.texShape;
   s.texData.isPacked ? e.setOutputPackedMatrixTexture(a.texture, i[0], i[1]) : e.setOutputMatrixTexture(a.texture, i[0], i[1]), e.setProgram(t.webGLProgram), X().getNumber("WEBGL_VERSION") === 1 && t.infLoc !== null && e.gl.uniform1f(t.infLoc, 1 / 0), t.nanLoc !== null && e.gl.uniform1f(t.nanLoc, NaN), n.forEach((u, l) => {
     let c = t.program.variableNames[l], p = t.uniformLocations[c], d = t.uniformLocations[`offset${c}`], h = t.inShapesLocations[`${c}Shape`], f = t.inTexShapesLocations[`${c}TexShape`];
     if (h) {
-      let { uniformShape: m } = mv(t.program.packedInputs, u.shape, u.texData.texShape);
+      let { uniformShape: m } = bv(t.program.packedInputs, u.shape, u.texData.texShape);
       switch (m.length) {
         case 1:
           e.gl.uniform1iv(h, new Int32Array(m));
@@ -24766,12 +24824,12 @@ function SK(e, t, n, s, r) {
       throw Error(`uniform type ${u.type} is not supported yet.`);
   }), e.executeProgram();
 }
-function CK(e, t, n) {
+function OK(e, t, n) {
   let s = "";
   t.concat(n).forEach((i) => {
     let o = i.texData != null && i.texData.slice != null && i.texData.slice.flatOffset > 0;
     if (e.enableShapeUniforms && !i.isUniform) {
-      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = mv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
+      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = bv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
       if (c.length === 1 && e.packedInputs) {
         let k = [Math.ceil(u[0] / 2), Math.ceil(u[1] / 2)];
         d = `${k[0] > 1}_${k[1] > 1}`;
@@ -24794,13 +24852,13 @@ function CK(e, t, n) {
 function Sn(e) {
   return X().getBool("WEBGL_USE_SHAPES_UNIFORMS") && e <= 4;
 }
-var NK = class {
+var PK = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
     let t = fn();
     this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms ? qp(["r", "c", "d"], e) : yi(["r", "c", "d"], e)}
+        ${this.enableShapeUniforms ? Kp(["r", "c", "d"], e) : wi(["r", "c", "d"], e)}
         return ivec3(r, c, d);
       }
 
@@ -24821,13 +24879,13 @@ var NK = class {
     `;
   }
 };
-var TK = class {
+var zK = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
     let t = fn();
     this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms ? qp(["r", "c", "d"], e) : yi(["r", "c", "d"], e)}
+        ${this.enableShapeUniforms ? Kp(["r", "c", "d"], e) : wi(["r", "c", "d"], e)}
         return ivec3(r, c, d);
       }
 
@@ -24848,12 +24906,12 @@ var TK = class {
     `;
   }
 };
-var $K = class {
+var MK = class {
   constructor(e) {
     this.variableNames = ["A"], this.outTexUsage = 3;
     let t = fn();
     this.outputShape = e, this.userCode = `
-      ${d1}
+      ${g1}
 
       void main() {
         float x = getAAtOutCoords();
@@ -24862,12 +24920,12 @@ var $K = class {
     `;
   }
 };
-var _K = class {
+var LK = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = 3;
     let t = fn();
     this.outputShape = e, this.userCode = `
-      ${d1}
+      ${g1}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -24877,14 +24935,14 @@ var _K = class {
     `;
   }
 };
-var AK = class {
+var BK = class {
   constructor(e, t = false) {
     this.variableNames = ["A"], this.customUniforms = [{ name: "texShape", type: "ivec2" }];
     let n = fn();
     this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
     let s = "result";
     t && (s = "floor(result * 255. + 0.5)"), this.userCode = `
-      ${this.enableShapeUniforms ? fv() : hv(e)}
+      ${this.enableShapeUniforms ? gv() : mv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -24916,7 +24974,7 @@ var AK = class {
     `;
   }
 };
-var EK = class {
+var VK = class {
   constructor(e, t = false) {
     this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
     let n = fn();
@@ -24957,7 +25015,7 @@ var EK = class {
         `;
       }
     this.userCode = `
-        ${this.enableShapeUniforms ? fv() : hv(e)}
+        ${this.enableShapeUniforms ? gv() : mv(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -24975,9 +25033,9 @@ var EK = class {
     `;
   }
 };
-var RK = {};
-Ae(RK, { bindVertexProgramAttributeStreams: () => S1, createBufferFromOutputTexture: () => T1, createFloat16MatrixTexture: () => x1, createFloat16PackedMatrixTexture: () => I1, createFloat32MatrixTexture: () => v1, createIndexBuffer: () => y1, createPackedMatrixTexture: () => k1, createUnsignedBytesMatrixTexture: () => w1, createVertexBuffer: () => b1, createVertexShader: () => g1, downloadByteEncodedFloatMatrixFromOutputTexture: () => _1, downloadFloat32MatrixFromBuffer: () => $1, downloadMatrixFromPackedOutputTexture: () => E1, downloadPackedMatrixFromBuffer: () => A1, getInternalFormatForFloat16MatrixTexture: () => bv, getInternalFormatForFloat16PackedMatrixTexture: () => xv, getInternalFormatForFloat32MatrixTexture: () => gv, getInternalFormatForPackedMatrixTexture: () => vv, getInternalFormatForUnsignedBytesMatrixTexture: () => yv, uploadDenseMatrixToTexture: () => C1, uploadPixelDataToTexture: () => N1 });
-function g1(e) {
+var WK = {};
+Ae(WK, { bindVertexProgramAttributeStreams: () => _1, createBufferFromOutputTexture: () => R1, createFloat16MatrixTexture: () => C1, createFloat16PackedMatrixTexture: () => $1, createFloat32MatrixTexture: () => S1, createIndexBuffer: () => I1, createPackedMatrixTexture: () => T1, createUnsignedBytesMatrixTexture: () => N1, createVertexBuffer: () => k1, createVertexShader: () => w1, downloadByteEncodedFloatMatrixFromOutputTexture: () => F1, downloadFloat32MatrixFromBuffer: () => D1, downloadMatrixFromPackedOutputTexture: () => P1, downloadPackedMatrixFromBuffer: () => O1, getInternalFormatForFloat16MatrixTexture: () => vv, getInternalFormatForFloat16PackedMatrixTexture: () => kv, getInternalFormatForFloat32MatrixTexture: () => yv, getInternalFormatForPackedMatrixTexture: () => wv, getInternalFormatForUnsignedBytesMatrixTexture: () => xv, uploadDenseMatrixToTexture: () => A1, uploadPixelDataToTexture: () => E1 });
+function w1(e) {
   let t = fn(), n = `${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
@@ -24988,103 +25046,103 @@ function g1(e) {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
     }`;
-  return UC(e, n);
+  return KC(e, n);
 }
-function b1(e) {
+function k1(e) {
   let t = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
-  return jC(e, t);
+  return ZC(e, t);
 }
-function y1(e) {
+function I1(e) {
   let t = new Uint16Array([0, 1, 2, 2, 1, 3]);
-  return KC(e, t);
+  return JC(e, t);
 }
 function Ql(e, t, n, s, r, a) {
-  YC(t, n);
-  let i = XC(e), o = e.TEXTURE_2D;
+  t1(t, n);
+  let i = e1(e), o = e.TEXTURE_2D;
   return fe(e, () => e.bindTexture(o, i)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_MIN_FILTER, e.NEAREST)), fe(e, () => e.texParameteri(o, e.TEXTURE_MAG_FILTER, e.NEAREST)), X().getNumber("WEBGL_VERSION") === 1 ? fe(e, () => e.texImage2D(o, 0, s, t, n, 0, r, a, null)) : fe(e, () => e.texStorage2D(o, 1, s, t, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null)), { texture: i, texShape: [n, t] };
 }
-function gv(e) {
+function yv(e) {
   return e.internalFormatFloat;
 }
-function v1(e, t, n, s) {
+function S1(e, t, n, s) {
   let [r, a] = Yl(t, n);
-  return Ql(e, r, a, gv(s), s.textureFormatFloat, e.FLOAT);
-}
-function bv(e) {
-  return e.internalFormatHalfFloat;
-}
-function x1(e, t, n, s) {
-  let [r, a] = Yl(t, n);
-  return Ql(e, r, a, bv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
-}
-function yv(e) {
-  return e.downloadTextureFormat;
-}
-function w1(e, t, n, s) {
-  let [r, a] = Yl(t, n);
-  return Ql(e, r, a, yv(s), e.RGBA, e.UNSIGNED_BYTE);
+  return Ql(e, r, a, yv(s), s.textureFormatFloat, e.FLOAT);
 }
 function vv(e) {
-  return e.internalFormatPackedFloat;
+  return e.internalFormatHalfFloat;
 }
-function k1(e, t, n, s) {
-  let [r, a] = tu(t, n);
-  return Ql(e, r, a, vv(s), e.RGBA, e.FLOAT);
+function C1(e, t, n, s) {
+  let [r, a] = Yl(t, n);
+  return Ql(e, r, a, vv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
 }
 function xv(e) {
+  return e.downloadTextureFormat;
+}
+function N1(e, t, n, s) {
+  let [r, a] = Yl(t, n);
+  return Ql(e, r, a, xv(s), e.RGBA, e.UNSIGNED_BYTE);
+}
+function wv(e) {
+  return e.internalFormatPackedFloat;
+}
+function T1(e, t, n, s) {
+  let [r, a] = nu(t, n);
+  return Ql(e, r, a, wv(s), e.RGBA, e.FLOAT);
+}
+function kv(e) {
   return e.internalFormatPackedHalfFloat;
 }
-function I1(e, t, n, s) {
-  let [r, a] = tu(t, n);
-  return Ql(e, r, a, xv(s), e.RGBA, s.textureTypeHalfFloat);
+function $1(e, t, n, s) {
+  let [r, a] = nu(t, n);
+  return Ql(e, r, a, kv(s), e.RGBA, s.textureTypeHalfFloat);
 }
-function S1(e, t, n) {
-  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Bm(e, t, "clipSpacePos", n, 3, 20, 0) && Bm(e, t, "uv", n, 2, 20, 12);
+function _1(e, t, n) {
+  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Wm(e, t, "clipSpacePos", n, 3, 20, 0) && Wm(e, t, "uv", n, 2, 20, 12);
 }
-function C1(e, t, n, s, r, a) {
+function A1(e, t, n, s, r, a) {
   fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
   let i, o, u;
   r instanceof Uint8Array ? (i = new Uint8Array(n * s * 4), o = e.UNSIGNED_BYTE, u = e.RGBA) : (i = new Float32Array(n * s * 4), o = e.FLOAT, u = a.internalFormatPackedFloat), i.set(r), X().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n, s, e.RGBA, o, i)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, u, n, s, 0, e.RGBA, o, i)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
 }
-function N1(e, t, n) {
+function E1(e, t, n) {
   fe(e, () => e.bindTexture(e.TEXTURE_2D, t)), n.data instanceof Uint8Array ? X().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, n.width, n.height, e.RGBA, e.UNSIGNED_BYTE, n.data)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, e.RGBA, n.width, n.height, 0, e.RGBA, e.UNSIGNED_BYTE, n.data)) : X().getNumber("WEBGL_VERSION") === 2 ? fe(e, () => e.texSubImage2D(e.TEXTURE_2D, 0, 0, 0, e.RGBA, e.UNSIGNED_BYTE, n)) : fe(e, () => e.texImage2D(e.TEXTURE_2D, 0, e.RGBA, e.RGBA, e.UNSIGNED_BYTE, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
 }
-function T1(e, t, n, s) {
+function R1(e, t, n, s) {
   let r = e.createBuffer();
   fe(e, () => e.bindBuffer(e.PIXEL_PACK_BUFFER, r));
   let o = 4 * 4 * t * n;
   return fe(e, () => e.bufferData(e.PIXEL_PACK_BUFFER, o, e.STREAM_READ)), fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, 0)), fe(e, () => e.bindBuffer(e.PIXEL_PACK_BUFFER, null)), r;
 }
-function $1(e, t, n) {
+function D1(e, t, n) {
   let s = e, r = new Float32Array(n);
   return s.bindBuffer(s.PIXEL_PACK_BUFFER, t), s.getBufferSubData(s.PIXEL_PACK_BUFFER, 0, r), s.bindBuffer(s.PIXEL_PACK_BUFFER, null), r;
 }
-function _1(e, t, n, s) {
-  let [r, a] = Yl(t, n), i = 4, o = new Uint8Array(T5(t * n, i));
+function F1(e, t, n, s) {
+  let [r, a] = Yl(t, n), i = 4, o = new Uint8Array(z5(t * n, i));
   return fe(e, () => e.readPixels(0, 0, r, a, s.downloadTextureFormat, e.UNSIGNED_BYTE, o)), new Float32Array(o.buffer);
 }
-function A1(e, t, n, s, r, a, i, o) {
-  let u = e, l = new Float32Array($5(a, i));
+function O1(e, t, n, s, r, a, i, o) {
+  let u = e, l = new Float32Array(M5(a, i));
   return u.bindBuffer(u.PIXEL_PACK_BUFFER, t), u.getBufferSubData(u.PIXEL_PACK_BUFFER, 0, l), u.bindBuffer(u.PIXEL_PACK_BUFFER, null), l;
 }
-function E1(e, t, n) {
+function P1(e, t, n) {
   let s = new Float32Array(t * n * 4);
   return fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, s)), s;
 }
-var Kf = class {
+var Xf = class {
   constructor(e) {
     this.outputTexture = null, this.program = null, this.disposed = false, this.vertexAttrsAreBound = false, this.itemsToPoll = [];
     let t = X().getNumber("WEBGL_VERSION");
-    e != null ? (this.gl = e, S5(t, e)) : this.gl = vs(t);
+    e != null ? (this.gl = e, F5(t, e)) : this.gl = vs(t);
     let n = "WEBGL_color_buffer_float", s = "EXT_color_buffer_half_float";
     if (this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile"), X().getNumber("WEBGL_VERSION") === 1) {
       let r = "OES_texture_float", a = "OES_texture_half_float";
-      if (this.textureFloatExtension = Fu(this.gl, r), Ln(this.gl, a))
-        this.textureHalfFloatExtension = Fu(this.gl, a);
+      if (this.textureFloatExtension = Ou(this.gl, r), Ln(this.gl, a))
+        this.textureHalfFloatExtension = Ou(this.gl, a);
       else if (X().get("WEBGL_FORCE_F16_TEXTURES"))
         throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
       if (this.colorBufferFloatExtension = this.gl.getExtension(n), Ln(this.gl, s))
-        this.colorBufferHalfFloatExtension = Fu(this.gl, s);
+        this.colorBufferHalfFloatExtension = Ou(this.gl, s);
       else if (X().get("WEBGL_FORCE_F16_TEXTURES"))
         throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
     } else if (n = "EXT_color_buffer_float", Ln(this.gl, n))
@@ -25093,7 +25151,7 @@ var Kf = class {
       this.colorBufferHalfFloatExtension = this.gl.getExtension(s);
     else
       throw new Error("GL context does not support color renderable floats");
-    this.vertexBuffer = b1(this.gl), this.indexBuffer = y1(this.gl), this.framebuffer = QC(this.gl), this.textureConfig = dv(this.gl, this.textureHalfFloatExtension);
+    this.vertexBuffer = k1(this.gl), this.indexBuffer = I1(this.gl), this.framebuffer = n1(this.gl), this.textureConfig = hv(this.gl, this.textureHalfFloatExtension);
   }
   get debug() {
     return X().getBool("DEBUG");
@@ -25106,41 +25164,41 @@ var Kf = class {
     fe(e, () => e.finish()), fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), fe(e, () => e.deleteFramebuffer(this.framebuffer)), fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, null)), fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, null)), fe(e, () => e.deleteBuffer(this.indexBuffer)), this.disposed = true;
   }
   createFloat32MatrixTexture(e, t) {
-    return this.throwIfDisposed(), v1(this.gl, e, t, this.textureConfig);
+    return this.throwIfDisposed(), S1(this.gl, e, t, this.textureConfig);
   }
   createFloat16MatrixTexture(e, t) {
-    return this.throwIfDisposed(), x1(this.gl, e, t, this.textureConfig);
+    return this.throwIfDisposed(), C1(this.gl, e, t, this.textureConfig);
   }
   createUnsignedBytesMatrixTexture(e, t) {
-    return this.throwIfDisposed(), w1(this.gl, e, t, this.textureConfig);
+    return this.throwIfDisposed(), N1(this.gl, e, t, this.textureConfig);
   }
   uploadPixelDataToTexture(e, t) {
-    this.throwIfDisposed(), N1(this.gl, e, t);
+    this.throwIfDisposed(), E1(this.gl, e, t);
   }
   uploadDenseMatrixToTexture(e, t, n, s) {
-    this.throwIfDisposed(), C1(this.gl, e, t, n, s, this.textureConfig);
+    this.throwIfDisposed(), A1(this.gl, e, t, n, s, this.textureConfig);
   }
   createFloat16PackedMatrixTexture(e, t) {
-    return this.throwIfDisposed(), I1(this.gl, e, t, this.textureConfig);
+    return this.throwIfDisposed(), $1(this.gl, e, t, this.textureConfig);
   }
   createPackedMatrixTexture(e, t) {
-    return this.throwIfDisposed(), k1(this.gl, e, t, this.textureConfig);
+    return this.throwIfDisposed(), T1(this.gl, e, t, this.textureConfig);
   }
   deleteMatrixTexture(e) {
-    this.throwIfDisposed(), this.outputTexture === e && (Vm(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
+    this.throwIfDisposed(), this.outputTexture === e && (Um(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
   }
   downloadByteEncodedFloatMatrixFromOutputTexture(e, t, n) {
-    return this.downloadMatrixDriver(e, () => _1(this.gl, t, n, this.textureConfig));
+    return this.downloadMatrixDriver(e, () => F1(this.gl, t, n, this.textureConfig));
   }
   downloadPackedMatrixFromBuffer(e, t, n, s, r, a) {
-    return A1(this.gl, e, t, n, s, r, a, this.textureConfig);
+    return O1(this.gl, e, t, n, s, r, a, this.textureConfig);
   }
   downloadFloat32MatrixFromBuffer(e, t) {
-    return $1(this.gl, e, t);
+    return D1(this.gl, e, t);
   }
   createBufferFromTexture(e, t, n) {
     this.bindTextureToFrameBuffer(e);
-    let s = T1(this.gl, t, n, this.textureConfig);
+    let s = R1(this.gl, t, n, this.textureConfig);
     return this.unbindTextureToFrameBuffer(), s;
   }
   createAndWaitForFence() {
@@ -25160,14 +25218,14 @@ var Kf = class {
     return { query: t, isFencePassed: n };
   }
   downloadMatrixFromPackedTexture(e, t, n) {
-    return this.downloadMatrixDriver(e, () => E1(this.gl, t, n));
+    return this.downloadMatrixDriver(e, () => P1(this.gl, t, n));
   }
   createProgram(e) {
     this.throwIfDisposed();
     let t = this.gl;
-    this.vertexShader == null && (this.vertexShader = g1(t));
-    let n = HC(t);
-    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), qC(t, n), this.debug && rd(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = S1(t, this.program, this.vertexBuffer)), n;
+    this.vertexShader == null && (this.vertexShader = w1(t));
+    let n = YC(t);
+    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), QC(t, n), this.debug && rd(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = _1(t, this.program, this.vertexBuffer)), n;
   }
   deleteProgram(e) {
     this.throwIfDisposed(), e === this.program && (this.program = null), e != null && fe(this.gl, () => this.gl.deleteProgram(e));
@@ -25176,7 +25234,7 @@ var Kf = class {
     this.throwIfDisposed(), this.program = e, this.program != null && this.debug && rd(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
   }
   getUniformLocation(e, t, n = true) {
-    return this.throwIfDisposed(), n ? JC(this.gl, e, t) : e1(this.gl, e, t);
+    return this.throwIfDisposed(), n ? r1(this.gl, e, t) : a1(this.gl, e, t);
   }
   getAttributeLocation(e, t) {
     return this.throwIfDisposed(), fe(this.gl, () => this.gl.getAttribLocation(e, t));
@@ -25185,14 +25243,14 @@ var Kf = class {
     return this.throwIfDisposed(), this.gl.getUniformLocation(e, t);
   }
   setInputMatrixTexture(e, t, n) {
-    this.throwIfDisposed(), this.throwIfNoProgram(), t1(this.gl, e, t, n);
+    this.throwIfDisposed(), this.throwIfNoProgram(), i1(this.gl, e, t, n);
   }
   setOutputMatrixTexture(e, t, n) {
     this.setOutputMatrixTextureDriver(e, n, t);
   }
   setOutputPackedMatrixTexture(e, t, n) {
     this.throwIfDisposed();
-    let [s, r] = tu(t, n);
+    let [s, r] = nu(t, n);
     this.setOutputMatrixTextureDriver(e, s, r);
   }
   setOutputMatrixWriteRegion(e, t, n, s) {
@@ -25202,7 +25260,7 @@ var Kf = class {
     throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
   }
   debugValidate() {
-    this.program != null && rd(this.gl, this.program), Ou(this.gl);
+    this.program != null && rd(this.gl, this.program), Pu(this.gl);
   }
   executeProgram() {
     this.throwIfDisposed(), this.throwIfNoProgram();
@@ -25213,7 +25271,7 @@ var Kf = class {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.finish());
   }
   getQueryTimerExtension() {
-    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Fu(this.gl, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
+    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Ou(this.gl, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
   }
   getQueryTimerExtensionWebGL2() {
     return this.getQueryTimerExtension();
@@ -25269,7 +25327,7 @@ var Kf = class {
     });
   }
   pollItems() {
-    let e = DK(this.itemsToPoll.map((t) => t.isDoneFn));
+    let e = UK(this.itemsToPoll.map((t) => t.isDoneFn));
     for (let t = 0; t <= e; ++t) {
       let { resolveFn: n } = this.itemsToPoll[t];
       n();
@@ -25280,10 +25338,10 @@ var Kf = class {
     this.itemsToPoll.push({ isDoneFn: e, resolveFn: t }), !(this.itemsToPoll.length > 1) && w.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0));
   }
   bindTextureToFrameBuffer(e) {
-    this.throwIfDisposed(), ad(this.gl, e, this.framebuffer), this.debug && Ou(this.gl);
+    this.throwIfDisposed(), ad(this.gl, e, this.framebuffer), this.debug && Pu(this.gl);
   }
   unbindTextureToFrameBuffer() {
-    this.outputTexture != null ? (ad(this.gl, this.outputTexture, this.framebuffer), this.debug && Ou(this.gl)) : Vm(this.gl, this.framebuffer);
+    this.outputTexture != null ? (ad(this.gl, this.outputTexture, this.framebuffer), this.debug && Pu(this.gl)) : Um(this.gl, this.framebuffer);
   }
   downloadMatrixDriver(e, t) {
     this.bindTextureToFrameBuffer(e);
@@ -25293,7 +25351,7 @@ var Kf = class {
   setOutputMatrixTextureDriver(e, t, n) {
     this.throwIfDisposed();
     let s = this.gl;
-    ad(s, e, this.framebuffer), this.debug && Ou(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
+    ad(s, e, this.framebuffer), this.debug && Pu(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
   }
   setOutputMatrixWriteRegionDriver(e, t, n, s) {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.scissor(e, t, n, s));
@@ -25307,20 +25365,20 @@ var Kf = class {
       throw new Error("No GPU program is currently set.");
   }
 };
-function DK(e) {
+function UK(e) {
   let t = 0;
   for (; t < e.length && e[t](); ++t)
     ;
   return t - 1;
 }
-var { addImpl: FK, bincountImpl: R1, bincountReduceImpl: OK, ceilImpl: PK, concatImpl: zK, equalImpl: MK, expImpl: LK, expm1Impl: BK, floorImpl: VK, gatherNdImpl: WK, gatherV2Impl: UK, greaterImpl: GK, greaterEqualImpl: HK, lessImpl: qK, lessEqualImpl: jK, linSpaceImpl: KK, logImpl: XK, maxImpl: YK, maximumImpl: QK, minimumImpl: ZK, multiplyImpl: JK, negImpl: eX, notEqualImpl: tX, prodImpl: nX, rangeImpl: sX, rsqrtImpl: rX, sigmoidImpl: aX, simpleAbsImpl: D1, sliceImpl: iX, sparseFillEmptyRowsImpl: oX, sparseReshapeImpl: uX, sparseSegmentReductionImpl: F1, sqrtImpl: lX, stridedSliceImpl: cX, stringNGramsImpl: dX, stringSplitImpl: pX, stringToHashBucketFastImpl: hX, subImpl: fX, tileImpl: mX, topKImpl: gX, transposeImpl: wv, uniqueImpl: bX } = Zy;
-function O1(e, t) {
+var { addImpl: GK, bincountImpl: z1, bincountReduceImpl: HK, ceilImpl: qK, concatImpl: jK, equalImpl: KK, expImpl: XK, expm1Impl: YK, floorImpl: QK, gatherNdImpl: ZK, gatherV2Impl: JK, greaterImpl: eX, greaterEqualImpl: tX, lessImpl: nX, lessEqualImpl: sX, linSpaceImpl: rX, logImpl: aX, maxImpl: iX, maximumImpl: oX, minimumImpl: uX, multiplyImpl: lX, negImpl: cX, notEqualImpl: dX, prodImpl: pX, rangeImpl: hX, rsqrtImpl: fX, sigmoidImpl: mX, simpleAbsImpl: M1, sliceImpl: gX, sparseFillEmptyRowsImpl: bX, sparseReshapeImpl: yX, sparseSegmentReductionImpl: L1, sqrtImpl: vX, stridedSliceImpl: xX, stringNGramsImpl: wX, stringSplitImpl: kX, stringToHashBucketFastImpl: IX, subImpl: SX, tileImpl: CX, topKImpl: NX, transposeImpl: Iv, uniqueImpl: TX } = ev;
+function B1(e, t) {
   return ["x", "y", "z", "w", "u", "v"].slice(0, t).map((n) => `${e}.${n}`);
 }
 function ln(e, t) {
-  return t === 1 ? [e] : O1(e, t);
+  return t === 1 ? [e] : B1(e, t);
 }
-function yX(e, t) {
+function $X(e, t) {
   if (e === 1)
     return "rc";
   let n = "";
@@ -25328,7 +25386,7 @@ function yX(e, t) {
     n += t[s], s < e - 1 && (n += ",");
   return n;
 }
-var vX = class {
+var _X = class {
   constructor(e) {
     if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = Sn(this.outputShape.length), this.rank === 0)
       this.userCode = `
@@ -25394,7 +25452,7 @@ var vX = class {
             rEdge || cEdge ? 0. : getA(${t[3]})`;
   }
 };
-var P1 = class {
+var V1 = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
     let n = "";
@@ -25414,8 +25472,8 @@ var P1 = class {
       `;
     }
     this.userCode = `
-      ${xX(t, this.enableShapeUniforms)}
-      ${this.enableShapeUniforms ? fv() : hv(e)}
+      ${AX(t, this.enableShapeUniforms)}
+      ${this.enableShapeUniforms ? gv() : mv(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -25433,22 +25491,22 @@ var P1 = class {
     `;
   }
 };
-function xX(e, t) {
+function AX(e, t) {
   return `
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t ? B5(["r", "c", "d"], "inputShape") : yi(["r", "c", "d"], e)}
+      ${t ? Y5(["r", "c", "d"], "inputShape") : wi(["r", "c", "d"], e)}
       return ivec3(r, c, d);
     }
   `;
 }
-var wX = class {
+var EX = class {
   constructor(e) {
     this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.logEnabled = false, this.usedTextures = {};
   }
   acquireTexture(e, t, n) {
-    let s = iw(t, n), r = ow(e, s, n);
+    let s = ow(t, n), r = uw(e, s, n);
     r in this.freeTextures || (this.freeTextures[r] = []), r in this.usedTextures || (this.usedTextures[r] = []);
-    let a = aw(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n);
+    let a = iw(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n);
     if (this.freeTextures[r].length > 0) {
       this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log();
       let o = this.freeTextures[r].shift();
@@ -25460,9 +25518,9 @@ var wX = class {
   releaseTexture(e, t, n, s) {
     if (this.freeTextures == null)
       return;
-    let r = iw(n, s), a = ow(t, r, s);
+    let r = ow(n, s), a = uw(t, r, s);
     a in this.freeTextures || (this.freeTextures[a] = []);
-    let i = aw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), o = X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
+    let i = iw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), o = X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
     o !== -1 && this._numBytesAllocated > o ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--;
     let u = this.usedTextures[a], l = u.indexOf(e);
     if (l < 0)
@@ -25503,7 +25561,7 @@ var wX = class {
     }
   }
 };
-function kX(e, t) {
+function RX(e, t) {
   let n = e;
   if (t === n.R32F)
     return 4;
@@ -25519,47 +25577,47 @@ function kX(e, t) {
     return 4;
   throw new Error(`Unknown internal format ${t}`);
 }
-function aw(e, t, n, s, r) {
-  let a = IX(t, s), i;
+function iw(e, t, n, s, r) {
+  let a = DX(t, s), i;
   if (r) {
-    let [u, l] = tu(e[0], e[1]);
+    let [u, l] = nu(e[0], e[1]);
     i = u * l;
   } else {
     let [u, l] = Yl(e[0], e[1]);
     i = u * l;
   }
-  let o = kX(n, a);
+  let o = RX(n, a);
   return i * o;
 }
-function IX(e, t) {
+function DX(e, t) {
   switch (e) {
     case 3:
-      return vv(t);
+      return wv(t);
     case 4:
-      return xv(t);
+      return kv(t);
     case 1:
-      return gv(t);
-    case 0:
-      return bv(t);
-    case 2:
       return yv(t);
+    case 0:
+      return vv(t);
+    case 2:
+      return xv(t);
     default:
       throw new Error(`Unknown physical texture type ${e}`);
   }
 }
-function SX(e) {
+function FX(e) {
   return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? e ? 3 : 1 : e ? 4 : 0;
 }
-function iw(e, t) {
+function ow(e, t) {
   if (e === 1)
     return 3;
   if (e === 0 || e == null)
-    return SX(t);
+    return FX(t);
   if (e === 3 || e === 2)
     return 2;
   throw new Error(`Unknown logical texture type ${e}`);
 }
-function ow(e, t, n) {
+function uw(e, t, n) {
   return `${e[0]}_${e[1]}_${t}_${n}`;
 }
 var Hs = class {
@@ -25579,19 +25637,19 @@ var Hs = class {
   }
 };
 var ss = "if (isnan(x)) return x;";
-var CX = "return x;";
-var uw = "return abs(x);";
-var NX = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
-var TX = ss + `
+var OX = "return x;";
+var lw = "return abs(x);";
+var PX = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+var zX = ss + `
   return (x < 0.0) ? 0.0 : x;
 `;
-var $X = ss + `
+var MX = ss + `
   return (x < 0.0) ? 0.0 : min(6.0, x);
 `;
-var Mi = "return x;";
-var _X = "return 1.0 / (1.0 + exp(-1.0 * x));";
-var AX = "return x;";
-var EX = `
+var Bi = "return x;";
+var LX = "return 1.0 / (1.0 + exp(-1.0 * x));";
+var BX = "return x;";
+var VX = `
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -25601,7 +25659,7 @@ var EX = `
 
   return result;
 `;
-var RX = `
+var WX = `
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -25612,7 +25670,7 @@ var RX = `
 
   return result;
 `;
-var DX = `
+var UX = `
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -25623,8 +25681,8 @@ var DX = `
 
   return result;
 `;
-var FX = "return 1.0 / (1.0 + exp(-1.0 * x));";
-var Zr = class {
+var GX = "return 1.0 / (1.0 + exp(-1.0 * x));";
+var Jr = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       vec4 unaryOperation(vec4 x) {
@@ -25640,10 +25698,10 @@ var Zr = class {
     `;
   }
 };
-var OX = class {
+var HX = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
-    let t = e.length, n = ln("rc", t), s = rt(t), r = yX(t, n), a = n.slice(-2), i = t <= 1 ? "rc" : `vec2(${a.join(",")})`;
+    let t = e.length, n = ln("rc", t), s = rt(t), r = $X(t, n), a = n.slice(-2), i = t <= 1 ? "rc" : `vec2(${a.join(",")})`;
     this.userCode = `
       void main() {
         ${s} rc = getOutputCoords();
@@ -25654,39 +25712,39 @@ var OX = class {
     `;
   }
 };
-var PX = xs.whereImpl;
-var zX = 1e-7;
-var MX = 1e-4;
+var qX = xs.whereImpl;
+var jX = 1e-7;
+var KX = 1e-4;
 var Zc = {};
-function LX(e) {
+function XX(e) {
   return e in Zc || (Zc[e] = {}), Zc[e];
 }
-var BX = X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
-var VX = 600;
-function WX() {
-  return X().global.screen == null ? 1024 : X().global.screen.height * X().global.screen.width * window.devicePixelRatio * VX / 1024 / 1024;
+var YX = X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
+var QX = 600;
+function ZX() {
+  return X().global.screen == null ? 1024 : X().global.screen.height * X().global.screen.width * window.devicePixelRatio * QX / 1024 / 1024;
 }
-var z1 = class extends nl {
+var W1 = class extends sl {
   constructor(e) {
     if (super(), this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !X().getBool("HAS_WEBGL"))
       throw new Error("WebGL is not supported on this device");
     let t;
     if (e != null) {
-      if (e instanceof Kf)
+      if (e instanceof Xf)
         t = e;
       else {
         let n = vs(X().getNumber("WEBGL_VERSION"), e);
-        t = new Kf(n);
+        t = new Xf(n);
       }
       this.binaryCache = {}, this.gpgpuCreatedLocally = false;
     } else {
       let n = vs(X().getNumber("WEBGL_VERSION"));
-      t = new Kf(n), this.binaryCache = LX(X().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
+      t = new Xf(n), this.binaryCache = XX(X().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
     }
-    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new wX(this.gpgpu), this.numMBBeforeWarning = WX(), this.texData = new Ud(this, Ss());
+    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new EX(this.gpgpu), this.numMBBeforeWarning = ZX(), this.texData = new Hd(this, Ss());
   }
   nextDataId() {
-    return z1.nextDataId++;
+    return W1.nextDataId++;
   }
   numDataIds() {
     return this.texData.numDataIds() - this.pendingDeletes;
@@ -25722,7 +25780,7 @@ var z1 = class extends nl {
     let t = this.texData.get(e), { values: n, dtype: s, complexTensorInfos: r, slice: a, shape: i, isPacked: o } = t;
     if (a != null) {
       let p;
-      o ? p = new Zr(i, Mi) : p = new Hs(i, Mi);
+      o ? p = new Jr(i, Bi) : p = new Hs(i, Bi);
       let d = this.runWebGLProgram(p, [{ dataId: e, shape: i, dtype: s }], s), h = this.readSync(d.dataId);
       return this.disposeIntermediateTensorInfo(d), h;
     }
@@ -25748,7 +25806,7 @@ var z1 = class extends nl {
     let t = this.texData.get(e), { values: n, shape: s, slice: r, dtype: a, complexTensorInfos: i, isPacked: o } = t;
     if (r != null) {
       let h;
-      o ? h = new Zr(s, Mi) : h = new Hs(s, Mi);
+      o ? h = new Jr(s, Bi) : h = new Hs(s, Bi);
       let f = this.runWebGLProgram(h, [{ dataId: e, shape: s, dtype: a }], a), m = this.read(f.dataId);
       return this.disposeIntermediateTensorInfo(f), m;
     }
@@ -25786,7 +25844,7 @@ var z1 = class extends nl {
       throw new Error("Does not support reading texture for complex64 dtype.");
     if (a != null) {
       let d;
-      o ? d = new Zr(r, Mi) : d = new Hs(r, Mi);
+      o ? d = new Jr(r, Bi) : d = new Hs(r, Bi);
       let h = this.runWebGLProgram(d, [{ dataId: e, shape: r, dtype: i }], i), f = this.readToGPU(h, t);
       return this.disposeIntermediateTensorInfo(h), f;
     }
@@ -25809,7 +25867,7 @@ var z1 = class extends nl {
     if (e != null)
       for (let t = 0; t < e.length; t++) {
         let n = e[t];
-        if (!VC(n))
+        if (!qC(n))
           throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE") ? Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`) : Error(`The value ${n} cannot be represented on this device.`);
       }
   }
@@ -25819,7 +25877,7 @@ var z1 = class extends nl {
       let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Yc(t)).subarray(0, r);
       return this.disposeIntermediateTensorInfo(p), h;
     }
-    let a = X().getBool("WEBGL_PACK") && s === true, i = a ? id(t) : t, o = a ? new _K(i) : new $K(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
+    let a = X().getBool("WEBGL_PACK") && s === true, i = a ? id(t) : t, o = a ? new LK(i) : new MK(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
     return this.disposeIntermediateTensorInfo(u), c;
   }
   timerAvailable() {
@@ -25880,7 +25938,7 @@ var z1 = class extends nl {
   getDataInfo(e) {
     return this.texData.get(e);
   }
-  shouldExecuteOnCPU(e, t = BX) {
+  shouldExecuteOnCPU(e, t = YX) {
     return X().getBool("WEBGL_CPU_FORWARD") && e.every((n) => this.texData.get(n.dataId).texture == null && w.sizeFromShape(n.shape) < t);
   }
   getGPGPUContext() {
@@ -25889,20 +25947,20 @@ var z1 = class extends nl {
   where(e) {
     S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
     let t = e.dataSync();
-    return PX(e.shape, t);
+    return qX(e.shape, t);
   }
   packedUnaryOp(e, t, n) {
-    let s = new Zr(e.shape, t), r = this.compileAndRun(s, [e], n);
+    let s = new Jr(e.shape, t), r = this.compileAndRun(s, [e], n);
     return Ss().makeTensorFromDataId(r.dataId, r.shape, r.dtype);
   }
   abs(e) {
     if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
-      let s = D1(this.texData.get(e.dataId).values);
+      let s = M1(this.texData.get(e.dataId).values);
       return this.makeOutput(e.shape, e.dtype, s);
     }
     if (X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
-      return this.packedUnaryOp(e, uw, e.dtype);
-    let t = new Hs(e.shape, uw), n = this.compileAndRun(t, [e]);
+      return this.packedUnaryOp(e, lw, e.dtype);
+    let t = new Hs(e.shape, lw), n = this.compileAndRun(t, [e]);
     return Ss().makeTensorFromDataId(n.dataId, n.shape, n.dtype);
   }
   makeTensorInfo(e, t, n) {
@@ -25919,15 +25977,15 @@ var z1 = class extends nl {
     return Ss().makeTensorFromDataId(s, e, t, this);
   }
   unpackTensor(e) {
-    let t = new OX(e.shape);
+    let t = new HX(e.shape);
     return this.runWebGLProgram(t, [e], e.dtype);
   }
   packTensor(e) {
-    let t = new vX(e.shape), n = true;
+    let t = new _X(e.shape), n = true;
     return this.runWebGLProgram(t, [e], e.dtype, null, n);
   }
   packedReshape(e, t) {
-    let n = [ba(e.shape), ...ya(e.shape)], s = { dtype: e.dtype, shape: n, dataId: e.dataId }, r = [ba(t), ...ya(t)], a = new P1(r, n), i = true, o = [n], u = this.runWebGLProgram(a, [s], e.dtype, o, i);
+    let n = [ya(e.shape), ...va(e.shape)], s = { dtype: e.dtype, shape: n, dataId: e.dataId }, r = [ya(t), ...va(t)], a = new V1(r, n), i = true, o = [n], u = this.runWebGLProgram(a, [s], e.dtype, o, i);
     return { dataId: u.dataId, shape: t, dtype: u.dtype };
   }
   decode(e, t) {
@@ -25937,7 +25995,7 @@ var z1 = class extends nl {
       w.assert(p <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
     }
     let i = id(r), o;
-    s ? o = new TK(i) : o = new NK(i);
+    s ? o = new zK(i) : o = new PK(i);
     let u = true, l = [t != null ? t : Yc(i)], c = this.runWebGLProgram(o, [{ shape: i, dtype: a, dataId: e }], a, l, u, t);
     return { dtype: a, shape: r, dataId: c.dataId };
   }
@@ -25960,15 +26018,15 @@ var z1 = class extends nl {
       }
       if (this.uploadToGPU(g.dataId), !!b.isPacked != !!e.packedInputs)
         g = b.isPacked ? this.unpackTensor(g) : this.packTensor(g), u.push(g), b = this.texData.get(g.dataId);
-      else if (b.isPacked && !el(b.shape, g.shape)) {
+      else if (b.isPacked && !tl(b.shape, g.shape)) {
         let y = g, v = g.shape;
         g.shape = b.shape, g = this.packedReshape(g, v), u.push(g), b = this.texData.get(g.dataId), y.shape = v;
       }
       return { shape: g.shape, texData: b, isUniform: false };
     });
     this.uploadToGPU(i.dataId);
-    let c = { shape: i.shape, texData: o, isUniform: false }, p = CK(e, l, c), d = this.getAndSaveBinary(p, () => IK(this.gpgpu, e, l, c)), h = this.activeTimers != null, f;
-    h && (f = this.startTimer()), X().get("ENGINE_COMPILE_ONLY") || SK(this.gpgpu, d, l, c, s), u.forEach((g) => this.disposeIntermediateTensorInfo(g)), h && (f = this.endTimer(f), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(f) }));
+    let c = { shape: i.shape, texData: o, isUniform: false }, p = OK(e, l, c), d = this.getAndSaveBinary(p, () => DK(this.gpgpu, e, l, c)), h = this.activeTimers != null, f;
+    h && (f = this.startTimer()), X().get("ENGINE_COMPILE_ONLY") || FK(this.gpgpu, d, l, c, s), u.forEach((g) => this.disposeIntermediateTensorInfo(g)), h && (f = this.endTimer(f), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(f) }));
     let m = X().get("WEBGL_FLUSH_THRESHOLD");
     if (m > 0) {
       let g = w.now();
@@ -26007,7 +26065,7 @@ var z1 = class extends nl {
     })), this.floatPrecisionValue;
   }
   epsilon() {
-    return this.floatPrecision() === 32 ? zX : MX;
+    return this.floatPrecision() === 32 ? jX : KX;
   }
   uploadToGPU(e) {
     let t = this.texData.get(e), { shape: n, dtype: s, values: r, texture: a, usage: i, isPacked: o } = t;
@@ -26016,9 +26074,9 @@ var z1 = class extends nl {
     let u = this.activeTimers != null, l;
     u && (l = w.now());
     let c = t.texShape;
-    if (c == null && (c = r1(n, o), t.texShape = c), r != null) {
+    if (c == null && (c = l1(n, o), t.texShape = c), r != null) {
       let p = id(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
-      (o || !m) && ([h, f] = tu(c[0], c[1])), o ? d = new EK(p, m) : d = new AK(p, m);
+      (o || !m) && ([h, f] = nu(c[0], c[1])), o ? d = new VK(p, m) : d = new BK(p, m);
       let g = m ? [f, h] : c, b = this.makeTensorInfo(g, s), y = this.texData.get(b.dataId);
       m ? y.usage = 2 : y.usage = 1, y.texShape = g, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), h, f, r);
       let v = [[f, h]], x = true, k = this.runWebGLProgram(d, [b], s, v, x), T = this.texData.get(k.dataId);
@@ -26030,7 +26088,7 @@ var z1 = class extends nl {
   }
   convertAndCacheOnCPU(e, t) {
     let n = this.texData.get(e), { dtype: s } = n;
-    return this.releaseGPUData(e), t != null && (n.values = UX(t, s)), n.values;
+    return this.releaseGPUData(e), t != null && (n.values = JX(t, s)), n.values;
   }
   acquireTexture(e, t, n, s) {
     if (this.numBytesInGPU += this.computeBytes(e, n), !this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
@@ -26067,23 +26125,23 @@ var z1 = class extends nl {
     }
   }
   async checkCompletionAsync_(e) {
-    return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await OI(), this.checkCompletionAsync_(e));
+    return this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR) ? this.checkCompletion_(e) : (await LI(), this.checkCompletionAsync_(e));
   }
   checkCompletion_(e) {
     if (this.gpgpu.gl.getProgramParameter(e.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false)
-      throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (pv(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders.");
+      throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)), this.gpgpu.gl.getShaderParameter(e.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false ? (fv(e.source, this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)), new Error("Failed to compile fragment shader.")) : new Error("Failed to link vertex and fragment shaders.");
     return true;
   }
   getUniformLocations() {
     for (let [, e] of Object.entries(this.binaryCache)) {
-      let { uniformLocations: t, customUniformLocations: n, infLoc: s, nanLoc: r, inShapesLocations: a, inTexShapesLocations: i, outShapeLocation: o, outShapeStridesLocation: u, outTexShapeLocation: l } = m1(this.gpgpu, e.program, e.webGLProgram);
+      let { uniformLocations: t, customUniformLocations: n, infLoc: s, nanLoc: r, inShapesLocations: a, inTexShapesLocations: i, outShapeLocation: o, outShapeStridesLocation: u, outTexShapeLocation: l } = x1(this.gpgpu, e.program, e.webGLProgram);
       e.uniformLocations = t, e.customUniformLocations = n, e.infLoc = s, e.nanLoc = r, e.inShapesLocations = a, e.inTexShapesLocations = i, e.outShapeLocation = o, e.outShapeStridesLocation = u, e.outTexShapeLocation = l;
     }
   }
 };
-var M1 = z1;
-M1.nextDataId = 0;
-function UX(e, t) {
+var U1 = W1;
+U1.nextDataId = 0;
+function JX(e, t) {
   if (t === "float32" || t === "complex64")
     return e;
   if (t === "int32" || t === "bool") {
@@ -26094,17 +26152,17 @@ function UX(e, t) {
   } else
     throw new Error(`Unknown dtype ${t}`);
 }
-var Ppe = "0.0.0";
-function GX() {
+var Ype = "0.0.0";
+function e8() {
   X().set("WEBGL_FORCE_F16_TEXTURES", true);
 }
-dp.isBrowser() && pp("webgl", () => new M1(), 2);
-var zpe = { forceHalfFloat: GX };
-var L1 = `
+hp.isBrowser() && fp("webgl", () => new U1(), 2);
+var Qpe = { forceHalfFloat: e8 };
+var G1 = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `;
-var ro = class {
+var io = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.enableShapeUniforms = Sn(this.outputShape.length), this.userCode = `
       float binaryOperation(float a, float b) {
@@ -26119,7 +26177,7 @@ var ro = class {
     `;
   }
 };
-var jp = `
+var Xp = `
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
@@ -26191,38 +26249,38 @@ function kn(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   return n.incRef(s.dataId), { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-var HX = { kernelName: Ba, backendName: "webgl", kernelFunc: kn };
-function Rr(e) {
+var t8 = { kernelName: Wa, backendName: "webgl", kernelFunc: kn };
+function Dr(e) {
   let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.texData.get(a.dataId), o = kn({ inputs: { x: s }, backend: n }), u = kn({ inputs: { x: r }, backend: n });
   return i.complexTensorInfos = { real: o, imag: u }, a;
 }
-var qX = { kernelName: jd, backendName: "webgl", kernelFunc: Rr };
-var B1 = "return (a < 0.) ? b * a : a;";
-var V1 = `
+var n8 = { kernelName: Xd, backendName: "webgl", kernelFunc: Dr };
+var H1 = "return (a < 0.) ? b * a : a;";
+var q1 = `
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
-function jX(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(V1, r.shape, i.shape) : new ro(B1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
+function s8(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(q1, r.shape, i.shape) : new io(H1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
   return n.disposeIntermediateTensorInfo(i), u;
 }
-var KX = { kernelName: Va, backendName: "webgl", kernelFunc: jX };
-var W1 = "return (a < 0.) ? b * a : a;";
-var U1 = `
+var r8 = { kernelName: Ua, backendName: "webgl", kernelFunc: s8 };
+var j1 = "return (a < 0.) ? b * a : a;";
+var K1 = `
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
-function XX(e) {
-  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(U1, s.shape, r.shape) : new ro(W1, s.shape, r.shape);
+function a8(e) {
+  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(K1, s.shape, r.shape) : new io(j1, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], "float32");
 }
-var YX = { kernelName: Ja, backendName: "webgl", kernelFunc: XX };
-var ou = "if (isnan(x)) return x;";
-var QX = `
+var i8 = { kernelName: ti, backendName: "webgl", kernelFunc: a8 };
+var uu = "if (isnan(x)) return x;";
+var o8 = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `;
-var ZX = `
+var u8 = `
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
@@ -26236,7 +26294,7 @@ function Ke({ opSnippet: e, packedOpSnippet: t, cpuKernelImpl: n, dtype: s }) {
       return o.makeTensorInfo(i.shape, u, d);
     }
     let l = X().getBool("WEBGL_PACK_UNARY_OPERATIONS") && t != null, c;
-    return l ? c = new Zr(i.shape, t) : c = new Hs(i.shape, e), o.runWebGLProgram(c, [i], u);
+    return l ? c = new Jr(i.shape, t) : c = new Hs(i.shape, e), o.runWebGLProgram(c, [i], u);
   };
 }
 function jt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, supportsComplex: s = false, cpuKernelImpl: r, dtype: a }) {
@@ -26244,9 +26302,9 @@ function jt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, sup
     let { a: u, b: l } = i, c = o;
     if (s && u.dtype === "complex64") {
       let f = c.texData.get(u.dataId), m = c.texData.get(l.dataId), [g, b] = [[f.complexTensorInfos.real, m.complexTensorInfos.real], [f.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((v) => {
-        let [x, k] = v, T = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, N = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new ro(e, u.shape, l.shape);
+        let [x, k] = v, T = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, N = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new io(e, u.shape, l.shape);
         return c.runWebGLProgram(E, [T, N], cn(x.dtype, k.dtype));
-      }), y = Rr({ inputs: { real: g, imag: b }, backend: c });
+      }), y = Dr({ inputs: { real: g, imag: b }, backend: c });
       return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(b), y;
     }
     let p = a || cn(u.dtype, l.dtype);
@@ -26255,27 +26313,27 @@ function jt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, sup
       return k.values = y, x;
     }
     let d = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") && t != null, h;
-    return d ? h = new Zl(t, u.shape, l.shape, n) : h = new ro(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
+    return d ? h = new Zl(t, u.shape, l.shape, n) : h = new io(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
   };
 }
-function Kp(e, t = false) {
+function Yp(e, t = false) {
   if (e === "linear")
-    return t ? AX : CX;
+    return t ? BX : OX;
   if (e === "relu")
-    return t ? RX : TX;
+    return t ? WX : zX;
   if (e === "elu")
-    return t ? EX : NX;
+    return t ? VX : PX;
   if (e === "relu6")
-    return t ? DX : $X;
+    return t ? UX : MX;
   if (e === "prelu")
-    return t ? U1 : W1;
+    return t ? K1 : j1;
   if (e === "leakyrelu")
-    return t ? V1 : B1;
+    return t ? q1 : H1;
   if (e === "sigmoid")
-    return t ? FX : _X;
+    return t ? GX : LX;
   throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`);
 }
-var G1 = class {
+var X1 = class {
   constructor(e, t, n, s = false, r = false, a = false, i = null, o = false, u = false) {
     this.variableNames = ["matrixA", "matrixB"], this.packedInputs = true, this.packedOutput = true, this.outputShape = n, this.enableShapeUniforms = Sn(this.outputShape.length);
     let l = s ? e[1] : e[2], c = Math.ceil(l / 2), p = s ? "i * 2, rc.y" : "rc.y, i * 2", d = r ? "rc.z, i * 2" : "i * 2, rc.z", h = s ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"], f = r ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"], m = "", g = "";
@@ -26325,8 +26383,8 @@ var G1 = class {
     `;
   }
 };
-var lw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
-var cw = class {
+var cw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
+var dw = class {
   constructor(e, t, n) {
     this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.userCode = `
       float binaryOpComplex(
@@ -26344,33 +26402,33 @@ var cw = class {
     `;
   }
 };
-var dw = "return a * b;";
-function kv(e) {
+var pw = "return a * b;";
+function Sv(e) {
   let { inputs: t, backend: n } = e, { a: s, b: r } = t, a = S.upcastType(s.dtype, r.dtype);
   if (s.dtype === "complex64") {
-    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new cw(lw.REAL, s.shape, r.shape), c = new cw(lw.IMAG, s.shape, r.shape), p = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: s.shape }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: s.shape }, { dataId: u.complexTensorInfos.real.dataId, dtype: u.complexTensorInfos.real.dtype, shape: r.shape }, { dataId: u.complexTensorInfos.imag.dataId, dtype: u.complexTensorInfos.imag.dtype, shape: r.shape }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Rr({ inputs: { real: d, imag: h }, backend: n });
+    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new dw(cw.REAL, s.shape, r.shape), c = new dw(cw.IMAG, s.shape, r.shape), p = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: s.shape }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: s.shape }, { dataId: u.complexTensorInfos.real.dataId, dtype: u.complexTensorInfos.real.dtype, shape: r.shape }, { dataId: u.complexTensorInfos.imag.dataId, dtype: u.complexTensorInfos.imag.dtype, shape: r.shape }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Dr({ inputs: { real: d, imag: h }, backend: n });
     return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
   }
   if (n.shouldExecuteOnCPU([s, r])) {
-    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), [l, c] = JK(s.shape, r.shape, o.values, u.values, a), p = n.makeTensorInfo(c, a), d = n.texData.get(p.dataId);
+    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), [l, c] = lX(s.shape, r.shape, o.values, u.values, a), p = n.makeTensorInfo(c, a), d = n.texData.get(p.dataId);
     return d.values = l, p;
   }
   let i;
-  return X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Zl(dw, s.shape, r.shape) : i = new ro(dw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
+  return X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Zl(pw, s.shape, r.shape) : i = new io(pw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
 }
-var JX = { kernelName: Ya, backendName: "webgl", kernelFunc: kv };
-function e8(e, t, n) {
-  let s = [ba(e.shape), ...ya(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [ba(t), ...ya(t)], i = new P1(a, s), o = true, u = [s], l = n.runWebGLProgram(i, [r], e.dtype, u, o);
+var l8 = { kernelName: Za, backendName: "webgl", kernelFunc: Sv };
+function c8(e, t, n) {
+  let s = [ya(e.shape), ...va(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [ya(t), ...va(t)], i = new V1(a, s), o = true, u = [s], l = n.runWebGLProgram(i, [r], e.dtype, u, o);
   return { dataId: l.dataId, shape: t, dtype: l.dtype };
 }
 function he(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = n, o = w.sizeFromShape(r.shape), u = w.inferFromImplicitShape(a, o), l = w.sizeFromShape(u);
   w.assert(o === l, () => `The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);
   let c = i.texData.get(r.dataId);
-  return c.isPacked && !el(r.shape, u) && !(c.texture !== null && el(c.shape, u)) ? e8(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
+  return c.isPacked && !tl(r.shape, u) && !(c.texture !== null && tl(c.shape, u)) ? c8(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
 }
-var t8 = { kernelName: Eo, backendName: "webgl", kernelFunc: he };
-var pw = class {
+var d8 = { kernelName: Ro, backendName: "webgl", kernelFunc: he };
+var hw = class {
   constructor(e, t) {
     this.variableNames = ["x"];
     let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
@@ -26437,7 +26495,7 @@ var pw = class {
     `;
   }
 };
-var n8 = class {
+var p8 = class {
   constructor(e, t) {
     this.variableNames = ["x"];
     let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
@@ -26544,7 +26602,7 @@ var n8 = class {
     `;
   }
 };
-function s8(e) {
+function h8(e) {
   let t = [];
   for (; t.length === 0 || t[t.length - 1].outSize !== 1; ) {
     let n = t.length ? t[t.length - 1].outSize : e[1], s = S.computeOptimalWindowSize(n);
@@ -26552,22 +26610,22 @@ function s8(e) {
   }
   return t;
 }
-function xi(e, t, n, s) {
-  let r = s8(e.shape), a = e;
+function Ii(e, t, n, s) {
+  let r = h8(e.shape), a = e;
   for (let i = 0; i < r.length; i++) {
     let { inSize: o, windowSize: u, outSize: l } = r[i], c, p;
-    n === "mean" ? c = i === 0 ? new pw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new pw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new n8({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, n), p = a, a = s.runWebGLProgram(c, [a], t), p.dataId !== e.dataId && s.disposeIntermediateTensorInfo(p);
+    n === "mean" ? c = i === 0 ? new hw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new hw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new p8({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, n), p = a, a = s.runWebGLProgram(c, [a], t), p.dataId !== e.dataId && s.disposeIntermediateTensorInfo(p);
   }
   return a;
 }
-var r8 = class {
+var f8 = class {
   constructor(e, t) {
     this.variableNames = ["A"];
     let n = new Array(e.length);
     for (let a = 0; a < n.length; a++)
       n[a] = e[t[a]];
     this.outputShape = n, this.rank = n.length;
-    let s = rt(this.rank), r = a8(t);
+    let s = rt(this.rank), r = m8(t);
     this.userCode = `
     void main() {
       ${s} resRC = getOutputCoords();
@@ -26576,7 +26634,7 @@ var r8 = class {
     `;
   }
 };
-function a8(e) {
+function m8(e) {
   let t = e.length;
   if (t > 6)
     throw Error(`Transpose for rank ${t} is not yet supported`);
@@ -26585,7 +26643,7 @@ function a8(e) {
     s[e[r]] = n[r];
   return s.join();
 }
-var i8 = class {
+var g8 = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true;
     let n = new Array(e.length);
@@ -26593,7 +26651,7 @@ var i8 = class {
       n[l] = e[t[l]];
     if (this.outputShape = n, this.rank = n.length, this.rank > 6)
       throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
-    let s = rt(this.rank), r = O1("rc", this.rank), a = new Array(this.rank);
+    let s = rt(this.rank), r = B1("rc", this.rank), a = new Array(this.rank);
     for (let l = 0; l < t.length; l++)
       a[t[l]] = r[l];
     let i = `vec2(${a.slice(-2).join()})`, o = `++${r[this.rank - 1]} < ${n[this.rank - 1]}`, u = `getChannel(getA(${a.join()}), ${i})`;
@@ -26617,54 +26675,54 @@ var i8 = class {
     `;
   }
 };
-function Xp(e, t, n) {
-  let s = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new i8(e.shape, t) : new r8(e.shape, t);
+function Qp(e, t, n) {
+  let s = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new g8(e.shape, t) : new f8(e.shape, t);
   return n.runWebGLProgram(s, [e], e.dtype);
 }
-function o8(e, t, n, s) {
+function b8(e, t, n, s) {
   let r = t, a = e.shape.length, i = w.parseAxisParam(r, e.shape), o = i, u = S.getAxesPermutation(o, a), l = u != null, c = e;
-  l && (c = Xp(e, u, s), o = S.getInnerMostAxes(o.length, a)), S.assertAxesAreInnerMostDims("sum", o, a);
+  l && (c = Qp(e, u, s), o = S.getInnerMostAxes(o.length, a)), S.assertAxesAreInnerMostDims("sum", o, a);
   let [p, d] = S.computeOutAndReduceShapes(c.shape, o), h = p;
   n && (h = S.expandShapeToKeepDim(p, i));
-  let f = w.sizeFromShape(d), g = w.sizeFromShape(e.shape) / f, b = he({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: s }), y = cp(e.dtype), v = xi(b, y, "sum", s), x = he({ inputs: { x: v }, attrs: { shape: h }, backend: s });
+  let f = w.sizeFromShape(d), g = w.sizeFromShape(e.shape) / f, b = he({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: s }), y = pp(e.dtype), v = Ii(b, y, "sum", s), x = he({ inputs: { x: v }, attrs: { shape: h }, backend: s });
   return s.disposeIntermediateTensorInfo(b), s.disposeIntermediateTensorInfo(v), l && s.disposeIntermediateTensorInfo(c), x;
 }
-function Yp(e) {
+function Zp(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return o8(r, a, i, n);
+  return b8(r, a, i, n);
 }
-var u8 = { kernelName: oi, backendName: "webgl", kernelFunc: Yp };
+var y8 = { kernelName: ci, backendName: "webgl", kernelFunc: Zp };
 function qt(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, i = n, o = r.shape.length, u = new Array(o);
   for (let c = 0; c < u.length; c++)
     u[c] = r.shape[a[c]];
   let l;
   if (i.shouldExecuteOnCPU([r])) {
-    let p = i.texData.get(r.dataId).values, d = wv(p, r.shape, r.dtype, a, u);
+    let p = i.texData.get(r.dataId).values, d = Iv(p, r.shape, r.dtype, a, u);
     l = i.makeTensorInfo(u, r.dtype);
     let h = i.texData.get(l.dataId);
     h.values = d;
   } else
-    l = Xp(r, a, i);
+    l = Qp(r, a, i);
   return l;
 }
-var l8 = { kernelName: pi, backendName: "webgl", kernelFunc: qt };
-var H1 = 1e3;
-function zd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = jo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+var v8 = { kernelName: mi, backendName: "webgl", kernelFunc: qt };
+var Y1 = 1e3;
+function Ld({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Ko.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
-  let k = n ? [b, p, h] : [b, h, p], T = s ? [y, f, d] : [y, d, f], N = he({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = he({ inputs: { x: t }, backend: r, attrs: { shape: T } }), A = [N, E], P = Math.max(b, y), R = n ? N.shape[1] : N.shape[2], F = a != null, $ = i != null, z = u === "leakyrelu", W = u != null ? Kp(u, true) : null, q = F || $ || z || W != null, K;
-  if ((h === 1 || f === 1) && R > H1 && q === false) {
+  let k = n ? [b, p, h] : [b, h, p], T = s ? [y, f, d] : [y, d, f], N = he({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = he({ inputs: { x: t }, backend: r, attrs: { shape: T } }), A = [N, E], P = Math.max(b, y), R = n ? N.shape[1] : N.shape[2], F = a != null, $ = i != null, z = u === "leakyrelu", W = u != null ? Yp(u, true) : null, q = F || $ || z || W != null, K;
+  if ((h === 1 || f === 1) && R > Y1 && q === false) {
     let Z = N, te = E;
     n && (Z = qt({ inputs: { x: N }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(Z)), s && (te = qt({ inputs: { x: E }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(te));
     let ee = f !== 1, se = f === 1, ne = Z;
     ee && (ne = he({ inputs: { x: Z }, backend: r, attrs: { shape: [P, R, 1] } }), A.push(ne));
     let oe = f === 1 ? 2 : 1, re = te;
     se && (re = he({ inputs: { x: te }, backend: r, attrs: { shape: [P, 1, R] } }), A.push(re));
-    let le = kv({ inputs: { a: ne, b: re }, backend: r });
-    K = Yp({ inputs: { x: le }, backend: r, attrs: { axis: oe, keepDims: true } }), A.push(le);
+    let le = Sv({ inputs: { a: ne, b: re }, backend: r });
+    K = Zp({ inputs: { x: le }, backend: r, attrs: { axis: oe, keepDims: true } }), A.push(le);
   } else {
-    let Z = cn(e.dtype, t.dtype), te = new G1(k, T, [P, h, f], n, s, F, W, $, z), ee = [N, E];
+    let Z = cn(e.dtype, t.dtype), te = new X1(k, T, [P, h, f], n, s, F, W, $, z), ee = [N, E];
     if (a != null && ee.push(a), $ && ee.push(i), z) {
       let se = r.makeTensorInfo([], "float32", w.createScalarValue(o, "float32"));
       ee.push(se), A.push(se);
@@ -26677,39 +26735,39 @@ function zd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = nu
     r.disposeIntermediateTensorInfo(Z);
   return Y;
 }
-function c8(e) {
+function x8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
-  return zd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
+  return Ld({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
 }
-var d8 = { kernelName: ra, backendName: "webgl", kernelFunc: c8 };
-var hw = "return abs(x);";
-function p8(e) {
+var w8 = { kernelName: aa, backendName: "webgl", kernelFunc: x8 };
+var fw = "return abs(x);";
+function k8(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s]) && s.dtype !== "complex64") {
-    let a = n.texData.get(s.dataId), i = D1(a.values);
+    let a = n.texData.get(s.dataId), i = M1(a.values);
     return n.makeTensorInfo(s.shape, s.dtype, i);
   }
   let r;
-  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Zr(s.shape, hw) : r = new Hs(s.shape, hw), n.runWebGLProgram(r, [s], s.dtype);
+  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Jr(s.shape, fw) : r = new Hs(s.shape, fw), n.runWebGLProgram(r, [s], s.dtype);
 }
-var h8 = { kernelName: io, backendName: "webgl", kernelFunc: p8 };
-var f8 = ss + `
+var I8 = { kernelName: uo, backendName: "webgl", kernelFunc: k8 };
+var S8 = ss + `
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
 `;
-var m8 = Ke({ opSnippet: f8 });
-var g8 = { kernelName: sl, backendName: "webgl", kernelFunc: m8 };
-var b8 = ss + `
+var C8 = Ke({ opSnippet: S8 });
+var N8 = { kernelName: rl, backendName: "webgl", kernelFunc: C8 };
+var T8 = ss + `
   if (x < 1.0) return NAN;
 return log(x + sqrt(x * x - 1.0));`;
-var y8 = Ke({ opSnippet: b8 });
-var v8 = { kernelName: rl, backendName: "webgl", kernelFunc: y8 };
-var fw = "return a + b;";
-var x8 = jt({ opSnippet: fw, packedOpSnippet: fw, supportsComplex: true, cpuKernelImpl: FK });
-var w8 = { kernelName: Ir, backendName: "webgl", kernelFunc: x8 };
-var k8 = class {
+var $8 = Ke({ opSnippet: T8 });
+var _8 = { kernelName: al, backendName: "webgl", kernelFunc: $8 };
+var mw = "return a + b;";
+var A8 = jt({ opSnippet: mw, packedOpSnippet: mw, supportsComplex: true, cpuKernelImpl: GK });
+var E8 = { kernelName: Sr, backendName: "webgl", kernelFunc: A8 };
+var R8 = class {
   constructor(e, t) {
     this.outputShape = [], this.outputShape = e, this.variableNames = t.map((r, a) => `T${a}`);
     let n = [];
@@ -26728,7 +26786,7 @@ var k8 = class {
     `;
   }
 };
-var I8 = class {
+var D8 = class {
   constructor(e, t) {
     this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t.map((r, a) => `T${a}`);
     let n = [];
@@ -26755,14 +26813,14 @@ function ld(e) {
     let u = Math.floor(s.length / 2), l = ld({ inputs: s.slice(0, u), backend: n }), c = ld({ inputs: s.slice(u), backend: n });
     return ld({ inputs: [l, c], backend: n });
   }
-  let r = s.map((u) => u.dtype).reduce((u, l) => cn(u, l)), a = s.map((u) => u.shape), o = X().getBool("WEBGL_PACK") ? new I8(s[0].shape, a) : new k8(s[0].shape, a);
+  let r = s.map((u) => u.dtype).reduce((u, l) => cn(u, l)), a = s.map((u) => u.shape), o = X().getBool("WEBGL_PACK") ? new D8(s[0].shape, a) : new R8(s[0].shape, a);
   return n.runWebGLProgram(o, s, r);
 }
-var S8 = { kernelName: ka, backendName: "webgl", kernelFunc: ld };
-function C8(e) {
+var F8 = { kernelName: Ia, backendName: "webgl", kernelFunc: ld };
+function O8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = S.getAxesPermutation(l, o), p = r;
   c != null && (p = qt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = S.getInnerMostAxes(l.length, o)), S.assertAxesAreInnerMostDims("all", l, o);
-  let [d, h] = S.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = xi(m, m.dtype, "all", n), b;
+  let [d, h] = S.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = Ii(m, m.dtype, "all", n), b;
   if (i) {
     let y = S.expandShapeToKeepDim(d, u);
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: y } });
@@ -26770,11 +26828,11 @@ function C8(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var N8 = { kernelName: al, backendName: "webgl", kernelFunc: C8 };
-function T8(e) {
+var P8 = { kernelName: il, backendName: "webgl", kernelFunc: O8 };
+function z8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = S.getAxesPermutation(l, o), p = r;
   c != null && (p = qt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = S.getInnerMostAxes(l.length, o)), S.assertAxesAreInnerMostDims("any", l, o);
-  let [d, h] = S.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = xi(m, m.dtype, "any", n), b;
+  let [d, h] = S.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = Ii(m, m.dtype, "any", n), b;
   if (i) {
     let y = S.expandShapeToKeepDim(d, u);
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: y } });
@@ -26782,8 +26840,8 @@ function T8(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var $8 = { kernelName: il, backendName: "webgl", kernelFunc: T8 };
-var _8 = class {
+var M8 = { kernelName: ol, backendName: "webgl", kernelFunc: z8 };
+var L8 = class {
   constructor(e, t, n) {
     this.variableNames = ["A"];
     let { windowSize: s, batchSize: r, outSize: a } = e;
@@ -26812,7 +26870,7 @@ var _8 = class {
     `;
   }
 };
-var A8 = class {
+var B8 = class {
   constructor(e, t, n, s) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, w.assert(e.length > 2, () => `Packed arg${n.charAt(0).toUpperCase() + n.slice(1)} supports only inputs with rank above 2.`);
     let r = e[e.length - 1], a = Math.ceil(r / t);
@@ -26890,86 +26948,86 @@ var A8 = class {
     `;
   }
 };
-function q1(e, t, n, s = null) {
+function Q1(e, t, n, s = null) {
   let r = t.shape[0], a = t.shape[1];
   s != null && (r = s.shape[0], a = s.shape[1]);
-  let i = S.computeOptimalWindowSize(a), o = { windowSize: i, inSize: a, batchSize: r, outSize: Math.ceil(a / i) }, u = new _8(o, n, s == null), l = [t];
+  let i = S.computeOptimalWindowSize(a), o = { windowSize: i, inSize: a, batchSize: r, outSize: Math.ceil(a / i) }, u = new L8(o, n, s == null), l = [t];
   s != null && l.push(s);
   let c = e.runWebGLProgram(u, l, "int32");
   if (c.shape[1] === 1)
     return c;
-  let p = q1(e, t, n, c);
+  let p = Q1(e, t, n, c);
   return e.disposeIntermediateTensorInfo(c), p;
 }
-function j1(e, t, n, s = null) {
-  let r = s != null ? s.shape : t.shape, a = r[r.length - 1], i = S.computeOptimalWindowSize(a), o = new A8(r, i, n, s == null), u = s == null ? [t] : [t, s], l = e.runWebGLProgram(o, u, "int32");
+function Z1(e, t, n, s = null) {
+  let r = s != null ? s.shape : t.shape, a = r[r.length - 1], i = S.computeOptimalWindowSize(a), o = new B8(r, i, n, s == null), u = s == null ? [t] : [t, s], l = e.runWebGLProgram(o, u, "int32");
   if (l.shape.length === t.shape.length) {
-    let c = j1(e, t, n, l);
+    let c = Z1(e, t, n, l);
     return e.disposeIntermediateTensorInfo(l), c;
   }
   return l;
 }
-function K1(e, t, n, s) {
+function J1(e, t, n, s) {
   let r = [n];
   if (S.assertAxesAreInnerMostDims("arg" + s.charAt(0).toUpperCase() + s.slice(1), r, t.shape.length), !X().getBool("WEBGL_PACK_REDUCE") || t.shape.length <= 2) {
     let a = [], i = e.texData.get(t.dataId), o = i !== null && i.isPacked, u = t;
     o && (u = e.unpackTensor(t), a.push(u));
     let [l, c] = S.computeOutAndReduceShapes(u.shape, r), p = w.sizeFromShape(c), d = he({ inputs: { x: u }, backend: e, attrs: { shape: [-1, p] } });
     a.push(d);
-    let h = q1(e, d, s);
+    let h = Q1(e, d, s);
     a.push(h);
     let f = he({ inputs: { x: h }, backend: e, attrs: { shape: l } });
     return a.forEach((m) => e.disposeIntermediateTensorInfo(m)), f;
   }
-  return j1(e, t, s);
+  return Z1(e, t, s);
 }
-function E8(e) {
+function V8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
   o != null && (u = qt({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
-  let c = K1(n, u, i[0], "max");
+  let c = J1(n, u, i[0], "max");
   return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
 }
-var R8 = { kernelName: Ia, backendName: "webgl", kernelFunc: E8 };
-function D8(e) {
+var W8 = { kernelName: Sa, backendName: "webgl", kernelFunc: V8 };
+function U8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
   o != null && (u = qt({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
-  let c = K1(n, u, i[0], "min");
+  let c = J1(n, u, i[0], "min");
   return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
 }
-var F8 = { kernelName: ol, backendName: "webgl", kernelFunc: D8 };
-var O8 = ss + `
+var G8 = { kernelName: ul, backendName: "webgl", kernelFunc: U8 };
+var H8 = ss + `
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
 `;
-var P8 = Ke({ opSnippet: O8 });
-var z8 = { kernelName: ul, backendName: "webgl", kernelFunc: P8 };
-var M8 = ss + "return log(x + sqrt(x * x + 1.0));";
-var L8 = Ke({ opSnippet: M8 });
-var B8 = { kernelName: ll, backendName: "webgl", kernelFunc: L8 };
-var V8 = ss + `
+var q8 = Ke({ opSnippet: H8 });
+var j8 = { kernelName: ll, backendName: "webgl", kernelFunc: q8 };
+var K8 = ss + "return log(x + sqrt(x * x + 1.0));";
+var X8 = Ke({ opSnippet: K8 });
+var Y8 = { kernelName: cl, backendName: "webgl", kernelFunc: X8 };
+var Q8 = ss + `
   return atan(x);
 `;
-var W8 = Ke({ opSnippet: V8 });
-var U8 = { kernelName: cl, backendName: "webgl", kernelFunc: W8 };
-var G8 = QX + `
+var Z8 = Ke({ opSnippet: Q8 });
+var J8 = { kernelName: dl, backendName: "webgl", kernelFunc: Z8 };
+var eY = o8 + `
   return atan(a, b);
 `;
-var H8 = `
+var tY = `
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  ` + ZX + `
+  ` + u8 + `
   return result;
 `;
-var q8 = jt({ opSnippet: G8, packedOpSnippet: H8 });
-var j8 = { kernelName: pl, backendName: "webgl", kernelFunc: q8 };
-var K8 = ss + `
+var nY = jt({ opSnippet: eY, packedOpSnippet: tY });
+var sY = { kernelName: hl, backendName: "webgl", kernelFunc: nY };
+var rY = ss + `
   if ((x < -1.0) || (x > 1.0)) return NAN;
 return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
-var X8 = Ke({ opSnippet: K8 });
-var Y8 = { kernelName: dl, backendName: "webgl", kernelFunc: X8 };
-var tl = class {
+var aY = Ke({ opSnippet: rY });
+var iY = { kernelName: pl, backendName: "webgl", kernelFunc: aY };
+var nl = class {
   constructor(e, t, n, s = false, r = false) {
     if (this.variableNames = ["x"], t === "avg" && n)
       throw new Error("Cannot compute positions for average pool.");
@@ -27128,7 +27186,7 @@ var tl = class {
     `;
   }
 };
-var Iv = class {
+var Cv = class {
   constructor(e, t, n, s = false, r = false) {
     if (this.variableNames = ["x"], t === "avg" && n)
       throw new Error("Cannot compute positions for average pool.");
@@ -27309,24 +27367,24 @@ var Iv = class {
     `;
   }
 };
-function Q8(e) {
+function oY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  nu(r, "avgPool");
+  su(r, "avgPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
   w.assert(S.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
   let c = S.computePool2DInfo(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return kn({ inputs: { x: r }, backend: n });
-  let p = new tl(c, "avg", false);
+  let p = new nl(c, "avg", false);
   return n.runWebGLProgram(p, [r], "float32");
 }
-var Z8 = { kernelName: Sa, backendName: "webgl", kernelFunc: Q8 };
-function J8(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s, c = [1, 1, 1], p = S.computePool3DInfo(r.shape, a, i, c, o, u, l), d = new Iv(p, "avg", false);
+var uY = { kernelName: Ca, backendName: "webgl", kernelFunc: oY };
+function lY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s, c = [1, 1, 1], p = S.computePool3DInfo(r.shape, a, i, c, o, u, l), d = new Cv(p, "avg", false);
   return n.runWebGLProgram(d, [r], "float32");
 }
-var eY = { kernelName: qd, backendName: "webgl", kernelFunc: J8 };
-var tY = class {
+var cY = { kernelName: Kd, backendName: "webgl", kernelFunc: lY };
+var dY = class {
   constructor(e) {
     this.variableNames = ["dy"], this.outputShape = e.inShape;
     let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterHeight, u = e.effectiveFilterWidth, l = o - 1 - e.padInfo.top, c = u - 1 - e.padInfo.left, p = 1 / (t * n);
@@ -27375,7 +27433,7 @@ var tY = class {
     `;
   }
 };
-var nY = class {
+var pY = class {
   constructor(e) {
     this.variableNames = ["dy"], this.outputShape = e.inShape;
     let t = e.filterDepth, n = e.filterHeight, s = e.filterWidth, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.effectiveFilterDepth, p = e.effectiveFilterHeight, d = e.effectiveFilterWidth, h = c - 1 - e.padInfo.front, f = p - 1 - e.padInfo.top, m = d - 1 - e.padInfo.left, g = 1 / (t * n * s);
@@ -27438,24 +27496,24 @@ var nY = class {
     `;
   }
 };
-function sY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = S.computePool3DInfo(i.shape, o, u, p, l, c), h = new nY(d);
+function hY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = S.computePool3DInfo(i.shape, o, u, p, l, c), h = new pY(d);
   return n.runWebGLProgram(h, [r], i.dtype);
 }
-var rY = { kernelName: og, backendName: "webgl", kernelFunc: sY };
-function aY(e) {
+var fY = { kernelName: lg, backendName: "webgl", kernelFunc: hY };
+function mY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
-  nu([r, a], "avgPoolGrad");
-  let { filterSize: o, strides: u, pad: l } = s, c = S.computePool2DInfo(i.shape, o, u, 1, l), p = new tY(c);
+  su([r, a], "avgPoolGrad");
+  let { filterSize: o, strides: u, pad: l } = s, c = S.computePool2DInfo(i.shape, o, u, 1, l), p = new dY(c);
   return n.runWebGLProgram(p, [r], i.dtype);
 }
-var iY = { kernelName: ig, backendName: "webgl", kernelFunc: aY };
-function oY(e) {
+var gY = { kernelName: ug, backendName: "webgl", kernelFunc: mY };
+function bY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
-  return zd({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
+  return Ld({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
 }
-var uY = { kernelName: Ca, backendName: "webgl", kernelFunc: oY };
-var lY = class {
+var yY = { kernelName: Na, backendName: "webgl", kernelFunc: bY };
+var vY = class {
   constructor(e, t, n, s, r, a) {
     this.outputShape = [], this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t), S.assertAndGetBroadcastShape(e, n);
     let i = "0.0";
@@ -27474,7 +27532,7 @@ var lY = class {
     `;
   }
 };
-var cY = class {
+var xY = class {
   constructor(e, t, n, s, r, a) {
     this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t), S.assertAndGetBroadcastShape(e, n);
     let i = "vec4(0.0)";
@@ -27496,7 +27554,7 @@ var cY = class {
     `;
   }
 };
-var dY = ({ inputs: e, backend: t, attrs: n }) => {
+var wY = ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s, mean: r, variance: a, offset: i, scale: o } = e;
   w.assert(r.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(i == null || r.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(o == null || r.shape.length === o.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
   let { varianceEpsilon: u } = n;
@@ -27505,16 +27563,16 @@ var dY = ({ inputs: e, backend: t, attrs: n }) => {
   i != null && (c = i.shape, l.push(i));
   let p = null;
   o != null && (p = o.shape, l.push(o));
-  let d = X().getBool("WEBGL_PACK_NORMALIZATION") ? new cY(s.shape, r.shape, a.shape, c, p, u) : new lY(s.shape, r.shape, a.shape, c, p, u);
+  let d = X().getBool("WEBGL_PACK_NORMALIZATION") ? new xY(s.shape, r.shape, a.shape, c, p, u) : new vY(s.shape, r.shape, a.shape, c, p, u);
   return t.runWebGLProgram(d, l, l[0].dtype);
 };
-var pY = { kernelName: Ma, backendName: "webgl", kernelFunc: dY };
-var hY = class {
+var kY = { kernelName: Ba, backendName: "webgl", kernelFunc: wY };
+var IY = class {
   constructor(e) {
     this.variableNames = ["source"], this.outputShape = e, this.rank = e.length;
     let t = rt(this.rank);
     this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
-    let n = fY(this.rank), s, r = e.map((a, i) => `sourceLoc.${Gm[i]} = start[${i}] + coords.${Gm[i]};`);
+    let n = SY(this.rank), s, r = e.map((a, i) => `sourceLoc.${qm[i]} = start[${i}] + coords.${qm[i]};`);
     s = `
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
@@ -27528,15 +27586,15 @@ var hY = class {
     `;
   }
 };
-var Gm = ["x", "y", "z", "w", "u", "v"];
-function fY(e) {
+var qm = ["x", "y", "z", "w", "u", "v"];
+function SY(e) {
   if (e === 1)
     return "sourceLoc";
   if (e <= 6)
-    return Gm.slice(0, e).map((t) => "sourceLoc." + t).join(",");
+    return qm.slice(0, e).map((t) => "sourceLoc." + t).join(",");
   throw Error(`Slicing for rank ${e} is not yet supported`);
 }
-var mY = class {
+var CY = class {
   constructor(e) {
     this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
     let t = rt(this.rank), n = ln("coords", this.rank), s = ln("sourceLoc", this.rank), r = this.rank === 1 ? "sourceLoc" : `vec2(${s.slice(-2).join()})`, a = `getChannel(getSource(${s.join()}), ${r})`, i = `
@@ -27572,7 +27630,7 @@ var mY = class {
     `;
   }
 };
-function gY(e, t, n, s) {
+function NY(e, t, n, s) {
   let r = s.texData.get(e.dataId), a = s.makeTensorInfo(n, e.dtype), i = s.texData.get(a.dataId);
   Object.assign(i, r), i.refCount = 1, i.shape = n, i.dtype = e.dtype;
   let o = wt.computeFlatOffset(t, w.computeStrides(e.shape));
@@ -27580,62 +27638,62 @@ function gY(e, t, n, s) {
   let u = s.dataRefCount.get(i.slice.origDataId) || 1;
   return s.dataRefCount.set(i.slice.origDataId, u + 1), a;
 }
-function uu(e) {
+function lu(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
   if (wt.assertParamsValid(r, o, u), w.sizeFromShape(u) === 0)
     return n.makeTensorInfo(u, r.dtype, []);
   if (n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
-    let p = n.texData.get(r.dataId), d = iX(p.values, o, u, r.shape, r.dtype);
+    let p = n.texData.get(r.dataId), d = gX(p.values, o, u, r.shape, r.dtype);
     return n.makeTensorInfo(u, r.dtype, d);
   }
   let { isPacked: l } = n.texData.get(r.dataId), c = wt.isSliceContinous(r.shape, o, u);
   if (l || !c) {
-    let p = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new mY(u) : new hY(u), d = [o];
+    let p = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new CY(u) : new IY(u), d = [o];
     return n.runWebGLProgram(p, [r], r.dtype, d);
   }
-  return n.uploadToGPU(r.dataId), gY(r, o, u, n);
+  return n.uploadToGPU(r.dataId), NY(r, o, u, n);
 }
-var bY = { kernelName: Po, backendName: "webgl", kernelFunc: uu };
-var yY = (e) => {
+var TY = { kernelName: zo, backendName: "webgl", kernelFunc: lu };
+var $Y = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = qt({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = uu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = qt({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = lu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 };
-var vY = { kernelName: oo, backendName: "webgl", kernelFunc: yY };
-function xY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.readSync(r.dataId), u = n.readSync(a.dataId), l = R1(o, u, a.dtype, a.shape, i);
+var _Y = { kernelName: lo, backendName: "webgl", kernelFunc: $Y };
+function AY(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.readSync(r.dataId), u = n.readSync(a.dataId), l = z1(o, u, a.dtype, a.shape, i);
   return n.makeTensorInfo([i], a.dtype, l);
 }
-var wY = { kernelName: ug, backendName: "webgl", kernelFunc: xY };
-function kY(e) {
+var EY = { kernelName: cg, backendName: "webgl", kernelFunc: AY };
+function RY(e) {
   let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.readSync(s.dataId), i = n.readSync(r.dataId), o = S.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
   return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
 }
-var IY = { kernelName: lg, backendName: "webgl", kernelFunc: kY };
-var SY = "return float(a != b);";
-var X1 = jt({ opSnippet: SY, cpuKernelImpl: tX, dtype: "bool" });
-var CY = { kernelName: So, backendName: "webgl", kernelFunc: X1 };
+var DY = { kernelName: dg, backendName: "webgl", kernelFunc: RY };
+var FY = "return float(a != b);";
+var e2 = jt({ opSnippet: FY, cpuKernelImpl: dX, dtype: "bool" });
+var OY = { kernelName: No, backendName: "webgl", kernelFunc: e2 };
 function Jl(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
   return kn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
 }
-var NY = { kernelName: np, backendName: "webgl", kernelFunc: Jl };
-var TY = "return float(int(x));";
-function $Y(e, t) {
-  let n = new Hs(e.shape, TY), s = t.runWebGLProgram(n, [e], "int32");
+var PY = { kernelName: rp, backendName: "webgl", kernelFunc: Jl };
+var zY = "return float(int(x));";
+function MY(e, t) {
+  let n = new Hs(e.shape, zY), s = t.runWebGLProgram(n, [e], "int32");
   return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-function Hm(e) {
+function jm(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return kn({ inputs: { x: r }, backend: n });
-    let i = $t(r.shape), o = Hm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Rr({ inputs: { real: o, imag: i }, backend: n });
+    let i = $t(r.shape), o = jm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Dr({ inputs: { real: o, imag: i }, backend: n });
     return i.dispose(), n.disposeIntermediateTensorInfo(o), u;
   }
   if (r.dtype === "complex64") {
-    let i = Jl({ inputs: { input: r }, backend: n }), o = Hm({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
+    let i = Jl({ inputs: { input: r }, backend: n }), o = jm({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
     return n.disposeIntermediateTensorInfo(i), o;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
@@ -27643,18 +27701,18 @@ function Hm(e) {
     return { dataId: i.dataId, shape: i.shape, dtype: a };
   }
   if (a === "int32")
-    return $Y(r, n);
+    return MY(r, n);
   if (a === "bool") {
-    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = X1({ inputs: { a: r, b: i }, backend: n });
+    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = e2({ inputs: { a: r, b: i }, backend: n });
     return n.disposeIntermediateTensorInfo(i), u;
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var _Y = { kernelName: Na, backendName: "webgl", kernelFunc: Hm };
-var mw = "return ceil(x);";
-var AY = Ke({ opSnippet: mw, packedOpSnippet: mw, cpuKernelImpl: PK });
-var EY = { kernelName: Ta, backendName: "webgl", kernelFunc: AY };
-var RY = class {
+var LY = { kernelName: Ta, backendName: "webgl", kernelFunc: jm };
+var gw = "return ceil(x);";
+var BY = Ke({ opSnippet: gw, packedOpSnippet: gw, cpuKernelImpl: qK });
+var VY = { kernelName: $a, backendName: "webgl", kernelFunc: BY };
+var WY = class {
   constructor(e) {
     this.variableNames = ["A"], this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = e, this.userCode = `
 
@@ -27670,7 +27728,7 @@ var RY = class {
     `;
   }
 };
-var DY = class {
+var UY = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "minVal", type: "float" }, { name: "maxVal", type: "float" }], this.outputShape = e, this.userCode = `
       void main() {
@@ -27686,14 +27744,14 @@ var DY = class {
     `;
   }
 };
-function FY(e) {
+function GY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o;
-  X().getBool("WEBGL_PACK_CLIP") ? o = new DY(r.shape) : o = new RY(r.shape);
+  X().getBool("WEBGL_PACK_CLIP") ? o = new UY(r.shape) : o = new WY(r.shape);
   let u = [[a], [i]];
   return n.runWebGLProgram(o, [r], r.dtype, u);
 }
-var OY = { kernelName: Sr, backendName: "webgl", kernelFunc: FY };
-var PY = class {
+var HY = { kernelName: Cr, backendName: "webgl", kernelFunc: GY };
+var qY = class {
   constructor(e) {
     this.variableNames = ["real", "imag"], this.outputShape = e, this.userCode = `
       void main() {
@@ -27711,15 +27769,15 @@ var PY = class {
     `;
   }
 };
-function gw(e, t) {
+function bw(e, t) {
   return { dataId: t.dataId, dtype: t.dtype, shape: e.shape };
 }
-function zY(e) {
-  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new PY(s.shape), i = [gw(s, r.complexTensorInfos.real), gw(s, r.complexTensorInfos.imag)];
+function jY(e) {
+  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new qY(s.shape), i = [bw(s, r.complexTensorInfos.real), bw(s, r.complexTensorInfos.imag)];
   return n.runWebGLProgram(a, i, i[0].dtype);
 }
-var MY = { kernelName: Kd, backendName: "webgl", kernelFunc: zY };
-var LY = class {
+var KY = { kernelName: Yd, backendName: "webgl", kernelFunc: jY };
+var XY = class {
   constructor(e) {
     this.outputShape = [], this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`);
     let t = new Array(e.length - 1);
@@ -27744,7 +27802,7 @@ var LY = class {
     `;
   }
 };
-var BY = class {
+var YY = class {
   constructor(e, t) {
     this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = S.computeOutShape(e, t);
     let n = this.outputShape, s = n.length, r = rt(s), a = ln("coords", s), i = ["x", "y", "z", "w", "u", "v"].slice(0, s);
@@ -27803,15 +27861,15 @@ function Jc(e, t, n) {
   let s = e.indexOf(t);
   return e.map((a, i) => i === s ? `${a} - ${n}` : a).join();
 }
-function Qp(e) {
+function Jp(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
   return kn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
 }
-var VY = { kernelName: Zd, backendName: "webgl", kernelFunc: Qp };
-function Gi(e, t, n) {
+var QY = { kernelName: ep, backendName: "webgl", kernelFunc: Jp };
+function qi(e, t, n) {
   let s = e[0].dtype;
   if (s === "complex64") {
-    let c = e.map((m) => Jl({ inputs: { input: m }, backend: n })), p = e.map((m) => Qp({ inputs: { input: m }, backend: n })), d = Gi(c, t, n), h = Gi(p, t, n), f = Rr({ inputs: { real: d, imag: h }, backend: n });
+    let c = e.map((m) => Jl({ inputs: { input: m }, backend: n })), p = e.map((m) => Jp({ inputs: { input: m }, backend: n })), d = qi(c, t, n), h = qi(p, t, n), f = Dr({ inputs: { real: d, imag: h }, backend: n });
     return c.forEach((m) => n.disposeIntermediateTensorInfo(m)), p.forEach((m) => n.disposeIntermediateTensorInfo(m)), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
   }
   let r = n.shouldExecuteOnCPU(e);
@@ -27819,27 +27877,27 @@ function Gi(e, t, n) {
     let c = e.map((b) => {
       let y = w.sizeFromShape(b.shape.slice(t));
       return he({ inputs: { x: b }, backend: n, attrs: { shape: [-1, y] } });
-    }), p = c.map((b) => ({ vals: n.readSync(b.dataId), shape: b.shape })), d = S.computeOutShape(c.map((b) => b.shape), 1), h = c[0].shape[0] === 1, f = zK(p, d, s, h), m = S.computeOutShape(e.map((b) => b.shape), t), g = n.makeTensorInfo(m, s, f);
+    }), p = c.map((b) => ({ vals: n.readSync(b.dataId), shape: b.shape })), d = S.computeOutShape(c.map((b) => b.shape), 1), h = c[0].shape[0] === 1, f = jK(p, d, s, h), m = S.computeOutShape(e.map((b) => b.shape), t), g = n.makeTensorInfo(m, s, f);
     return c.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
   }
   if (e.length > X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
-    let c = Math.floor(e.length / 2), p = Gi(e.slice(0, c), t, n), d = Gi(e.slice(c), t, n), h = Gi([p, d], t, n);
+    let c = Math.floor(e.length / 2), p = qi(e.slice(0, c), t, n), d = qi(e.slice(c), t, n), h = qi([p, d], t, n);
     return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), h;
   }
   if (X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && e[0].shape.length > 1) {
-    let c = new BY(e.map((p) => p.shape), t);
+    let c = new YY(e.map((p) => p.shape), t);
     return n.runWebGLProgram(c, e, s);
   }
-  let { tensors2D: a, outShape: i } = WY(e, t, n), o = new LY(a.map((c) => c.shape)), u = n.runWebGLProgram(o, a, s);
+  let { tensors2D: a, outShape: i } = ZY(e, t, n), o = new XY(a.map((c) => c.shape)), u = n.runWebGLProgram(o, a, s);
   a.forEach((c) => n.disposeIntermediateTensorInfo(c));
   let l = he({ inputs: { x: u }, attrs: { shape: i }, backend: n });
   return n.disposeIntermediateTensorInfo(u), l;
 }
-function WY(e, t, n) {
+function ZY(e, t, n) {
   let s = S.computeOutShape(e.map((a) => a.shape), t);
   return { tensors2D: e.map((a) => he({ inputs: { x: a }, attrs: { shape: [-1, w.sizeFromShape(a.shape.slice(t))] }, backend: n })), outShape: s };
 }
-function Y1(e) {
+function t2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = S.computeOutShape(t.map((l) => l.shape), a);
   if (w.sizeFromShape(i) === 0)
     return n.makeTensorInfo(i, t[0].dtype, []);
@@ -27847,10 +27905,10 @@ function Y1(e) {
   if (o.length === 1)
     return kn({ inputs: { x: o[0] }, backend: n });
   let u = o.map((l) => l.shape);
-  return S.assertParamsConsistent(u, a), Gi(o, a, n);
+  return S.assertParamsConsistent(u, a), qi(o, a, n);
 }
-var UY = { kernelName: uo, backendName: "webgl", kernelFunc: Y1 };
-var Q1 = class {
+var JY = { kernelName: co, backendName: "webgl", kernelFunc: t2 };
+var n2 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
     let a = e.padInfo.top, i = e.padInfo.left, o = e.strideHeight, u = e.strideWidth, l = e.dilationHeight, c = e.dilationWidth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4, m = e.dataFormat === "channelsLast", g = m ? 1 : 2, b = m ? 2 : 3, y = m ? 3 : 1, v = "", x = "";
@@ -27993,7 +28051,7 @@ var Q1 = class {
     `;
   }
 };
-var GY = class {
+var e9 = class {
   constructor(e) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
     let t = e.padInfo.front, n = e.padInfo.top, s = e.padInfo.left, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.filterDepth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4;
@@ -28088,7 +28146,7 @@ var GY = class {
     `;
   }
 };
-var HY = class {
+var t9 = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }, { name: "pad", type: "ivec2" }, { name: "stride", type: "ivec2" }, { name: "dilation", type: "ivec2" }, { name: "inChannels", type: "int" }, { name: "itemsPerBlockRow", type: "int" }, { name: "outWidth", type: "int" }], this.outputShape = e, this.enableShapeUniforms = Sn(this.outputShape.length);
     let { dataFormat: n } = t, s = fn(), r = n === "channelsLast", a = r ? 0 : 1, i = r ? 1 : 2, o = this.enableShapeUniforms ? "if(blockIndex < outShape[1] && pos < outShape[0]) {" : `if(blockIndex < ${e[1]} && pos < ${e[0]}) {`, u = "";
@@ -28145,29 +28203,29 @@ var HY = class {
     `;
   }
 };
-function Z1({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function s2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let u = e.shape, l = s.texData.get(e.dataId), c = n.inChannels, p = u[0] * u[1] * u[2], d = n.outChannels, h = n.dataFormat === "channelsLast", f = false, m = false, g, b = [];
-  if (!((p === 1 || d === 1) && c > H1) && l.isPacked && h && l.texture != null && u[2] % 2 !== 0 && w.arraysEqual(l.shape.slice(-3), u.slice(-3))) {
+  if (!((p === 1 || d === 1) && c > Y1) && l.isPacked && h && l.texture != null && u[2] % 2 !== 0 && w.arraysEqual(l.shape.slice(-3), u.slice(-3))) {
     let x = u[0] * u[1] * (u[2] + 1), k = { dataId: e.dataId, shape: [1, x, n.inChannels], dtype: e.dtype }, T = l.shape;
-    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(el(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
+    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(tl(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
     let N = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
     b.push(N);
-    let E = zd({ a: k, b: N, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), A = s.texData.get(E.dataId);
+    let E = Ld({ a: k, b: N, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), A = s.texData.get(E.dataId);
     w.assert(A.isPacked, () => "batchMatMul result is expected to be packed"), l.shape = T, A.shape = n.outShape, g = kn({ inputs: { x: E }, backend: s }), g.shape = n.outShape, b.push(E);
   } else {
-    let x = h ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3], k = he({ inputs: { x: e }, backend: s, attrs: { shape: [1, x, n.inChannels] } }), T = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), N = zd({ a: k, b: T, transposeA: f, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
+    let x = h ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3], k = he({ inputs: { x: e }, backend: s, attrs: { shape: [1, x, n.inChannels] } }), T = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), N = Ld({ a: k, b: T, transposeA: f, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
     g = he({ inputs: { x: N }, backend: s, attrs: { shape: n.outShape } }), b.push(k), b.push(T), b.push(N);
   }
   for (let x of b)
     s.disposeIntermediateTensorInfo(x);
   return g;
 }
-function J1({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function r2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let { filterWidth: u, filterHeight: l, inChannels: c, outWidth: p, outHeight: d, dataFormat: h } = n, f = h === "channelsLast", m = u * l * c, g = d * p, b = [m, g], y = true, v = false, x = [], k = he({ inputs: { x: e }, backend: s, attrs: { shape: e.shape.slice(1) } }), T = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, m, w.sizeFromShape(t.shape) / m] } });
   x.push(k), x.push(T);
-  let N = new HY(b, n), E = [k.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], A = s.runWebGLProgram(N, [k], "float32", E), P = he({ inputs: { x: A }, backend: s, attrs: { shape: [1, b[0], b[1]] } });
+  let N = new t9(b, n), E = [k.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], A = s.runWebGLProgram(N, [k], "float32", E), P = he({ inputs: { x: A }, backend: s, attrs: { shape: [1, b[0], b[1]] } });
   x.push(A), x.push(P);
-  let R = r != null, F = a != null, $ = o === "leakyrelu", z = o ? Kp(o, true) : null, W = new G1(P.shape, T.shape, [1, g, n.outChannels], y, v, R, z, F, $), q = [P, T];
+  let R = r != null, F = a != null, $ = o === "leakyrelu", z = o ? Yp(o, true) : null, W = new X1(P.shape, T.shape, [1, g, n.outChannels], y, v, R, z, F, $), q = [P, T];
   if (r && q.push(r), F && q.push(a), $) {
     let te = s.makeTensorInfo([], "float32", w.createScalarValue(i, "float32"));
     q.push(te), x.push(te);
@@ -28178,21 +28236,21 @@ function J1({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAct
     s.disposeIntermediateTensorInfo(te);
   return Z;
 }
-function qY(e) {
+function n9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s, p = S.convertConv2DDataFormat(u), d = S.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p), h;
   if (d.filterHeight === 1 && d.filterWidth === 1 && d.dilationHeight === 1 && d.dilationWidth === 1 && d.strideHeight === 1 && d.strideWidth === 1 && (d.padInfo.type === "SAME" || d.padInfo.type === "VALID"))
-    h = Z1({ x: r, filter: a, convInfo: d, backend: n });
+    h = s2({ x: r, filter: a, convInfo: d, backend: n });
   else if (X().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
-    h = J1({ x: r, filter: a, convInfo: d, backend: n });
+    h = r2({ x: r, filter: a, convInfo: d, backend: n });
   else {
-    let m = new Q1(d);
+    let m = new n2(d);
     h = n.runWebGLProgram(m, [r, a], "float32");
   }
   let f = he({ inputs: { x: h }, backend: n, attrs: { shape: d.outShape } });
   return n.disposeIntermediateTensorInfo(h), f;
 }
-var jY = { kernelName: $a, backendName: "webgl", kernelFunc: qY };
-var KY = class {
+var s9 = { kernelName: _a, backendName: "webgl", kernelFunc: n9 };
+var r9 = class {
   constructor(e) {
     this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
     let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.dataFormat === "channelsLast";
@@ -28241,7 +28299,7 @@ var KY = class {
     `;
   }
 };
-var XY = class {
+var a9 = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
     let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = e.dataFormat === "channelsLast", i = t - 1 - e.padInfo.top, o = n - 1 - e.padInfo.left, u = a ? 1 : 2, l = a ? 2 : 3, c = a ? 3 : 1;
@@ -28301,7 +28359,7 @@ var XY = class {
     `;
   }
 };
-var YY = class {
+var i9 = class {
   constructor(e) {
     this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
     let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.padInfo.front, a = e.padInfo.top, i = e.padInfo.left;
@@ -28350,7 +28408,7 @@ var YY = class {
     `;
   }
 };
-var QY = class {
+var o9 = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
     let t = e.filterDepth, n = e.filterHeight, s = e.filterWidth, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = t - 1 - e.padInfo.front, u = n - 1 - e.padInfo.top, l = s - 1 - e.padInfo.left;
@@ -28414,43 +28472,43 @@ var QY = class {
     `;
   }
 };
-function ZY(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s, p = S.convertConv2DDataFormat(u), d = S.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), h = new KY(d);
+function u9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s, p = S.convertConv2DDataFormat(u), d = S.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), h = new r9(d);
   return n.runWebGLProgram(h, [r, a], "float32");
 }
-var JY = { kernelName: cg, backendName: "webgl", kernelFunc: ZY };
-function e9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = S.convertConv2DDataFormat(l), d = S.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = new XY(d);
+var l9 = { kernelName: pg, backendName: "webgl", kernelFunc: u9 };
+function c9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = S.convertConv2DDataFormat(l), d = S.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = new a9(d);
   return n.runWebGLProgram(h, [r, a], "float32");
 }
-var t9 = { kernelName: _a, backendName: "webgl", kernelFunc: e9 };
-function n9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = S.computeConv3DInfo(r.shape, a.shape, i, u, o), c = new GY(l);
+var d9 = { kernelName: Aa, backendName: "webgl", kernelFunc: c9 };
+function p9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = S.computeConv3DInfo(r.shape, a.shape, i, u, o), c = new e9(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var s9 = { kernelName: Xd, backendName: "webgl", kernelFunc: n9 };
-function r9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s, l = S.computeConv3DInfo(r.shape, u, i, 1, o), c = new YY(l);
+var h9 = { kernelName: Qd, backendName: "webgl", kernelFunc: p9 };
+function f9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s, l = S.computeConv3DInfo(r.shape, u, i, 1, o), c = new i9(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var a9 = { kernelName: dg, backendName: "webgl", kernelFunc: r9 };
-function i9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s, l = S.computeConv3DInfo(u, a.shape, o, 1, i), c = new QY(l);
+var m9 = { kernelName: hg, backendName: "webgl", kernelFunc: f9 };
+function g9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s, l = S.computeConv3DInfo(u, a.shape, o, 1, i), c = new o9(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var o9 = { kernelName: pg, backendName: "webgl", kernelFunc: i9 };
-var u9 = ou + `
+var b9 = { kernelName: fg, backendName: "webgl", kernelFunc: g9 };
+var y9 = uu + `
   return cos(x);
 `;
-var l9 = Ke({ opSnippet: u9 });
-var c9 = { kernelName: Aa, backendName: "webgl", kernelFunc: l9 };
-var d9 = `
+var v9 = Ke({ opSnippet: y9 });
+var x9 = { kernelName: Ea, backendName: "webgl", kernelFunc: v9 };
+var w9 = `
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
 `;
-var p9 = Ke({ opSnippet: d9 });
-var h9 = { kernelName: Ea, backendName: "webgl", kernelFunc: p9 };
-var f9 = class {
+var k9 = Ke({ opSnippet: w9 });
+var I9 = { kernelName: Ra, backendName: "webgl", kernelFunc: k9 };
+var S9 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
     let [a, i, o, u] = e, [l] = t, [c, p] = n;
@@ -28520,32 +28578,32 @@ var f9 = class {
     `;
   }
 };
-var m9 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new f9(r.shape, a.shape, o, u, l);
+var C9 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new S9(r.shape, a.shape, o, u, l);
   return n.runWebGLProgram(c, [r, a, i], "float32");
 };
-var g9 = { kernelName: co, backendName: "webgl", kernelFunc: m9 };
-var bw = class {
+var N9 = { kernelName: ho, backendName: "webgl", kernelFunc: C9 };
+var yw = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }], this.outputShape = e;
-    let s = e.length, r = t ? "1.0" : `getX(${yw(s, "coords")})`, a = e[e.length - 1], i = "", o = "";
+    let s = e.length, r = t ? "1.0" : `getX(${vw(s, "coords")})`, a = e[e.length - 1], i = "", o = "";
     t ? (i = n ? `end != ${a - 1}` : "end != 0", o = n ? "end + 1" : "end - 1") : (i = n ? `end + pow2 < ${a}` : "end >= pow2", o = n ? "end + pow2" : "end - pow2"), this.userCode = `
       void main() {
         ${rt(s)} coords = getOutputCoords();
-        int end = ${vw(s, "coords")};
+        int end = ${xw(s, "coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${vw(s, "coords")} = idx;
-          val *= getX(${yw(s, "coords")});
+          ${xw(s, "coords")} = idx;
+          val *= getX(${vw(s, "coords")});
         }
         setOutput(val);
       }
     `;
   }
 };
-function yw(e, t) {
+function vw(e, t) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28556,7 +28614,7 @@ function yw(e, t) {
     return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
   throw Error(`Cumulative product for rank ${e} is not yet supported`);
 }
-function vw(e, t) {
+function xw(e, t) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28567,7 +28625,7 @@ function vw(e, t) {
     return `${t}.w`;
   throw Error(`Cumulative product for rank ${e} is not yet supported`);
 }
-function b9(e) {
+function T9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length, l = S.getAxesPermutation([a], u), c = r;
   l != null && (c = qt({ inputs: { x: r }, backend: n, attrs: { perm: l } }));
   let p = S.getInnerMostAxes(1, u)[0];
@@ -28575,11 +28633,11 @@ function b9(e) {
     throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length - 1} but got axis=${a}`);
   let d = c.shape[p], h = kn({ inputs: { x: c }, backend: n });
   for (let f = 0; f <= Math.ceil(Math.log2(d)) - 1; f++) {
-    let m = new bw(c.shape, false, o), g = [[f]], b = h;
+    let m = new yw(c.shape, false, o), g = [[f]], b = h;
     h = n.runWebGLProgram(m, [h], h.dtype, g), n.disposeIntermediateTensorInfo(b);
   }
   if (i) {
-    let f = new bw(c.shape, i, o), m = h;
+    let f = new yw(c.shape, i, o), m = h;
     h = n.runWebGLProgram(f, [h], h.dtype), n.disposeIntermediateTensorInfo(m);
   }
   if (l != null) {
@@ -28588,28 +28646,28 @@ function b9(e) {
   }
   return h;
 }
-var y9 = { kernelName: hl, backendName: "webgl", kernelFunc: b9 };
-var xw = class {
+var $9 = { kernelName: po, backendName: "webgl", kernelFunc: T9 };
+var ww = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }], this.outputShape = e;
-    let s = e.length, r = t ? "0.0" : `getX(${ww(s, "coords")})`, a = e[e.length - 1], i = "", o = "";
+    let s = e.length, r = t ? "0.0" : `getX(${kw(s, "coords")})`, a = e[e.length - 1], i = "", o = "";
     t ? (i = n ? `end != ${a - 1}` : "end != 0", o = n ? "end + 1" : "end - 1") : (i = n ? `end + pow2 < ${a}` : "end >= pow2", o = n ? "end + pow2" : "end - pow2"), this.userCode = `
       void main() {
         ${rt(s)} coords = getOutputCoords();
-        int end = ${kw(s, "coords")};
+        int end = ${Iw(s, "coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${kw(s, "coords")} = idx;
-          val += getX(${ww(s, "coords")});
+          ${Iw(s, "coords")} = idx;
+          val += getX(${kw(s, "coords")});
         }
         setOutput(val);
       }
     `;
   }
 };
-function ww(e, t) {
+function kw(e, t) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28620,7 +28678,7 @@ function ww(e, t) {
     return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
   throw Error(`Cumulative sum for rank ${e} is not yet supported`);
 }
-function kw(e, t) {
+function Iw(e, t) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28631,7 +28689,7 @@ function kw(e, t) {
     return `${t}.w`;
   throw Error(`Cumulative sum for rank ${e} is not yet supported`);
 }
-function v9(e) {
+function _9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length, l = S.getAxesPermutation([a], u), c = r;
   l != null && (c = qt({ inputs: { x: r }, backend: n, attrs: { perm: l } }));
   let p = S.getInnerMostAxes(1, u)[0];
@@ -28639,11 +28697,11 @@ function v9(e) {
     throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length - 1} but got axis=${a}`);
   let d = c.shape[p], h = kn({ inputs: { x: c }, backend: n });
   for (let f = 0; f <= Math.ceil(Math.log2(d)) - 1; f++) {
-    let m = new xw(c.shape, false, o), g = [[f]], b = h;
+    let m = new ww(c.shape, false, o), g = [[f]], b = h;
     h = n.runWebGLProgram(m, [h], h.dtype, g), n.disposeIntermediateTensorInfo(b);
   }
   if (i) {
-    let f = new xw(c.shape, i, o), m = h;
+    let f = new ww(c.shape, i, o), m = h;
     h = n.runWebGLProgram(f, [h], h.dtype), n.disposeIntermediateTensorInfo(m);
   }
   if (l != null) {
@@ -28652,20 +28710,20 @@ function v9(e) {
   }
   return h;
 }
-var x9 = { kernelName: lo, backendName: "webgl", kernelFunc: v9 };
-function w9(e) {
+var A9 = { kernelName: Da, backendName: "webgl", kernelFunc: _9 };
+function E9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
   if (r.shape.length === 1) {
-    let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = R1(u, l, a.dtype, a.shape, i);
+    let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = z1(u, l, a.dtype, a.shape, i);
     return n.makeTensorInfo([i], a.dtype, c);
   } else if (r.shape.length === 2) {
-    let u = n.bufferSync(r), l = n.bufferSync(a), c = OK(u, l, i, o);
+    let u = n.bufferSync(r), l = n.bufferSync(a), c = HK(u, l, i, o);
     return n.makeTensorInfo(c.shape, a.dtype, c.values);
   }
   throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
 }
-var k9 = { kernelName: hg, backendName: "webgl", kernelFunc: w9 };
-var I9 = class {
+var R9 = { kernelName: mg, backendName: "webgl", kernelFunc: E9 };
+var D9 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t, this.dataFormat = n, this.userCode = `
     void main() {
@@ -28704,12 +28762,12 @@ var I9 = class {
     return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
   }
 };
-function S9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = new I9(f, a, i);
+function F9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = new D9(f, a, i);
   return n.runWebGLProgram(m, [r], r.dtype);
 }
-var C9 = { kernelName: po, backendName: "webgl", kernelFunc: S9 };
-var e2 = class {
+var O9 = { kernelName: fo, backendName: "webgl", kernelFunc: F9 };
+var a2 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = Sn(this.outputShape.length);
     let a = e.filterHeight, i = e.filterWidth, o = e.outChannels / e.inChannels, u = "", l = "";
@@ -28771,7 +28829,7 @@ var e2 = class {
     `;
   }
 };
-var t2 = class {
+var i2 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = Sn(this.outputShape.length);
     let a = e.outChannels / e.inChannels, i = e.padInfo.left, o = e.strideWidth, u = e.dilationWidth, l = e.filterHeight, c = e.filterWidth, p = c, d = `
@@ -28986,16 +29044,16 @@ var t2 = class {
     `;
   }
 };
-function N9(e) {
+function P9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
   c == null && (c = [1, 1]), w.assert(S.eitherStridesOrDilationsAreOne(i, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);
   let p = S.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d;
-  X().getBool("WEBGL_PACK_DEPTHWISECONV") && p.strideWidth <= 2 && p.outChannels / p.inChannels === 1 ? d = new t2(p) : d = new e2(p);
+  X().getBool("WEBGL_PACK_DEPTHWISECONV") && p.strideWidth <= 2 && p.outChannels / p.inChannels === 1 ? d = new i2(p) : d = new a2(p);
   let h = [[p.padInfo.top, p.padInfo.left], [p.strideHeight, p.strideWidth], [p.dilationHeight, p.dilationWidth], [p.inHeight, p.inWidth]];
   return n.runWebGLProgram(d, [r, a], "float32", h);
 }
-var T9 = { kernelName: Ra, backendName: "webgl", kernelFunc: N9 };
-var $9 = class {
+var z9 = { kernelName: Fa, backendName: "webgl", kernelFunc: P9 };
+var M9 = class {
   constructor(e) {
     this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
     let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.outChannels / e.inChannels;
@@ -29037,7 +29095,7 @@ var $9 = class {
     `;
   }
 };
-var _9 = class {
+var L9 = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
     let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = t - 1 - e.padInfo.top, i = n - 1 - e.padInfo.left, o = e.outChannels / e.inChannels;
@@ -29089,17 +29147,17 @@ var _9 = class {
     `;
   }
 };
-function A9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s, p = S.computeConv2DInfo(r.shape, c, i, o, u, l, true), d = new $9(p);
+function B9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s, p = S.computeConv2DInfo(r.shape, c, i, o, u, l, true), d = new M9(p);
   return n.runWebGLProgram(d, [r, a], "float32");
 }
-var E9 = { kernelName: fg, backendName: "webgl", kernelFunc: A9 };
-function R9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s, p = S.computeConv2DInfo(c, a.shape, i, o, u, l, true), d = new _9(p);
+var V9 = { kernelName: gg, backendName: "webgl", kernelFunc: B9 };
+function W9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s, p = S.computeConv2DInfo(c, a.shape, i, o, u, l, true), d = new L9(p);
   return n.runWebGLProgram(d, [r, a], "float32");
 }
-var D9 = { kernelName: mg, backendName: "webgl", kernelFunc: R9 };
-var F9 = class {
+var U9 = { kernelName: bg, backendName: "webgl", kernelFunc: W9 };
+var G9 = class {
   constructor(e) {
     this.variableNames = ["X"], this.outputShape = [e, e], this.userCode = `
       void main() {
@@ -29110,12 +29168,12 @@ var F9 = class {
     `;
   }
 };
-function O9(e) {
-  let { inputs: t, backend: n } = e, { x: s } = t, r = [...s.shape, ...s.shape], a = w.sizeFromShape(s.shape), i = he({ inputs: { x: s }, backend: n, attrs: { shape: [a] } }), o = new F9(a), u = n.runWebGLProgram(o, [i], i.dtype), l = he({ inputs: { x: u }, backend: n, attrs: { shape: r } });
+function H9(e) {
+  let { inputs: t, backend: n } = e, { x: s } = t, r = [...s.shape, ...s.shape], a = w.sizeFromShape(s.shape), i = he({ inputs: { x: s }, backend: n, attrs: { shape: [a] } }), o = new G9(a), u = n.runWebGLProgram(o, [i], i.dtype), l = he({ inputs: { x: u }, backend: n, attrs: { shape: r } });
   return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(u), l;
 }
-var P9 = { kernelName: gg, backendName: "webgl", kernelFunc: O9 };
-var z9 = class {
+var q9 = { kernelName: yg, backendName: "webgl", kernelFunc: H9 };
+var j9 = class {
   constructor(e) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
     let { inHeight: t, inWidth: n, padInfo: s, strideHeight: r, strideWidth: a, filterHeight: i, filterWidth: o, dilationHeight: u, dilationWidth: l } = e, { top: c, left: p } = s;
@@ -29160,14 +29218,14 @@ var z9 = class {
     `;
   }
 };
-function M9(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = S.computeDilation2DInfo(r.shape, a.shape, i, o, "NHWC", u), c, p = new z9(l);
+function K9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = S.computeDilation2DInfo(r.shape, a.shape, i, o, "NHWC", u), c, p = new j9(l);
   c = n.runWebGLProgram(p, [r, a], "float32");
   let d = he({ inputs: { x: c }, backend: n, attrs: { shape: l.outShape } });
   return n.disposeIntermediateTensorInfo(c), d;
 }
-var L9 = { kernelName: Yd, backendName: "webgl", kernelFunc: M9 };
-function B9(e) {
+var X9 = { kernelName: Zd, backendName: "webgl", kernelFunc: K9 };
+function Y9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = S.decodeEinsumEquation(r, a.length);
   S.checkEinsumDimSizes(i.length, u, a);
   let { path: l, steps: c } = S.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
@@ -29178,17 +29236,17 @@ function B9(e) {
       let x = v.shape.slice();
       for (let k = 0; k < y.length; ++k)
         x.splice(y[k], 0, 1);
-      w.arraysEqual(v.shape, x) || (v = he({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = kv({ inputs: { a: v, b: d }, backend: n }), f.push(d));
+      w.arraysEqual(v.shape, x) || (v = he({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Sv({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = Yp({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = Zp({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeIntermediateTensorInfo(m);
   return d;
 }
-var V9 = { kernelName: Qd, backendName: "webgl", kernelFunc: B9 };
-var W9 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
-var U9 = `
+var Q9 = { kernelName: Jd, backendName: "webgl", kernelFunc: Y9 };
+var Z9 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+var J9 = `
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -29198,25 +29256,25 @@ var U9 = `
 
   return result;
 `;
-var G9 = Ke({ opSnippet: W9, packedOpSnippet: U9 });
-var H9 = { kernelName: Fa, backendName: "webgl", kernelFunc: G9 };
-var q9 = "return (b >= 1.0) ? a : a * (b + 1.0);";
-var j9 = `
+var eQ = Ke({ opSnippet: Z9, packedOpSnippet: J9 });
+var tQ = { kernelName: Pa, backendName: "webgl", kernelFunc: eQ };
+var nQ = "return (b >= 1.0) ? a : a * (b + 1.0);";
+var sQ = `
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
 `;
-var K9 = (e) => {
-  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(j9, s.shape, r.shape) : new ro(q9, s.shape, r.shape);
+var rQ = (e) => {
+  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(sQ, s.shape, r.shape) : new io(nQ, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], s.dtype);
 };
-var X9 = { kernelName: bg, backendName: "webgl", kernelFunc: K9 };
-var Y9 = `
+var aQ = { kernelName: vg, backendName: "webgl", kernelFunc: rQ };
+var iQ = `
   return vec4(equal(a, b));
 `;
-var Q9 = "return float(a == b);";
-var Z9 = jt({ opSnippet: Q9, packedOpSnippet: Y9, dtype: "bool", cpuKernelImpl: MK });
-var J9 = { kernelName: ho, backendName: "webgl", kernelFunc: Z9 };
-var eQ = `
+var oQ = "return float(a == b);";
+var uQ = jt({ opSnippet: oQ, packedOpSnippet: iQ, dtype: "bool", cpuKernelImpl: KK });
+var lQ = { kernelName: mo, backendName: "webgl", kernelFunc: uQ };
+var cQ = `
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -29232,12 +29290,12 @@ var eQ = `
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
 `;
-var tQ = Ke({ opSnippet: eQ });
-var nQ = { kernelName: fl, backendName: "webgl", kernelFunc: tQ };
-var sQ = ou + `
+var dQ = Ke({ opSnippet: cQ });
+var pQ = { kernelName: fl, backendName: "webgl", kernelFunc: dQ };
+var hQ = uu + `
   return exp(x);
 `;
-var rQ = `
+var fQ = `
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -29247,17 +29305,17 @@ var rQ = `
 
   return result;
 `;
-var n2 = Ke({ opSnippet: sQ, packedOpSnippet: rQ, cpuKernelImpl: LK, dtype: "float32" });
-var aQ = { kernelName: Oa, backendName: "webgl", kernelFunc: n2 };
-function qm(e) {
+var o2 = Ke({ opSnippet: hQ, packedOpSnippet: fQ, cpuKernelImpl: XK, dtype: "float32" });
+var mQ = { kernelName: za, backendName: "webgl", kernelFunc: o2 };
+function Km(e) {
   let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
   return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), he({ inputs: { x: a }, backend: s, attrs: { shape: o } });
 }
-var iQ = { kernelName: fo, backendName: "webgl", kernelFunc: qm };
-var Iw = "return exp(x) - 1.0;";
-var oQ = Ke({ opSnippet: Iw, packedOpSnippet: Iw, cpuKernelImpl: BK });
-var uQ = { kernelName: mo, backendName: "webgl", kernelFunc: oQ };
-var Sw = class {
+var gQ = { kernelName: go, backendName: "webgl", kernelFunc: Km };
+var Sw = "return exp(x) - 1.0;";
+var bQ = Ke({ opSnippet: Sw, packedOpSnippet: Sw, cpuKernelImpl: YK });
+var yQ = { kernelName: bo, backendName: "webgl", kernelFunc: bQ };
+var Cw = class {
   constructor(e, t, n) {
     this.variableNames = ["real", "imag"];
     let s = t[1];
@@ -29305,18 +29363,18 @@ var Sw = class {
     `;
   }
 };
-function s2(e, t, n) {
-  let s = n.texData.get(e.dataId), r = w.sizeFromShape(e.shape), a = e.shape[e.shape.length - 1], i = r / a, o = he({ inputs: { x: e }, backend: n, attrs: { shape: [i, a] } }), u = o.shape, l = new Sw("real", u, t), c = new Sw("imag", u, t), p = [{ dataId: s.complexTensorInfos.real.dataId, dtype: s.complexTensorInfos.real.dtype, shape: u }, { dataId: s.complexTensorInfos.imag.dataId, dtype: s.complexTensorInfos.imag.dtype, shape: u }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Rr({ inputs: { real: d, imag: h }, backend: n });
+function u2(e, t, n) {
+  let s = n.texData.get(e.dataId), r = w.sizeFromShape(e.shape), a = e.shape[e.shape.length - 1], i = r / a, o = he({ inputs: { x: e }, backend: n, attrs: { shape: [i, a] } }), u = o.shape, l = new Cw("real", u, t), c = new Cw("imag", u, t), p = [{ dataId: s.complexTensorInfos.real.dataId, dtype: s.complexTensorInfos.real.dtype, shape: u }, { dataId: s.complexTensorInfos.imag.dataId, dtype: s.complexTensorInfos.imag.dtype, shape: u }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Dr({ inputs: { real: d, imag: h }, backend: n });
   n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h);
   let m = he({ inputs: { x: f }, backend: n, attrs: { shape: e.shape } });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(f), m;
 }
-function lQ(e) {
+function vQ(e) {
   let { inputs: t, backend: n } = e, { input: s } = t;
-  return s2(s, false, n);
+  return u2(s, false, n);
 }
-var cQ = { kernelName: yg, backendName: "webgl", kernelFunc: lQ };
-var dQ = class {
+var xQ = { kernelName: xg, backendName: "webgl", kernelFunc: vQ };
+var wQ = class {
   constructor(e, t) {
     this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = e, this.userCode = `
       void main() {
@@ -29332,12 +29390,12 @@ function ec(e) {
     let i = w.getArrayFromDType(a, w.sizeFromShape(s));
     return i.fill(r), t.makeTensorInfo(s, a, i);
   } else {
-    let i = new dQ(s, r), o = [[r]];
+    let i = new wQ(s, r), o = [[r]];
     return t.runWebGLProgram(i, [], a, o);
   }
 }
-var pQ = { kernelName: ml, backendName: "webgl", kernelFunc: ec };
-var hQ = class {
+var kQ = { kernelName: ml, backendName: "webgl", kernelFunc: ec };
+var IQ = class {
   constructor(e) {
     this.variableNames = ["Image"], this.outputShape = [];
     let t = e[2];
@@ -29358,14 +29416,14 @@ var hQ = class {
     `;
   }
 };
-var fQ = { kernelName: go, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { image: n } = e, s = t, r = new hQ(n.shape);
+var SQ = { kernelName: yo, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { image: n } = e, s = t, r = new IQ(n.shape);
   return s.runWebGLProgram(r, [n], n.dtype);
 } };
-var Cw = "return floor(x);";
-var mQ = Ke({ opSnippet: Cw, packedOpSnippet: Cw, cpuKernelImpl: VK });
-var gQ = { kernelName: Pa, backendName: "webgl", kernelFunc: mQ };
-var bQ = `
+var Nw = "return floor(x);";
+var CQ = Ke({ opSnippet: Nw, packedOpSnippet: Nw, cpuKernelImpl: QK });
+var NQ = { kernelName: Ma, backendName: "webgl", kernelFunc: CQ };
+var TQ = `
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -29376,7 +29434,7 @@ var bQ = `
     return NAN;
   }
 `;
-var yQ = `
+var $Q = `
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -29398,9 +29456,9 @@ var yQ = `
   }
   return vec4(result);
 `;
-var vQ = jt({ opSnippet: bQ, packedOpSnippet: yQ, dtype: "int32" });
-var xQ = { kernelName: za, backendName: "webgl", kernelFunc: vQ };
-var wQ = class {
+var _Q = jt({ opSnippet: TQ, packedOpSnippet: $Q, dtype: "int32" });
+var AQ = { kernelName: La, backendName: "webgl", kernelFunc: _Q };
+var EQ = class {
   constructor(e) {
     this.variableNames = ["A"];
     let t = fn(), [n, s] = e;
@@ -29429,7 +29487,7 @@ var wQ = class {
     `;
   }
 };
-var kQ = class {
+var RQ = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
     let t = fn(), [n, s] = e;
@@ -29470,24 +29528,24 @@ var kQ = class {
     `;
   }
 };
-var IQ = { kernelName: fd, backendName: "webgl", kernelFunc: SQ };
-var Li;
-function SQ(e) {
+var DQ = { kernelName: fd, backendName: "webgl", kernelFunc: FQ };
+var Vi;
+function FQ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s, i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, [u, l] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], c = [l, u], p = [l, u, a];
-  (o || i) && (Li == null && (Li = document.createElement("canvas").getContext("2d")), Li.canvas.width = u, Li.canvas.height = l, Li.drawImage(r, 0, 0, u, l), r = Li.canvas);
+  (o || i) && (Vi == null && (Vi = document.createElement("canvas").getContext("2d")), Vi.canvas.width = u, Vi.canvas.height = l, Vi.drawImage(r, 0, 0, u, l), r = Vi.canvas);
   let d = n.makeTensorInfo(c, "int32");
   n.texData.get(d.dataId).usage = 2, n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId), r);
-  let h = X().getBool("WEBGL_PACK") ? new kQ(p) : new wQ(p), f = n.runWebGLProgram(h, [d], "int32");
+  let h = X().getBool("WEBGL_PACK") ? new RQ(p) : new EQ(p), f = n.runWebGLProgram(h, [d], "int32");
   return n.disposeData(d.dataId), f;
 }
-function CQ(e) {
+function OQ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m), b, y = [];
   if (g.filterHeight === 1 && g.filterWidth === 1 && g.dilationHeight === 1 && g.dilationWidth === 1 && g.strideHeight === 1 && g.strideWidth === 1 && (g.padInfo.type === "SAME" || g.padInfo.type === "VALID"))
-    b = Z1({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
+    b = s2({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
   else if (X().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
-    b = J1({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
+    b = r2({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
   else {
-    let x = i != null, k = o != null, T = h === "leakyrelu", N = h ? Kp(h, false) : null, E = new Q1(g, x, N, k, T), A = [r, a];
+    let x = i != null, k = o != null, T = h === "leakyrelu", N = h ? Yp(h, false) : null, E = new n2(g, x, N, k, T), A = [r, a];
     if (i && A.push(i), o && A.push(o), T) {
       let P = n.makeTensorInfo([], "float32", w.createScalarValue(f, "float32"));
       A.push(P), y.push(P);
@@ -29497,22 +29555,22 @@ function CQ(e) {
   let v = he({ inputs: { x: b }, backend: n, attrs: { shape: g.outShape } });
   return y.push(b), y.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
 }
-var NQ = { kernelName: aa, backendName: "webgl", kernelFunc: CQ };
-function TQ(e) {
+var PQ = { kernelName: ia, backendName: "webgl", kernelFunc: OQ };
+function zQ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = [], m = c;
   m == null && (m = [1, 1]), w.assert(S.eitherStridesOrDilationsAreOne(u, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);
-  let g = S.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = X().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? Kp(d, b) : null, v = [r, a], x = i != null, k = o != null, T = d === "leakyrelu";
+  let g = S.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = X().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? Yp(d, b) : null, v = [r, a], x = i != null, k = o != null, T = d === "leakyrelu";
   if (x && v.push(i), k && v.push(o), T) {
     let P = n.makeTensorInfo([], "float32", w.createScalarValue(h, "float32"));
     v.push(P), f.push(P);
   }
   let N;
-  b ? N = new t2(g, x, y, k, T) : N = new e2(g, x, y, k, T);
+  b ? N = new i2(g, x, y, k, T) : N = new a2(g, x, y, k, T);
   let E = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], A = n.runWebGLProgram(N, v, "float32", E);
   return f.forEach((P) => n.disposeIntermediateTensorInfo(P)), A;
 }
-var $Q = { kernelName: ia, backendName: "webgl", kernelFunc: TQ };
-var _Q = class {
+var MQ = { kernelName: oa, backendName: "webgl", kernelFunc: zQ };
+var LQ = class {
   constructor(e, t, n) {
     this.sliceDim = e, this.strides = t, this.variableNames = ["x", "indices"], this.outputShape = n;
     let s = rt(t.length), r = rt(n.length), a = this.sliceDim > 1 ? "strides[j]" : "strides";
@@ -29530,20 +29588,20 @@ var _Q = class {
       `;
   }
 };
-function AQ(e) {
+function BQ(e) {
   let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = S.prepareAndValidate(s, r), d = he({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = he({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
   if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
-    let b = n.readSync(r.dataId), y = n.bufferSync(s), v = WK(b, y, s.dtype, l, i, c, p, s.shape, o);
+    let b = n.readSync(r.dataId), y = n.bufferSync(s), v = ZK(b, y, s.dtype, l, i, c, p, s.shape, o);
     return n.makeTensorInfo(u, s.dtype, v.values);
   }
-  let f = new _Q(i, p, [l, c]), m = n.runWebGLProgram(f, [h, d], h.dtype), g = he({ inputs: { x: m }, backend: n, attrs: { shape: u } });
+  let f = new LQ(i, p, [l, c]), m = n.runWebGLProgram(f, [h, d], h.dtype), g = he({ inputs: { x: m }, backend: n, attrs: { shape: u } });
   return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(m), g;
 }
-var EQ = { kernelName: yo, backendName: "webgl", kernelFunc: AQ };
-var RQ = class {
+var VQ = { kernelName: xo, backendName: "webgl", kernelFunc: BQ };
+var WQ = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.outputShape = t, this.rank = t.length;
-    let n = rt(this.rank), s = DQ(e, 2);
+    let n = rt(this.rank), s = UQ(e, 2);
     this.userCode = `
       void main() {
         ${n} resRC = getOutputCoords();
@@ -29554,13 +29612,13 @@ var RQ = class {
     `;
   }
 };
-function DQ(e, t) {
+function UQ(e, t) {
   let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [];
   for (let r = 0; r < e.length; r++)
     r === 2 ? s.push("index") : s.push(`${n[r]}`);
   return s.join();
 }
-function r2(e) {
+function l2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0];
   if (X().get("DEBUG")) {
     let y = n.readSync(a.dataId), v = r.shape[u];
@@ -29573,62 +29631,62 @@ function r2(e) {
   p.push(d), p.push(h);
   let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
   if (n.shouldExecuteOnCPU([r, a]) || r.dtype === "string") {
-    let y = n.bufferSync(h), v = n.bufferSync(d), x = UK(v, y, f);
+    let y = n.bufferSync(h), v = n.bufferSync(d), x = JK(v, y, f);
     return p.forEach((k) => n.disposeIntermediateTensorInfo(k)), n.makeTensorInfo(l.outputShape, x.dtype, x.values);
   }
-  let m = new RQ(d.shape, f), g = n.runWebGLProgram(m, [d, h], d.dtype);
+  let m = new WQ(d.shape, f), g = n.runWebGLProgram(m, [d, h], d.dtype);
   p.push(g);
   let b = he({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 }
-var FQ = { kernelName: bo, backendName: "webgl", kernelFunc: r2 };
-var OQ = "return float(a > b);";
-var PQ = `
+var GQ = { kernelName: vo, backendName: "webgl", kernelFunc: l2 };
+var HQ = "return float(a > b);";
+var qQ = `
   return vec4(greaterThan(a, b));
 `;
-var zQ = jt({ opSnippet: OQ, packedOpSnippet: PQ, cpuKernelImpl: GK, dtype: "bool" });
-var MQ = { kernelName: vo, backendName: "webgl", kernelFunc: zQ };
-var LQ = "return float(a >= b);";
-var BQ = `
+var jQ = jt({ opSnippet: HQ, packedOpSnippet: qQ, cpuKernelImpl: eX, dtype: "bool" });
+var KQ = { kernelName: wo, backendName: "webgl", kernelFunc: jQ };
+var XQ = "return float(a >= b);";
+var YQ = `
   return vec4(greaterThanEqual(a, b));
 `;
-var VQ = jt({ opSnippet: LQ, packedOpSnippet: BQ, dtype: "bool", cpuKernelImpl: HK });
-var WQ = { kernelName: La, backendName: "webgl", kernelFunc: VQ };
-function UQ(e) {
+var QQ = jt({ opSnippet: XQ, packedOpSnippet: YQ, dtype: "bool", cpuKernelImpl: tX });
+var ZQ = { kernelName: Va, backendName: "webgl", kernelFunc: QQ };
+function JQ(e) {
   let { inputs: t, backend: n } = e, { input: s } = t;
-  return s2(s, true, n);
+  return u2(s, true, n);
 }
-var GQ = { kernelName: vg, backendName: "webgl", kernelFunc: UQ };
-var HQ = "return float(!isnan(x) && !isinf(x));";
-var qQ = Ke({ opSnippet: HQ, dtype: "bool" });
-var jQ = { kernelName: gl, backendName: "webgl", kernelFunc: qQ };
-var KQ = "return float(isinf(x));";
-var XQ = Ke({ opSnippet: KQ, dtype: "bool" });
-var YQ = { kernelName: bl, backendName: "webgl", kernelFunc: XQ };
-var QQ = "return float(isnan(x));";
-var ZQ = Ke({ opSnippet: QQ, dtype: "bool" });
-var JQ = { kernelName: yl, backendName: "webgl", kernelFunc: ZQ };
-var eZ = "return float(a < b);";
-var tZ = `
+var eZ = { kernelName: wg, backendName: "webgl", kernelFunc: JQ };
+var tZ = "return float(!isnan(x) && !isinf(x));";
+var nZ = Ke({ opSnippet: tZ, dtype: "bool" });
+var sZ = { kernelName: gl, backendName: "webgl", kernelFunc: nZ };
+var rZ = "return float(isinf(x));";
+var aZ = Ke({ opSnippet: rZ, dtype: "bool" });
+var iZ = { kernelName: bl, backendName: "webgl", kernelFunc: aZ };
+var oZ = "return float(isnan(x));";
+var uZ = Ke({ opSnippet: oZ, dtype: "bool" });
+var lZ = { kernelName: yl, backendName: "webgl", kernelFunc: uZ };
+var cZ = "return float(a < b);";
+var dZ = `
   return vec4(lessThan(a, b));
 `;
-var nZ = jt({ opSnippet: eZ, packedOpSnippet: tZ, cpuKernelImpl: qK, dtype: "bool" });
-var sZ = { kernelName: xo, backendName: "webgl", kernelFunc: nZ };
-var rZ = "return float(a <= b);";
-var aZ = `
+var pZ = jt({ opSnippet: cZ, packedOpSnippet: dZ, cpuKernelImpl: nX, dtype: "bool" });
+var hZ = { kernelName: ko, backendName: "webgl", kernelFunc: pZ };
+var fZ = "return float(a <= b);";
+var mZ = `
   return vec4(lessThanEqual(a, b));
 `;
-var iZ = jt({ opSnippet: rZ, packedOpSnippet: aZ, cpuKernelImpl: jK, dtype: "bool" });
-var oZ = { kernelName: wo, backendName: "webgl", kernelFunc: iZ };
-function uZ(e) {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = KK(s, r, a);
+var gZ = jt({ opSnippet: fZ, packedOpSnippet: mZ, cpuKernelImpl: sX, dtype: "bool" });
+var bZ = { kernelName: Io, backendName: "webgl", kernelFunc: gZ };
+function yZ(e) {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = rX(s, r, a);
   return t.makeTensorInfo([i.length], "float32", i);
 }
-var lZ = { kernelName: xg, backendName: "webgl", kernelFunc: uZ };
-var cZ = ou + `
+var vZ = { kernelName: kg, backendName: "webgl", kernelFunc: yZ };
+var xZ = uu + `
   return x < 0.0 ? 0./0. : log(x);
 `;
-var dZ = `
+var wZ = `
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -29637,34 +29695,34 @@ var dZ = `
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
 `;
-var pZ = Ke({ opSnippet: cZ, packedOpSnippet: dZ, cpuKernelImpl: XK });
-var hZ = { kernelName: Wa, backendName: "webgl", kernelFunc: pZ };
-var fZ = ou + `
+var kZ = Ke({ opSnippet: xZ, packedOpSnippet: wZ, cpuKernelImpl: aX });
+var IZ = { kernelName: Ga, backendName: "webgl", kernelFunc: kZ };
+var SZ = uu + `
   return log(1.0 + x);
 `;
-var mZ = Ke({ opSnippet: fZ });
-var gZ = { kernelName: vl, backendName: "webgl", kernelFunc: mZ };
-var bZ = "return float(a >= 1.0 && b >= 1.0);";
-var yZ = `
+var CZ = Ke({ opSnippet: SZ });
+var NZ = { kernelName: vl, backendName: "webgl", kernelFunc: CZ };
+var TZ = "return float(a >= 1.0 && b >= 1.0);";
+var $Z = `
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
 `;
-var vZ = jt({ opSnippet: bZ, packedOpSnippet: yZ, dtype: "bool" });
-var xZ = { kernelName: ko, backendName: "webgl", kernelFunc: vZ };
-var wZ = "return float(!(x >= 1.0));";
-var kZ = Ke({ opSnippet: wZ });
-var IZ = { kernelName: xl, backendName: "webgl", kernelFunc: kZ };
-var SZ = "return float(a >= 1.0 || b >= 1.0);";
-var CZ = `
+var _Z = jt({ opSnippet: TZ, packedOpSnippet: $Z, dtype: "bool" });
+var AZ = { kernelName: So, backendName: "webgl", kernelFunc: _Z };
+var EZ = "return float(!(x >= 1.0));";
+var RZ = Ke({ opSnippet: EZ });
+var DZ = { kernelName: xl, backendName: "webgl", kernelFunc: RZ };
+var FZ = "return float(a >= 1.0 || b >= 1.0);";
+var OZ = `
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
 `;
-var NZ = jt({ opSnippet: SZ, packedOpSnippet: CZ, dtype: "bool" });
-var TZ = { kernelName: Jd, backendName: "webgl", kernelFunc: NZ };
-var $Z = class {
+var PZ = jt({ opSnippet: FZ, packedOpSnippet: OZ, dtype: "bool" });
+var zZ = { kernelName: tp, backendName: "webgl", kernelFunc: PZ };
+var MZ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["x"], this.outputShape = [];
     let a = t, i = e[3] - 1;
@@ -29692,7 +29750,7 @@ var $Z = class {
     `;
   }
 };
-var _Z = class {
+var LZ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
     let a = t, i = e[3] - 1;
@@ -29763,12 +29821,12 @@ var _Z = class {
     `;
   }
 };
-var AZ = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s, l = X().getBool("WEBGL_PACK_NORMALIZATION") ? new _Z(r.shape, a, i, o, u) : new $Z(r.shape, a, i, o, u);
+var BZ = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s, l = X().getBool("WEBGL_PACK_NORMALIZATION") ? new LZ(r.shape, a, i, o, u) : new MZ(r.shape, a, i, o, u);
   return n.runWebGLProgram(l, [r], r.dtype);
 };
-var EZ = { kernelName: ep, backendName: "webgl", kernelFunc: AZ };
-var RZ = class {
+var VZ = { kernelName: np, backendName: "webgl", kernelFunc: BZ };
+var WZ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t, this.bias = n, this.alpha = s, this.beta = r, this.userCode = `
       void main() {
@@ -29828,28 +29886,28 @@ var RZ = class {
     `;
   }
 };
-var DZ = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s, p = new RZ(r.shape, o, u, l, c);
+var UZ = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s, p = new WZ(r.shape, o, u, l, c);
   return n.runWebGLProgram(p, [r, a, i], r.dtype);
 };
-var FZ = { kernelName: wg, backendName: "webgl", kernelFunc: DZ };
-function OZ(e, t, n, s) {
-  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = xi(o, e.dtype, "max", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
+var GZ = { kernelName: Ig, backendName: "webgl", kernelFunc: UZ };
+function HZ(e, t, n, s) {
+  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = Ii(o, e.dtype, "max", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
   return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
 }
-function a2(e) {
+function c2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = S.getAxesPermutation(l, o), p = c != null, d = n.shouldExecuteOnCPU([r]), h = r;
   if (p) {
     if (d) {
       let v = n.texData.get(h.dataId).values, x = new Array(o);
       for (let N = 0; N < x.length; N++)
         x[N] = r.shape[c[N]];
-      let k = wv(v, r.shape, r.dtype, c, x);
+      let k = Iv(v, r.shape, r.dtype, c, x);
       h = n.makeTensorInfo(x, r.dtype);
       let T = n.texData.get(h.dataId);
       T.values = k;
     } else
-      h = Xp(r, c, n);
+      h = Qp(r, c, n);
     l = S.getInnerMostAxes(l.length, o);
   }
   S.assertAxesAreInnerMostDims("max", l, o);
@@ -29857,44 +29915,44 @@ function a2(e) {
   i && (g = S.expandShapeToKeepDim(f, u));
   let b;
   if (d) {
-    let v = n.texData.get(h.dataId).values, x = YK(v, w.sizeFromShape(m), g, r.dtype);
+    let v = n.texData.get(h.dataId).values, x = iX(v, w.sizeFromShape(m), g, r.dtype);
     b = n.makeTensorInfo(g, r.dtype);
     let k = n.texData.get(b.dataId);
     k.values = x;
   } else
-    b = OZ(h, m, g, n);
+    b = HZ(h, m, g, n);
   return p && n.disposeIntermediateTensorInfo(h), b;
 }
-var PZ = { kernelName: Ua, backendName: "webgl", kernelFunc: a2 };
-var zZ = L1 + `
+var qZ = { kernelName: Ha, backendName: "webgl", kernelFunc: c2 };
+var jZ = G1 + `
   return max(a, b);
 `;
-var MZ = `
+var KZ = `
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  ` + jp + `
+  ` + Xp + `
   return result;
 `;
-var LZ = jt({ opSnippet: zZ, packedOpSnippet: MZ, cpuKernelImpl: QK });
-var BZ = { kernelName: Ga, backendName: "webgl", kernelFunc: LZ };
-function VZ(e) {
+var XZ = jt({ opSnippet: jZ, packedOpSnippet: KZ, cpuKernelImpl: oX });
+var YZ = { kernelName: qa, backendName: "webgl", kernelFunc: XZ };
+function QZ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  nu(r, "maxPool");
+  su(r, "maxPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
   w.assert(S.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
   let c = S.computePool2DInfo(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return kn({ inputs: { x: r }, backend: n });
-  let p = new tl(c, "max", false);
+  let p = new nl(c, "max", false);
   return n.runWebGLProgram(p, [r], r.dtype);
 }
-var WZ = { kernelName: Ha, backendName: "webgl", kernelFunc: VZ };
-function UZ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = S.computePool3DInfo(r.shape, a, i, c, o, l, u), d = new Iv(p, "max", false);
+var ZZ = { kernelName: ja, backendName: "webgl", kernelFunc: QZ };
+function JZ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = S.computePool3DInfo(r.shape, a, i, c, o, l, u), d = new Cv(p, "max", false);
   return n.runWebGLProgram(d, [r], r.dtype);
 }
-var GZ = { kernelName: tp, backendName: "webgl", kernelFunc: UZ };
-var HZ = class {
+var e7 = { kernelName: sp, backendName: "webgl", kernelFunc: JZ };
+var t7 = class {
   constructor(e) {
     this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
     let t = e.strideHeight, n = e.strideWidth, s = e.dilationHeight, r = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = r - 1 - e.padInfo.top, o = a - 1 - e.padInfo.left, u = r * a - 1;
@@ -29947,7 +30005,7 @@ var HZ = class {
     `;
   }
 };
-var qZ = class {
+var n7 = class {
   constructor(e) {
     this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
     let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterDepth, u = e.effectiveFilterHeight, l = e.effectiveFilterWidth, c = o - 1 - e.padInfo.front, p = u - 1 - e.padInfo.top, d = l - 1 - e.padInfo.left, h = o * u * l - 1;
@@ -30018,63 +30076,63 @@ var qZ = class {
     `;
   }
 };
-function jZ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = S.computePool3DInfo(i.shape, o, u, p, l, c), h = new Iv(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new qZ(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
+function s7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = S.computePool3DInfo(i.shape, o, u, p, l, c), h = new Cv(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new n7(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
   return n.disposeIntermediateTensorInfo(f), g;
 }
-var KZ = { kernelName: Ig, backendName: "webgl", kernelFunc: jZ };
-function XZ(e) {
+var r7 = { kernelName: Cg, backendName: "webgl", kernelFunc: s7 };
+function a7(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
-  nu([a, i], "maxPoolGrad");
-  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = S.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new tl(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new HZ(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
+  su([a, i], "maxPoolGrad");
+  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = S.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new nl(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new t7(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
   return n.disposeIntermediateTensorInfo(m), b;
 }
-var YZ = { kernelName: kg, backendName: "webgl", kernelFunc: XZ };
-function QZ(e, t, n, s) {
-  let r = new tl(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
-  r = new tl(n, "max", true, true, t);
+var i7 = { kernelName: Sg, backendName: "webgl", kernelFunc: a7 };
+function o7(e, t, n, s) {
+  let r = new nl(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
+  r = new nl(n, "max", true, true, t);
   let i = s.runWebGLProgram(r, [e], "float32");
   return [a, i];
 }
-var ZZ = { kernelName: Sg, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var u7 = { kernelName: Ng, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
   w.assert(s.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);
   let l = [1, 1];
   w.assert(S.eitherStridesOrDilationsAreOne(a, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);
-  let c = S.computePool2DInfo(s.shape, r, a, l, i), [p, d] = QZ(s, o, c, u);
+  let c = S.computePool2DInfo(s.shape, r, a, l, i), [p, d] = o7(s, o, c, u);
   return [p, d];
 } };
-function JZ(e, t, n, s) {
-  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = xi(o, "float32", "mean", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
+function l7(e, t, n, s) {
+  let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = Ii(o, "float32", "mean", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
   return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
 }
-var e7 = { kernelName: qa, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var c7 = { kernelName: Ka, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s } = e, { keepDims: r, axis: a } = t, i = n, o = s.shape.length, u = w.parseAxisParam(a, s.shape), l = u, c = S.getAxesPermutation(l, o), p = c != null, d = i.shouldExecuteOnCPU([s]), h = [], f = s;
   if (p) {
     if (d) {
       let x = i.texData.get(f.dataId).values, k = new Array(o);
       for (let E = 0; E < k.length; E++)
         k[E] = s.shape[c[E]];
-      let T = wv(x, s.shape, s.dtype, c, k);
+      let T = Iv(x, s.shape, s.dtype, c, k);
       f = i.makeTensorInfo(k, s.dtype);
       let N = i.texData.get(f.dataId);
       N.values = T;
     } else
-      f = Xp(s, c, i);
+      f = Qp(s, c, i);
     h.push(f), l = S.getInnerMostAxes(l.length, o);
   }
   S.assertAxesAreInnerMostDims("sum", l, o);
   let [m, g] = S.computeOutAndReduceShapes(f.shape, l), b = m;
   r && (b = S.expandShapeToKeepDim(m, u));
-  let y = JZ(f, g, b, i);
+  let y = l7(f, g, b, i);
   for (let v of h)
     i.disposeIntermediateTensorInfo(v);
   return y;
 } };
-function t7(e) {
+function d7(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = S.getAxesPermutation(l, o), p = r;
   c != null && (p = qt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = S.getInnerMostAxes(l.length, r.shape.length)), S.assertAxesAreInnerMostDims("min", l, o);
-  let [d, h] = S.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = xi(m, m.dtype, "min", n), b;
+  let [d, h] = S.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = Ii(m, m.dtype, "min", n), b;
   if (i) {
     let y = S.expandShapeToKeepDim(d, u);
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: y } });
@@ -30082,19 +30140,19 @@ function t7(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var n7 = { kernelName: ja, backendName: "webgl", kernelFunc: t7 };
-var s7 = L1 + `
+var p7 = { kernelName: Xa, backendName: "webgl", kernelFunc: d7 };
+var h7 = G1 + `
   return min(a, b);
 `;
-var r7 = `
+var f7 = `
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  ` + jp + `
+  ` + Xp + `
   return result;
 `;
-var a7 = jt({ opSnippet: s7, packedOpSnippet: r7, cpuKernelImpl: ZK });
-var i7 = { kernelName: Ka, backendName: "webgl", kernelFunc: a7 };
-var o7 = class {
+var m7 = jt({ opSnippet: h7, packedOpSnippet: f7, cpuKernelImpl: uX });
+var g7 = { kernelName: Ya, backendName: "webgl", kernelFunc: m7 };
+var b7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = t.map((l, c) => l[0] + e[c] + l[1]);
     let s = e.length, r = rt(s), a = t.map((l) => l[0]).join(","), i = t.map((l, c) => l[0] + e[c]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s), u = n === "reflect" ? 0 : 1;
@@ -30134,7 +30192,7 @@ var o7 = class {
     `;
   }
 };
-var u7 = class {
+var y7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t.map((h, f) => h[0] + e[f] + h[1]);
     let s = e.length, r = rt(s), a = t.map((h) => h[0]).join(","), i = t.map((h, f) => h[0] + e[f]).join(","), o = ln("rc", s), u = ln("source", s), l = `${o[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = n === "reflect" ? 0 : 1, d = "";
@@ -30204,22 +30262,22 @@ var u7 = class {
     `;
   }
 };
-var l7 = ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s } = e, { paddings: r, mode: a } = n, i = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new u7(s.shape, r, a) : new o7(s.shape, r, a);
+var v7 = ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s } = e, { paddings: r, mode: a } = n, i = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new y7(s.shape, r, a) : new b7(s.shape, r, a);
   return t.runWebGLProgram(i, [s], s.dtype);
 };
-var c7 = { kernelName: Xa, backendName: "webgl", kernelFunc: l7 };
-var d7 = `if (b == 0.0) return NAN;
+var x7 = { kernelName: Qa, backendName: "webgl", kernelFunc: v7 };
+var w7 = `if (b == 0.0) return NAN;
   return mod(a, b);`;
-var p7 = `
+var k7 = `
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  ` + jp + `
+  ` + Xp + `
   return result;
 `;
-var h7 = jt({ opSnippet: d7, packedOpSnippet: p7 });
-var f7 = { kernelName: wl, backendName: "webgl", kernelFunc: h7 };
-var m7 = class {
+var I7 = jt({ opSnippet: w7, packedOpSnippet: k7 });
+var S7 = { kernelName: wl, backendName: "webgl", kernelFunc: I7 };
+var C7 = class {
   constructor(e, t, n) {
     this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, n], this.userCode = `
       void main() {
@@ -30244,12 +30302,12 @@ var m7 = class {
     `;
   }
 };
-var g7 = `
+var N7 = `
 if (a == b) {
   return 1.0;
 };
 return a / b;`;
-var b7 = `
+var T7 = `
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -30268,25 +30326,25 @@ var b7 = `
 
   return result;
 `;
-var i2 = jt({ opSnippet: g7, packedOpSnippet: b7, checkOutOfBounds: true });
-var y7 = { kernelName: Da, backendName: "webgl", kernelFunc: i2 };
-var Nw = "return a - b;";
-var o2 = jt({ opSnippet: Nw, packedOpSnippet: Nw, supportsComplex: true, cpuKernelImpl: fX });
-var v7 = { kernelName: ci, backendName: "webgl", kernelFunc: o2 };
-function u2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = a2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = S.expandShapeToKeepDim(o.shape, i), l = he({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = o2({ inputs: { a: r, b: l }, backend: n }), p = n2({ inputs: { x: c }, backend: n }), d = Yp({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = he({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = i2({ inputs: { a: p, b: h }, backend: n });
+var d2 = jt({ opSnippet: N7, packedOpSnippet: T7, checkOutOfBounds: true });
+var $7 = { kernelName: Oa, backendName: "webgl", kernelFunc: d2 };
+var Tw = "return a - b;";
+var p2 = jt({ opSnippet: Tw, packedOpSnippet: Tw, supportsComplex: true, cpuKernelImpl: SX });
+var _7 = { kernelName: hi, backendName: "webgl", kernelFunc: p2 };
+function h2(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = c2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = S.expandShapeToKeepDim(o.shape, i), l = he({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = p2({ inputs: { a: r, b: l }, backend: n }), p = o2({ inputs: { x: c }, backend: n }), d = Zp({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = he({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = d2({ inputs: { a: p, b: h }, backend: n });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
 }
-var x7 = { kernelName: ui, backendName: "webgl", kernelFunc: u2 };
-function w7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : u2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new m7(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
+var A7 = { kernelName: di, backendName: "webgl", kernelFunc: h2 };
+function E7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : h2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new C7(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
   return o || n.disposeIntermediateTensorInfo(u), h;
 }
-var k7 = { kernelName: Cg, backendName: "webgl", kernelFunc: w7 };
-var I7 = ss + `
+var R7 = { kernelName: Tg, backendName: "webgl", kernelFunc: E7 };
+var D7 = ss + `
   return -x;
 `;
-var S7 = `
+var F7 = `
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -30297,38 +30355,38 @@ var S7 = `
 
   return result;
 `;
-function C7(e) {
+function O7(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s])) {
-    let a = n.texData.get(s.dataId), [i, o] = eX(a.values, s.shape, s.dtype);
+    let a = n.texData.get(s.dataId), [i, o] = cX(a.values, s.shape, s.dtype);
     return n.makeTensorInfo(o, s.dtype, i);
   }
   let r;
-  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Zr(s.shape, S7) : r = new Hs(s.shape, I7), n.runWebGLProgram(r, [s], s.dtype);
+  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Jr(s.shape, F7) : r = new Hs(s.shape, D7), n.runWebGLProgram(r, [s], s.dtype);
 }
-var N7 = { kernelName: Io, backendName: "webgl", kernelFunc: C7 };
-var T7 = xs.nonMaxSuppressionV3Impl;
-function $7(e) {
+var P7 = { kernelName: Co, backendName: "webgl", kernelFunc: O7 };
+var z7 = xs.nonMaxSuppressionV3Impl;
+function M7(e) {
   S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = T7(l, c, i, o, u);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = z7(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var _7 = { kernelName: Co, backendName: "webgl", kernelFunc: $7 };
-var A7 = xs.nonMaxSuppressionV4Impl;
-function E7(e) {
+var L7 = { kernelName: To, backendName: "webgl", kernelFunc: M7 };
+var B7 = xs.nonMaxSuppressionV4Impl;
+function V7(e) {
   S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = A7(c, p, i, o, u, l);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = B7(c, p, i, o, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var R7 = { kernelName: kl, backendName: "webgl", kernelFunc: E7 };
-var D7 = xs.nonMaxSuppressionV5Impl;
-function F7(e) {
+var W7 = { kernelName: kl, backendName: "webgl", kernelFunc: V7 };
+var U7 = xs.nonMaxSuppressionV5Impl;
+function G7(e) {
   S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = D7(c, p, d, h, f, m);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = U7(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var O7 = { kernelName: No, backendName: "webgl", kernelFunc: F7 };
-var P7 = class {
+var H7 = { kernelName: $o, backendName: "webgl", kernelFunc: G7 };
+var q7 = class {
   constructor(e, t, n, s) {
     this.variableNames = ["indices"], this.outputShape = [e, t], this.userCode = `
       void main() {
@@ -30340,49 +30398,49 @@ var P7 = class {
     `;
   }
 };
-var z7 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = w.sizeFromShape(r.shape), l = new P7(u, a, i, o), c = he({ inputs: { x: r }, backend: n, attrs: { shape: [u] } }), p = n.runWebGLProgram(l, [c], r.dtype);
+var j7 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = w.sizeFromShape(r.shape), l = new q7(u, a, i, o), c = he({ inputs: { x: r }, backend: n, attrs: { shape: [u] } }), p = n.runWebGLProgram(l, [c], r.dtype);
   n.disposeIntermediateTensorInfo(c);
   let d = [...r.shape, a], h = he({ inputs: { x: p }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(p), h;
 };
-var M7 = { kernelName: $o, backendName: "webgl", kernelFunc: z7 };
-function Md(e) {
+var K7 = { kernelName: Ao, backendName: "webgl", kernelFunc: j7 };
+function Bd(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
-    let r = Jl({ inputs: { input: s }, backend: n }), a = Md({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = Rr({ inputs: { real: a, imag: o }, backend: n });
+    let r = Jl({ inputs: { input: s }, backend: n }), a = Bd({ inputs: { x: r }, backend: n }), i = Jp({ inputs: { input: s }, backend: n }), o = Bd({ inputs: { x: i }, backend: n }), u = Dr({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
     return ec({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var L7 = { kernelName: Ho, backendName: "webgl", kernelFunc: Md };
-function l2(e) {
+var X7 = { kernelName: qo, backendName: "webgl", kernelFunc: Bd };
+function f2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported under string dtype");
   if (s.dtype === "complex64") {
-    let r = Jl({ inputs: { input: s }, backend: n }), a = l2({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = Rr({ inputs: { real: a, imag: o }, backend: n });
+    let r = Jl({ inputs: { input: s }, backend: n }), a = f2({ inputs: { x: r }, backend: n }), i = Jp({ inputs: { input: s }, backend: n }), o = Bd({ inputs: { x: i }, backend: n }), u = Dr({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
     return ec({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var B7 = { kernelName: To, backendName: "webgl", kernelFunc: l2 };
-function V7(e) {
+var Y7 = { kernelName: _o, backendName: "webgl", kernelFunc: f2 };
+function Q7(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return qm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return Km({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = qm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = Km({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
-  }), l = Y1({ inputs: u, backend: n, attrs: { axis: r } });
+  }), l = t2({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var W7 = { kernelName: _o, backendName: "webgl", kernelFunc: V7 };
-var U7 = class {
+var Z7 = { kernelName: Eo, backendName: "webgl", kernelFunc: Q7 };
+var J7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((u, l) => u[0] + e[l] + u[1]);
     let s = e.length, r = rt(s), a = t.map((u) => u[0]).join(","), i = t.map((u, l) => u[0] + e[l]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s);
@@ -30418,7 +30476,7 @@ var U7 = class {
     `;
   }
 };
-var G7 = class {
+var eJ = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((f, m) => f[0] + e[m] + f[1]);
     let s = e.length, r = rt(s), a = t.map((f) => f[0]).join(","), i = t.map((f, m) => f[0] + e[m]).join(","), o = ln("rc", s), u = ln("source", s), l = `${o[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = [`${r} rc = outputLoc;`, `${o[s - 1]} += 1;
@@ -30451,17 +30509,17 @@ var G7 = class {
     `;
   }
 };
-var c2 = (e) => {
+var m2 = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
   if (w.sizeFromShape(r.shape) === 0) {
     let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
     return ec({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
   }
-  let o = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new G7(r.shape, a, i) : new U7(r.shape, a, i), u = [[i]];
+  let o = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new eJ(r.shape, a, i) : new J7(r.shape, a, i), u = [[i]];
   return n.runWebGLProgram(o, [r], r.dtype, u);
 };
-var H7 = { kernelName: Qa, backendName: "webgl", kernelFunc: c2 };
-var q7 = `
+var tJ = { kernelName: Ja, backendName: "webgl", kernelFunc: m2 };
+var nJ = `
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -30471,7 +30529,7 @@ var q7 = `
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
 `;
-var j7 = `
+var sJ = `
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -30485,20 +30543,20 @@ var j7 = `
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  ` + jp + `
+  ` + Xp + `
   return result;
 `;
-var K7 = jt({ opSnippet: q7, packedOpSnippet: j7 });
-var X7 = { kernelName: Za, backendName: "webgl", kernelFunc: K7 };
-function Y7(e) {
+var rJ = jt({ opSnippet: nJ, packedOpSnippet: sJ });
+var aJ = { kernelName: ei, backendName: "webgl", kernelFunc: rJ };
+function iJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = [], l = w.parseAxisParam(a, r.shape), c = l, p = S.getAxesPermutation(c, o), d = r;
   p != null && (d = qt({ inputs: { x: r }, backend: n, attrs: { perm: p } }), c = S.getInnerMostAxes(c.length, o), u.push(d)), S.assertAxesAreInnerMostDims("prod", c, o);
   let h;
   if (n.shouldExecuteOnCPU([d])) {
-    let f = n.texData.get(d.dataId).values, { outVals: m, outShape: g, outDtype: b } = nX(d.shape, d.dtype, f, c);
+    let f = n.texData.get(d.dataId).values, { outVals: m, outShape: g, outDtype: b } = pX(d.shape, d.dtype, f, c);
     h = n.makeTensorInfo(g, b, m);
   } else {
-    let [f, m] = S.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = he({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = cp(r.dtype), v = xi(b, y, "prod", n);
+    let [f, m] = S.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = he({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = pp(r.dtype), v = Ii(b, y, "prod", n);
     h = he({ inputs: { x: v }, backend: n, attrs: { shape: f } }), u.push(b), u.push(v);
   }
   if (i) {
@@ -30508,19 +30566,19 @@ function Y7(e) {
   }
   return u.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
 }
-var Q7 = { kernelName: Ao, backendName: "webgl", kernelFunc: Y7 };
-var d2 = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = sX(s, r, a, i);
+var oJ = { kernelName: ni, backendName: "webgl", kernelFunc: iJ };
+var g2 = (e) => {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = hX(s, r, a, i);
   return t.makeTensorInfo([o.length], i, o);
 };
-var Z7 = { kernelName: Il, backendName: "webgl", kernelFunc: d2 };
-var J7 = "return 1.0 / x;";
-var eJ = Ke({ opSnippet: J7 });
-var tJ = { kernelName: Sl, backendName: "webgl", kernelFunc: eJ };
-var nJ = ss + `
+var uJ = { kernelName: Il, backendName: "webgl", kernelFunc: g2 };
+var lJ = "return 1.0 / x;";
+var cJ = Ke({ opSnippet: lJ });
+var dJ = { kernelName: Sl, backendName: "webgl", kernelFunc: cJ };
+var pJ = ss + `
   return (x < 0.0) ? 0.0 : x;
 `;
-var sJ = `
+var hJ = `
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -30531,12 +30589,12 @@ var sJ = `
 
   return result;
 `;
-var rJ = Ke({ opSnippet: nJ, packedOpSnippet: sJ });
-var aJ = { kernelName: ei, backendName: "webgl", kernelFunc: rJ };
-var iJ = ss + `
+var fJ = Ke({ opSnippet: pJ, packedOpSnippet: hJ });
+var mJ = { kernelName: si, backendName: "webgl", kernelFunc: fJ };
+var gJ = ss + `
   return (x < 0.0) ? 0.0 : min(6.0, x);
 `;
-var oJ = `
+var bJ = `
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -30547,9 +30605,9 @@ var oJ = `
 
   return result;
 `;
-var uJ = Ke({ opSnippet: iJ, packedOpSnippet: oJ });
-var lJ = { kernelName: ni, backendName: "webgl", kernelFunc: uJ };
-var cJ = class {
+var yJ = Ke({ opSnippet: gJ, packedOpSnippet: bJ });
+var vJ = { kernelName: ai, backendName: "webgl", kernelFunc: yJ };
+var xJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.outputShape = [];
     let [a, i, o, u] = e;
@@ -30591,7 +30649,7 @@ var cJ = class {
     `;
   }
 };
-var dJ = class {
+var wJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
     let [a, i, o, u] = e;
@@ -30677,12 +30735,12 @@ var dJ = class {
     `;
   }
 };
-function pJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new dJ(r.shape, u, l, a, i) : new cJ(r.shape, u, l, a, i);
+function kJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new wJ(r.shape, u, l, a, i) : new xJ(r.shape, u, l, a, i);
   return n.runWebGLProgram(c, [r], "float32");
 }
-var hJ = { kernelName: ti, backendName: "webgl", kernelFunc: pJ };
-var fJ = class {
+var IJ = { kernelName: ri, backendName: "webgl", kernelFunc: kJ };
+var SJ = class {
   constructor(e, t, n) {
     this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
     let [, s, r] = t, [, a, i] = e, o = [n && a > 1 ? s - 1 : s, n && i > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && i > 1 ? i - 1 : i], l = o[0] / u[0], c = o[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
@@ -30770,12 +30828,12 @@ var fJ = class {
     `;
   }
 };
-function mJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new fJ(a.shape, r.shape, i);
+function CJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new SJ(a.shape, r.shape, i);
   return n.runWebGLProgram(o, [a], a.dtype);
 }
-var gJ = { kernelName: Tg, backendName: "webgl", kernelFunc: mJ };
-var bJ = class {
+var NJ = { kernelName: _g, backendName: "webgl", kernelFunc: CJ };
+var TJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.outputShape = [];
     let [a, i, o, u] = e;
@@ -30806,7 +30864,7 @@ var bJ = class {
     `;
   }
 };
-var yJ = class {
+var $J = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
     let [a, i, o, u] = e;
@@ -30856,12 +30914,12 @@ var yJ = class {
     `;
   }
 };
-function vJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new yJ(r.shape, u, l, a, i) : new bJ(r.shape, u, l, a, i);
+function _J(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new $J(r.shape, u, l, a, i) : new TJ(r.shape, u, l, a, i);
   return n.runWebGLProgram(c, [r], r.dtype);
 }
-var xJ = { kernelName: Cl, backendName: "webgl", kernelFunc: vJ };
-var wJ = class {
+var AJ = { kernelName: Cl, backendName: "webgl", kernelFunc: _J };
+var EJ = class {
   constructor(e, t, n) {
     this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
     let [, s, r] = t, [, a, i] = e, o = [n && a > 1 ? s - 1 : s, n && i > 1 ? r - 1 : r], u = [n && a > 1 ? a - 1 : a, n && i > 1 ? i - 1 : i], l = o[0] / u[0], c = o[1] / u[1], p = 1 / l, d = 1 / c, h = Math.ceil(p) * 2 + 2, f = Math.ceil(d) * 2 + 2;
@@ -30938,12 +30996,12 @@ var wJ = class {
     `;
   }
 };
-function kJ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new wJ(a.shape, r.shape, i);
+function RJ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new EJ(a.shape, r.shape, i);
   return n.runWebGLProgram(o, [a], a.dtype);
 }
-var IJ = { kernelName: Ng, backendName: "webgl", kernelFunc: kJ };
-var SJ = class {
+var DJ = { kernelName: $g, backendName: "webgl", kernelFunc: RJ };
+var FJ = class {
   constructor(e, t) {
     this.variableNames = ["x"];
     let n = e.length;
@@ -30967,7 +31025,7 @@ var SJ = class {
     `;
   }
 };
-var CJ = class {
+var OJ = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true;
     let n = e.length;
@@ -31025,15 +31083,15 @@ var CJ = class {
     }
   }
 };
-function NJ(e) {
+function PJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = r.shape.length, o = w.parseAxisParam(a, r.shape);
   if (i === 0)
     return kn({ inputs: { x: r }, backend: n });
-  let u = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new CJ(r.shape, o) : new SJ(r.shape, o);
+  let u = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new OJ(r.shape, o) : new FJ(r.shape, o);
   return n.runWebGLProgram(u, [r], r.dtype);
 }
-var TJ = { kernelName: Ro, backendName: "webgl", kernelFunc: NJ };
-var $J = class {
+var zJ = { kernelName: Do, backendName: "webgl", kernelFunc: PJ };
+var MJ = class {
   constructor(e, t) {
     this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
     let n = e[1], s = e[2];
@@ -31061,11 +31119,11 @@ var $J = class {
     `;
   }
 };
-var _J = { kernelName: qo, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new $J(s.shape, a), [l, c] = S.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
+var LJ = { kernelName: jo, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new MJ(s.shape, a), [l, c] = S.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
   return o.runWebGLProgram(u, [s], s.dtype, p);
 } };
-var AJ = `
+var BJ = `
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -31081,12 +31139,12 @@ var AJ = `
     }
   }
 `;
-var EJ = Ke({ opSnippet: AJ });
-var RJ = { kernelName: Do, backendName: "webgl", kernelFunc: EJ };
-var DJ = "return inversesqrt(x);";
-var FJ = Ke({ opSnippet: DJ, cpuKernelImpl: rX });
-var OJ = { kernelName: si, backendName: "webgl", kernelFunc: FJ };
-var p2 = class {
+var VJ = Ke({ opSnippet: BJ });
+var WJ = { kernelName: Fo, backendName: "webgl", kernelFunc: VJ };
+var UJ = "return inversesqrt(x);";
+var GJ = Ke({ opSnippet: UJ, cpuKernelImpl: fX });
+var HJ = { kernelName: ii, backendName: "webgl", kernelFunc: GJ };
+var b2 = class {
   constructor(e, t, n, s, r, a, i = true) {
     this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
     let o = rt(r.length), u = rt(a.length), l = "";
@@ -31117,15 +31175,15 @@ var p2 = class {
       `;
   }
 };
-function PJ(e) {
+function qJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = S.calculateShapes(a, r, i), d = [p / l, l];
   if (p === 0)
     return n.makeTensorInfo(i, r.dtype);
-  let h = he({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = he({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = n.makeTensorInfo([], "float32", new Float32Array([0])), g = new p2(u, o, h.shape.length, f.shape.length, c, d), b = n.runWebGLProgram(g, [f, h, m], f.dtype), y = he({ inputs: { x: b }, backend: n, attrs: { shape: i } });
+  let h = he({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = he({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = n.makeTensorInfo([], "float32", new Float32Array([0])), g = new b2(u, o, h.shape.length, f.shape.length, c, d), b = n.runWebGLProgram(g, [f, h, m], f.dtype), y = he({ inputs: { x: b }, backend: n, attrs: { shape: i } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(m), y;
 }
-var zJ = { kernelName: Fo, backendName: "webgl", kernelFunc: PJ };
-var MJ = class {
+var jJ = { kernelName: Oo, backendName: "webgl", kernelFunc: qJ };
+var KJ = class {
   constructor(e, t, n) {
     this.variableNames = ["c", "a", "b"], this.outputShape = t;
     let s, r;
@@ -31153,24 +31211,24 @@ var MJ = class {
     `;
   }
 };
-function LJ(e) {
-  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new MJ(s.shape.length, r.shape, r.shape.length);
+function XJ(e) {
+  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new KJ(s.shape.length, r.shape, r.shape.length);
   return n.runWebGLProgram(i, [s, r, a], cn(r.dtype, a.dtype));
 }
-var BJ = { kernelName: Oo, backendName: "webgl", kernelFunc: LJ };
-var VJ = `
+var YJ = { kernelName: Po, backendName: "webgl", kernelFunc: XJ };
+var QJ = `
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${S.SELU_SCALEALPHA};
   float scale = ${S.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
 `;
-var WJ = Ke({ opSnippet: VJ });
-var UJ = { kernelName: Nl, backendName: "webgl", kernelFunc: WJ };
-var GJ = ou + `
+var ZJ = Ke({ opSnippet: QJ });
+var JJ = { kernelName: Nl, backendName: "webgl", kernelFunc: ZJ };
+var eee = uu + `
   return 1.0 / (1.0 + exp(-1.0 * x));
 `;
-var HJ = `
+var tee = `
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
   bvec4 isNaN = isnan(x);
 
@@ -31181,26 +31239,26 @@ var HJ = `
 
   return result;
 `;
-var qJ = Ke({ opSnippet: GJ, packedOpSnippet: HJ, cpuKernelImpl: aX });
-var jJ = { kernelName: ai, backendName: "webgl", kernelFunc: qJ };
-var KJ = `
+var nee = Ke({ opSnippet: eee, packedOpSnippet: tee, cpuKernelImpl: mX });
+var see = { kernelName: ui, backendName: "webgl", kernelFunc: nee };
+var ree = `
   if (isnan(x)) { return 0.0; }
   return sign(x);
 `;
-var XJ = Ke({ opSnippet: KJ });
-var YJ = { kernelName: Tl, backendName: "webgl", kernelFunc: XJ };
-var QJ = ou + `
+var aee = Ke({ opSnippet: ree });
+var iee = { kernelName: Tl, backendName: "webgl", kernelFunc: aee };
+var oee = uu + `
   return sin(x);
 `;
-var ZJ = Ke({ opSnippet: QJ });
-var JJ = { kernelName: ri, backendName: "webgl", kernelFunc: ZJ };
-var eee = `
+var uee = Ke({ opSnippet: oee });
+var lee = { kernelName: oi, backendName: "webgl", kernelFunc: uee };
+var cee = `
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
 `;
-var tee = Ke({ opSnippet: eee });
-var nee = { kernelName: zo, backendName: "webgl", kernelFunc: tee };
-var see = `
+var dee = Ke({ opSnippet: cee });
+var pee = { kernelName: Mo, backendName: "webgl", kernelFunc: dee };
+var hee = `
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -31221,20 +31279,20 @@ var see = `
   }
   return result;
 `;
-var ree = Ke({ opSnippet: see });
-var aee = { kernelName: $l, backendName: "webgl", kernelFunc: ree };
-var iee = (e) => {
+var fee = Ke({ opSnippet: hee });
+var mee = { kernelName: $l, backendName: "webgl", kernelFunc: fee };
+var gee = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
   let o = a.reduce((b, y) => b * y), u = [[0, 0]];
   u.push(...i);
   for (let b = 1 + a.length; b < r.shape.length; ++b)
     u.push([0, 0]);
-  let l = [], c = c2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = S.getReshaped(c.shape, a, o, false), d = S.getPermuted(p.length, a.length, false), h = S.getReshapedPermuted(c.shape, a, o, false), f = he({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = qt({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: h } });
+  let l = [], c = m2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = S.getReshaped(c.shape, a, o, false), d = S.getPermuted(p.length, a.length, false), h = S.getReshapedPermuted(c.shape, a, o, false), f = he({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = qt({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
 };
-var oee = { kernelName: Mo, backendName: "webgl", kernelFunc: iee };
-function uee(e) {
+var bee = { kernelName: Lo, backendName: "webgl", kernelFunc: gee };
+function yee(e) {
   let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
   if (a.shape.length !== 1)
     throw new Error(`Dense shape must be a vector, saw:
@@ -31248,11 +31306,11 @@ function uee(e) {
   if (i.shape.length !== 0)
     throw new Error(`Default value must be a scalar, saw:
         ${i.shape}`);
-  let o = n.readSync(s.dataId), u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = n.readSync(i.dataId)[0], [p, d, h, f, m] = oX(o, s.shape, s.dtype, u, r.dtype, l, c);
+  let o = n.readSync(s.dataId), u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = n.readSync(i.dataId)[0], [p, d, h, f, m] = bX(o, s.shape, s.dtype, u, r.dtype, l, c);
   return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
 }
-var lee = { kernelName: sp, backendName: "webgl", kernelFunc: uee };
-function cee(e) {
+var vee = { kernelName: ap, backendName: "webgl", kernelFunc: yee };
+function xee(e) {
   let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
   if (s.shape.length !== 2)
     throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);
@@ -31260,11 +31318,11 @@ function cee(e) {
     throw new Error(`Input shape should be a vector but received shape ${r.shape}`);
   if (a.shape.length !== 1)
     throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
-  let i = Array.from(n.readSync(r.dataId)), o = n.readSync(s.dataId), u = Array.from(n.readSync(a.dataId)), [l, c, p] = uX(o, s.shape, s.dtype, i, u);
+  let i = Array.from(n.readSync(r.dataId)), o = n.readSync(s.dataId), u = Array.from(n.readSync(a.dataId)), [l, c, p] = yX(o, s.shape, s.dtype, i, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var dee = { kernelName: _l, backendName: "webgl", kernelFunc: cee };
-function pee(e) {
+var wee = { kernelName: _l, backendName: "webgl", kernelFunc: xee };
+function kee(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -31274,11 +31332,11 @@ function pee(e) {
   if (a.shape.length !== 1)
     throw new Error(`Segment ids should be a vector but received shape
               ${a.shape}`);
-  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = F1(i, s.shape, s.dtype, o, u, true);
+  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = L1(i, s.shape, s.dtype, o, u, true);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var hee = { kernelName: rp, backendName: "webgl", kernelFunc: pee };
-function fee(e) {
+var Iee = { kernelName: ip, backendName: "webgl", kernelFunc: kee };
+function See(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
     throw new Error("Data should be at least 1 dimensional but received scalar");
@@ -31288,42 +31346,42 @@ function fee(e) {
   if (a.shape.length !== 1)
     throw new Error(`Segment ids should be a vector but received shape
              ${a.shape}`);
-  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = F1(i, s.shape, s.dtype, o, u);
+  let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = L1(i, s.shape, s.dtype, o, u);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var mee = { kernelName: ap, backendName: "webgl", kernelFunc: fee };
-function gee(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = S.calculateShapes(a, r, o), d = false, h = new p2(l, u, r.shape.length, a.shape.length, c, [p, 1], d), f = n.runWebGLProgram(h, [a, r, i], a.dtype), m = he({ inputs: { x: f }, backend: n, attrs: { shape: o } });
+var Cee = { kernelName: op, backendName: "webgl", kernelFunc: See };
+function Nee(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = S.calculateShapes(a, r, o), d = false, h = new b2(l, u, r.shape.length, a.shape.length, c, [p, 1], d), f = n.runWebGLProgram(h, [a, r, i], a.dtype), m = he({ inputs: { x: f }, backend: n, attrs: { shape: o } });
   return n.disposeIntermediateTensorInfo(f), m;
 }
-var bee = { kernelName: ip, backendName: "webgl", kernelFunc: gee };
-function yee(e) {
+var Tee = { kernelName: up, backendName: "webgl", kernelFunc: Nee };
+function $ee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = S.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
   return u.map((d) => {
     let h = [...p];
     h[o] = d;
-    let f = uu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    let f = lu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
     return c[o] += d, f;
   });
 }
-var vee = { kernelName: Lo, backendName: "webgl", kernelFunc: yee };
-var Tw = "return sqrt(x);";
-var xee = Ke({ opSnippet: Tw, packedOpSnippet: Tw, cpuKernelImpl: lX });
-var wee = { kernelName: ii, backendName: "webgl", kernelFunc: xee };
-var kee = "return x * x;";
-var Iee = Ke({ opSnippet: kee });
-var See = { kernelName: Al, backendName: "webgl", kernelFunc: Iee };
-var $w = "return (a - b) * (a - b);";
-var Cee = jt({ opSnippet: $w, packedOpSnippet: $w });
-var Nee = { kernelName: li, backendName: "webgl", kernelFunc: Cee };
-function Tee({ inputs: e, attrs: t, backend: n }) {
+var _ee = { kernelName: Bo, backendName: "webgl", kernelFunc: $ee };
+var $w = "return sqrt(x);";
+var Aee = Ke({ opSnippet: $w, packedOpSnippet: $w, cpuKernelImpl: vX });
+var Eee = { kernelName: li, backendName: "webgl", kernelFunc: Aee };
+var Ree = "return x * x;";
+var Dee = Ke({ opSnippet: Ree });
+var Fee = { kernelName: Al, backendName: "webgl", kernelFunc: Dee };
+var _w = "return (a - b) * (a - b);";
+var Oee = jt({ opSnippet: _w, packedOpSnippet: _w });
+var Pee = { kernelName: pi, backendName: "webgl", kernelFunc: Oee };
+function zee({ inputs: e, attrs: t, backend: n }) {
   let { x: s } = e, r = ss + `
     return x > 0.0 ? 1.0 : float(${t.alpha});
   `, a = new Hs(s.shape, r);
   return n.runWebGLProgram(a, [s], s.dtype);
 }
-var $ee = { kernelName: hi, backendName: "webgl", kernelFunc: Tee };
-var _ee = class {
+var Mee = { kernelName: gi, backendName: "webgl", kernelFunc: zee };
+var Lee = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = n;
     let s = n.length, r = rt(n.length), a = rt(n.length), i = "";
@@ -31344,31 +31402,31 @@ var _ee = class {
     `;
   }
 };
-function Aee(e) {
+function Bee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
   if (m)
     k = he({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let N = wt.computeOutShape(y, v, x), E = uu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: N } });
+    let N = wt.computeOutShape(y, v, x), E = lu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: N } });
     k = he({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(E);
   } else if (n.shouldExecuteOnCPU([r])) {
-    let E = n.readSync(r.dataId), A = De(r.shape, r.dtype, E), P = cX(h, A, x, y);
+    let E = n.readSync(r.dataId), A = De(r.shape, r.dtype, E), P = xX(h, A, x, y);
     k = n.makeTensorInfo(f, r.dtype, P.values);
   } else {
-    let E = new _ee(y, x, h);
+    let E = new Lee(y, x, h);
     k = n.runWebGLProgram(E, [r], r.dtype);
   }
   let T = he({ inputs: { x: k }, backend: n, attrs: { shape: f } });
   return n.disposeIntermediateTensorInfo(k), T;
 }
-var Eee = { kernelName: Bo, backendName: "webgl", kernelFunc: Aee };
-function Ree(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = dX(d, h, r, a, i, o, u, l);
+var Vee = { kernelName: Vo, backendName: "webgl", kernelFunc: Bee };
+function Wee(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = wX(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var Dee = { kernelName: op, backendName: "webgl", kernelFunc: Ree };
-function Fee(e) {
+var Uee = { kernelName: lp, backendName: "webgl", kernelFunc: Wee };
+function Gee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
@@ -31376,37 +31434,37 @@ function Fee(e) {
     throw new Error(`Input must be a vector, got shape: ${a.shape}`);
   if (i.shape.length !== 0)
     throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);
-  let o = n.readSync(a.dataId), u = n.readSync(i.dataId)[0], [l, c, p] = pX(o, u, r), d = c.length;
+  let o = n.readSync(a.dataId), u = n.readSync(i.dataId)[0], [l, c, p] = kX(o, u, r), d = c.length;
   return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
 }
-var Oee = { kernelName: $g, backendName: "webgl", kernelFunc: Fee };
-function Pee(e) {
+var Hee = { kernelName: Ag, backendName: "webgl", kernelFunc: Gee };
+function qee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
   if (a.dtype !== "string")
     throw new Error("Input must be of datatype string");
   if (r <= 0)
     throw new Error("Number of buckets must be at least 1");
-  let i = n.readSync(a.dataId), o = hX(i, r);
+  let i = n.readSync(a.dataId), o = IX(i, r);
   return n.makeTensorInfo(a.shape, "int32", o);
 }
-var zee = { kernelName: _g, backendName: "webgl", kernelFunc: Pee };
-var Mee = "return tan(x);";
-var Lee = Ke({ opSnippet: Mee });
-var Bee = { kernelName: Vo, backendName: "webgl", kernelFunc: Lee };
-var Vee = `
+var jee = { kernelName: Eg, backendName: "webgl", kernelFunc: qee };
+var Kee = "return tan(x);";
+var Xee = Ke({ opSnippet: Kee });
+var Yee = { kernelName: Wo, backendName: "webgl", kernelFunc: Xee };
+var Qee = `
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
 `;
-var Wee = Ke({ opSnippet: Vee });
-var Uee = { kernelName: di, backendName: "webgl", kernelFunc: Wee };
-var Gee = class {
+var Zee = Ke({ opSnippet: Qee });
+var Jee = { kernelName: fi, backendName: "webgl", kernelFunc: Zee };
+var ete = class {
   constructor(e, t) {
     this.variableNames = ["A"];
     let n = new Array(e.length);
     for (let a = 0; a < n.length; a++)
       n[a] = e[a] * t[a];
     this.outputShape = n, this.rank = n.length;
-    let s = rt(this.rank), r = Hee(e);
+    let s = rt(this.rank), r = tte(e);
     this.userCode = `
       void main() {
         ${s} resRC = getOutputCoords();
@@ -31415,7 +31473,7 @@ var Gee = class {
     `;
   }
 };
-function Hee(e) {
+function tte(e) {
   let t = e.length;
   if (t > 5)
     throw Error(`Tile for rank ${t} is not yet supported`);
@@ -31426,17 +31484,17 @@ function Hee(e) {
     s.push(`imod(${n[r]}, ${e[r]})`);
   return s.join();
 }
-function h2(e) {
+function y2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
   if (r.dtype === "string" || r.shape.length > 5) {
-    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = mX(c, a);
+    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = CX(c, a);
     return n.makeTensorInfo(p.shape, p.dtype, p.values);
   }
-  let i = new Gee(r.shape, a);
+  let i = new ete(r.shape, a);
   return n.runWebGLProgram(i, [r], r.dtype);
 }
-var qee = { kernelName: Cr, backendName: "webgl", kernelFunc: h2 };
-var jee = class {
+var nte = { kernelName: Nr, backendName: "webgl", kernelFunc: y2 };
+var ste = class {
   constructor(e) {
     this.variableNames = ["x", "indices"], this.customUniforms = [{ name: "n", type: "int" }, { name: "firstPass", type: "int" }, { name: "negativeInf", type: "float" }, { name: "dir", type: "int" }, { name: "inc", type: "int" }], this.outputShape = e, this.userCode = `
        void main() {
@@ -31481,7 +31539,7 @@ var jee = class {
      `;
   }
 };
-var Kee = class {
+var rte = class {
   constructor(e) {
     this.variableNames = ["x", "indices"], this.customUniforms = [{ name: "n", type: "int" }, { name: "firstPass", type: "int" }, { name: "k", type: "int" }], this.outputShape = e, this.userCode = `
     void main() {
@@ -31520,19 +31578,19 @@ var Kee = class {
      `;
   }
 };
-function Ur(e, t) {
+function Gr(e, t) {
   t !== null && e.disposeIntermediateTensorInfo(t);
 }
-function _w(e) {
+function Aw(e) {
   let t = 1;
   for (; t < e; )
     t *= 2;
   return t;
 }
-function Xee(e) {
+function ate(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), u = X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), l = r.shape, c = l[l.length - 1];
   if (n.shouldExecuteOnCPU([r]) || c < o || a > u) {
-    let P = n.readSync(r.dataId), [R, F] = gX(P, l, r.dtype, a, i);
+    let P = n.readSync(r.dataId), [R, F] = NX(P, l, r.dtype, a, i);
     return [n.makeTensorInfo(R.shape, R.dtype, R.values), n.makeTensorInfo(F.shape, F.dtype, F.values)];
   }
   if (a === 0)
@@ -31540,10 +31598,10 @@ function Xee(e) {
   if (c === 1)
     return [r, ec({ attrs: { shape: l, dtype: "int32", value: 0 }, backend: n })];
   let p = n.texData.get(r.dataId), d = p !== null && p.isPacked, h = d ? n.unpackTensor(r) : r, m = w.sizeFromShape(l) / c, g = he({ inputs: { x: h }, attrs: { shape: [m, c] }, backend: n });
-  d && Ur(n, h);
-  let b = _w(a), y = _w(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, R, F) => {
-    let $ = x(), z = new jee(F), q = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [R]], K = v;
-    v = n.runWebGLProgram(z, $, "int32", q), Ur(n, K);
+  d && Gr(n, h);
+  let b = Aw(a), y = Aw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, R, F) => {
+    let $ = x(), z = new ste(F), q = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [R]], K = v;
+    v = n.runWebGLProgram(z, $, "int32", q), Gr(n, K);
   };
   for (let P = 1; P < b; P *= 2) {
     let R = P * 2;
@@ -31551,23 +31609,23 @@ function Xee(e) {
       k(R, F, [m, y]);
   }
   for (let P = y; P > b; P /= 2) {
-    let R = x(), F = new Kee([m, P / 2]), z = [[c], [v === null ? 1 : 0], [b]], W = v;
-    v = n.runWebGLProgram(F, R, "int32", z), Ur(n, W);
+    let R = x(), F = new rte([m, P / 2]), z = [[c], [v === null ? 1 : 0], [b]], W = v;
+    v = n.runWebGLProgram(F, R, "int32", z), Gr(n, W);
     let q = b / 2, K = q * 2;
     for (let Y = q; Y >= 1; Y /= 2)
       k(K, Y, v.shape);
   }
   let T = v;
-  v = uu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Ur(n, T);
-  let N = r2({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
-  Ur(n, g);
+  v = lu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Gr(n, T);
+  let N = l2({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
+  Gr(n, g);
   let E = l.slice(0, -1);
-  E.push(a), T = v, v = he({ inputs: { x: v }, attrs: { shape: E }, backend: n }), Ur(n, T);
+  E.push(a), T = v, v = he({ inputs: { x: v }, attrs: { shape: E }, backend: n }), Gr(n, T);
   let A = N;
-  return N = he({ inputs: { x: N }, attrs: { shape: E }, backend: n }), Ur(n, A), [N, v];
+  return N = he({ inputs: { x: N }, attrs: { shape: E }, backend: n }), Gr(n, A), [N, v];
 }
-var Yee = { kernelName: Wo, backendName: "webgl", kernelFunc: Xee };
-var Qee = class {
+var ite = { kernelName: Uo, backendName: "webgl", kernelFunc: ate };
+var ote = class {
   constructor(e, t, n, s, r, a) {
     this.variableNames = ["Image", "Transforms"], this.outputShape = a;
     let i = n === "nearest" ? 1 : 2, o;
@@ -31703,19 +31761,19 @@ var Qee = class {
         `;
   }
 };
-function Zee(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Qee(p, d, i, o, u, g);
+function ute(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new ote(p, d, i, o, u, g);
   return n.runWebGLProgram(b, [r, a], "float32");
 }
-var Jee = { kernelName: Uo, backendName: "webgl", kernelFunc: Zee };
-function ete(e) {
+var lte = { kernelName: Go, backendName: "webgl", kernelFunc: ute };
+function cte(e) {
   let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
-  nu(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
-  let i = s.readSync(a.dataId), { outputValues: o, outputShape: u, indices: l } = bX(i, r, a.shape, a.dtype);
+  su(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
+  let i = s.readSync(a.dataId), { outputValues: o, outputShape: u, indices: l } = TX(i, r, a.shape, a.dtype);
   return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
 }
-var tte = { kernelName: Ag, backendName: "webgl", kernelFunc: ete };
-function nte(e) {
+var dte = { kernelName: Rg, backendName: "webgl", kernelFunc: cte };
+function pte(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
   let i = r, o = i.shape.length, u = r.shape[a], l = new Array(o - 1), c = 0;
@@ -31726,13 +31784,13 @@ function nte(e) {
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = uu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = lu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var ste = { kernelName: Go, backendName: "webgl", kernelFunc: nte };
-var rte = class {
+var hte = { kernelName: Ho, backendName: "webgl", kernelFunc: pte };
+var fte = class {
   constructor(e, t) {
     this.variableNames = ["x", "segmentIds"];
     let n = e.windowSize, s = e.batchSize, r = e.inSize, a = e.numSegments, i = a * Math.ceil(r / n);
@@ -31849,16 +31907,16 @@ var rte = class {
     `;
   }
 };
-function ate(e) {
+function mte(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s, o = r.shape.length, u = [], l = 0, c = S.getAxesPermutation([l], o), p = r;
   c != null && (p = qt({ inputs: { x: r }, backend: n, attrs: { perm: c } }), u.push(p), l = S.getInnerMostAxes(1, o)[0]);
   let d = S.segment_util.computeOutShape(p.shape, l, i), h = w.sizeFromShape([p.shape[l]]), f = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, h] } });
   u.push(f);
-  let m = cp(r.dtype), g = (x, k, T, N, E) => {
-    let A = x.shape[0], P = x.shape[1], R = S.segment_util.segOpComputeOptimalWindowSize(P, E), F = { windowSize: R, inSize: P, batchSize: A, numSegments: E }, $ = new rte(F, k), z = n.compileAndRun($, [x, T], N);
+  let m = pp(r.dtype), g = (x, k, T, N, E) => {
+    let A = x.shape[0], P = x.shape[1], R = S.segment_util.segOpComputeOptimalWindowSize(P, E), F = { windowSize: R, inSize: P, batchSize: A, numSegments: E }, $ = new fte(F, k), z = n.compileAndRun($, [x, T], N);
     if (u.push(z), z.shape[1] === E)
       return z;
-    let W = d2({ backend: n, attrs: { start: 0, stop: E, step: 1, dtype: "float32" } }), q = h2({ inputs: { x: W }, backend: n, attrs: { reps: [P / R] } });
+    let W = g2({ backend: n, attrs: { start: 0, stop: E, step: 1, dtype: "float32" } }), q = y2({ inputs: { x: W }, backend: n, attrs: { reps: [P / R] } });
     return u.push(W), u.push(q), g(z, k, q, N, E);
   }, b = g(f, "unsortedSegmentSum", a, m, i), y = he({ inputs: { x: b }, backend: n, attrs: { shape: d } }), v = y;
   if (c != null) {
@@ -31868,9 +31926,9 @@ function ate(e) {
   }
   return u.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
 }
-var ite = { kernelName: up, backendName: "webgl", kernelFunc: ate };
-var ote = [d8, h8, g8, v8, w8, S8, N8, $8, R8, F8, z8, B8, U8, j8, Y8, Z8, eY, rY, iY, uY, pY, vY, wY, IY, _Y, EY, OY, qX, MY, UY, jY, JY, t9, s9, a9, o9, c9, h9, g9, y9, x9, k9, C9, T9, E9, D9, P9, L9, V9, H9, X9, J9, nQ, aQ, iQ, uQ, cQ, pQ, fQ, gQ, xQ, IQ, NQ, $Q, EQ, FQ, MQ, WQ, HX, GQ, VY, jQ, YQ, JQ, KX, sZ, oZ, lZ, hZ, gZ, xZ, IZ, TZ, EZ, FZ, PZ, BZ, WZ, GZ, KZ, YZ, ZZ, e7, n7, i7, c7, f7, k7, JX, N7, _7, R7, O7, CY, M7, B7, W7, H7, X7, YX, Q7, Z7, NY, y7, tJ, aJ, lJ, t8, hJ, gJ, xJ, IJ, TJ, _J, RJ, OJ, zJ, BJ, UJ, jJ, YJ, JJ, nee, bY, x7, aee, oee, lee, dee, hee, mee, bee, vee, wee, See, Nee, $ee, Eee, Dee, Oee, zee, v7, u8, Bee, Uee, qee, Yee, Jee, l8, tte, ste, ite, L7];
-for (let e of ote)
+var gte = { kernelName: cp, backendName: "webgl", kernelFunc: mte };
+var bte = [w8, I8, N8, _8, E8, F8, P8, M8, W8, G8, j8, Y8, J8, sY, iY, uY, cY, fY, gY, yY, kY, _Y, EY, DY, LY, VY, HY, n8, KY, JY, s9, l9, d9, h9, m9, b9, x9, I9, N9, $9, A9, R9, O9, z9, V9, U9, q9, X9, Q9, tQ, aQ, lQ, pQ, mQ, gQ, yQ, xQ, kQ, SQ, NQ, AQ, DQ, PQ, MQ, VQ, GQ, KQ, ZQ, t8, eZ, QY, sZ, iZ, lZ, r8, hZ, bZ, vZ, IZ, NZ, AZ, DZ, zZ, VZ, GZ, qZ, YZ, ZZ, e7, r7, i7, u7, c7, p7, g7, x7, S7, R7, l8, P7, L7, W7, H7, OY, K7, Y7, Z7, tJ, aJ, i8, oJ, uJ, PY, $7, dJ, mJ, vJ, d8, IJ, NJ, AJ, DJ, zJ, LJ, WJ, HJ, jJ, YJ, JJ, see, iee, lee, pee, TY, A7, mee, bee, vee, wee, Iee, Cee, Tee, _ee, Eee, Fee, Pee, Mee, Vee, Uee, Hee, jee, _7, y8, Yee, Jee, nte, ite, lte, v8, dte, hte, gte, X7];
+for (let e of bte)
   El(e);
 var zs = X();
 zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
@@ -31883,31 +31941,31 @@ zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
 zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
 zs.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
 zs.registerFlag("WEBGPU_USE_IMPORT", () => false);
-var ute = "return a + b;";
-var lte = "return areal * breal - aimag * bimag;";
-var cte = "return areal * bimag + aimag * breal;";
-var dte = "return a / b;";
-var pte = "return a * b;";
-var hte = "return (a - b) * (a - b);";
-var fte = "return a - b;";
-var mte = "return f32(a == b);";
-var gte = "return vec4<f32>(a == b);";
-var bte = "return f32(a > b);";
-var yte = "return vec4<f32>(a > b);";
-var vte = "return f32(a >= b);";
-var xte = "return vec4<f32>(a >= b);";
-var wte = "return f32(a < b);";
-var kte = "return vec4<f32>(a < b);";
-var Ite = "return f32(a <= b);";
-var Ste = "return vec4<f32>(a <= b);";
-var Cte = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
-var Nte = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
+var yte = "return a + b;";
+var vte = "return areal * breal - aimag * bimag;";
+var xte = "return areal * bimag + aimag * breal;";
+var wte = "return a / b;";
+var kte = "return a * b;";
+var Ite = "return (a - b) * (a - b);";
+var Ste = "return a - b;";
+var Cte = "return f32(a == b);";
+var Nte = "return vec4<f32>(a == b);";
+var Tte = "return f32(a > b);";
+var $te = "return vec4<f32>(a > b);";
+var _te = "return f32(a >= b);";
+var Ate = "return vec4<f32>(a >= b);";
+var Ete = "return f32(a < b);";
+var Rte = "return vec4<f32>(a < b);";
+var Dte = "return f32(a <= b);";
+var Fte = "return vec4<f32>(a <= b);";
+var Ote = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
+var Pte = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`;
-var Tte = `
+var zte = `
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
   `;
-var f2 = `
+var v2 = `
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -31921,13 +31979,13 @@ var f2 = `
     resultTemp.a = uniforms.NAN;
   }
   `;
-var $te = `
+var Mte = `
   let s = sign(a) * sign(b);
   let ia = i32(round(a));
   let ib = i32(round(b));
   return f32(idiv(ia, ib, s));
   `;
-var _te = `
+var Lte = `
   let ia = vec4<i32>(round(a));
   let ib = vec4<i32>(round(b));
   let cond = ib != vec4<i32>(0);
@@ -31949,9 +32007,9 @@ var _te = `
   }
   return vec4<f32>(resultTemp);
   `;
-var Ate = "return f32(a != b);";
-var Ete = "return vec4<f32>(a != b);";
-var Rte = `
+var Bte = "return f32(a != b);";
+var Vte = "return vec4<f32>(a != b);";
+var Wte = `
   if(a < 0.0 && floor(b) < b) {
     return uniforms.NAN;
   }
@@ -31963,7 +32021,7 @@ var Rte = `
   }
   return sign(a) * pow(abs(a), b);
   `;
-var Dte = `
+var Ute = `
   let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
   let isModRound1 = vec4<f32>(isModRound1Bool);
   let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
@@ -31984,16 +32042,16 @@ var Dte = `
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${f2}
+  ${v2}
   return resultTemp;
   `;
-var Fte = "if (a < 0.0) { return b * a; }  return a;";
-var Ote = `
+var Gte = "if (a < 0.0) { return b * a; }  return a;";
+var Hte = `
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
   `;
-function Aw(e, t) {
-  let n = t ? f2 : Tte;
+function Ew(e, t) {
+  let n = t ? v2 : zte;
   return t ? `
     var resultTemp = vec4<f32>(${e}(a, b));
     let isNaN = isnanVec4(a) | isnanVec4(b);
@@ -32006,57 +32064,57 @@ function Aw(e, t) {
 function tc(e, t) {
   switch (e) {
     case 0:
-      return pte;
+      return kte;
     case 1:
-      return ute;
+      return yte;
     case 2:
-      return fte;
+      return Ste;
     case 3:
-      return dte;
+      return wte;
     case 4:
-      return t ? gte : mte;
-    case 5:
-      return t ? yte : bte;
-    case 6:
-      return t ? xte : vte;
-    case 7:
-      return t ? kte : wte;
-    case 8:
-      return t ? Ste : Ite;
-    case 9:
       return t ? Nte : Cte;
+    case 5:
+      return t ? $te : Tte;
+    case 6:
+      return t ? Ate : _te;
+    case 7:
+      return t ? Rte : Ete;
+    case 8:
+      return t ? Fte : Dte;
+    case 9:
+      return t ? Pte : Ote;
     case 10:
-      return t ? Ete : Ate;
+      return t ? Vte : Bte;
     case 11:
-      return hte;
+      return Ite;
     case 12:
-      return t ? _te : $te;
+      return t ? Lte : Mte;
     case 14:
-      return t ? Ote : Fte;
+      return t ? Hte : Gte;
     case 15:
-      return Aw("max", t);
+      return Ew("max", t);
     case 16:
-      return Aw("min", t);
+      return Ew("min", t);
     case 13:
-      return t ? Dte : Rte;
+      return t ? Ute : Wte;
     case 17:
-      return lte;
+      return vte;
     case 18:
-      return cte;
+      return xte;
     default:
       throw new Error(`BinaryType ${e} is not implemented!`);
   }
 }
-var Pte = "return abs(a);";
-var zte = "return ceil(a);";
-var Mte = "return cos(a);";
-var Lte = `
+var qte = "return abs(a);";
+var jte = "return ceil(a);";
+var Kte = "return cos(a);";
+var Xte = `
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
 `;
-var Bte = "return exp(a) - 1.0;";
-var Vte = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
-var Wte = `
+var Yte = "return exp(a) - 1.0;";
+var Qte = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
+var Zte = `
   var resFloat = exp(a) - vec4<f32>(1.0);
   if (a.r >= 0.0) {
     resFloat.r = a.r;
@@ -32072,22 +32130,22 @@ var Wte = `
   }
   return resFloat;
 `;
-var Ute = "return exp(a);";
-var Gte = "return floor(a);";
-var Hte = "return a;";
-var qte = `if (a < 0.0) { return 1.0/0.0; }
+var Jte = "return exp(a);";
+var ene = "return floor(a);";
+var tne = "return a;";
+var nne = `if (a < 0.0) { return 1.0/0.0; }
   return log(a);`;
-var jte = "return f32(!(a >= 1.0));";
-var Kte = "return -a;";
-var Xte = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
-var Yte = `
+var sne = "return f32(!(a >= 1.0));";
+var rne = "return -a;";
+var ane = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
+var ine = `
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
 `;
-var Qte = "if(a < 0.0) { return 0.0; } return a;";
-var Zte = "return clamp(a, 0.0, 6.0);";
-var Jte = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
-var ene = `
+var one = "if(a < 0.0) { return 0.0; } return a;";
+var une = "return clamp(a, 0.0, 6.0);";
+var lne = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
+var cne = `
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
   let isNaN = isnanVec4(a);
 
@@ -32105,92 +32163,92 @@ var ene = `
   }
   return resFloat;
 `;
-var tne = "return 1.0/sqrt(a);";
-var nne = "return 1.0 / (1.0 + exp(-1.0 * a));";
-var sne = "return sin(a);";
-var rne = `
+var dne = "return 1.0/sqrt(a);";
+var pne = "return 1.0 / (1.0 + exp(-1.0 * a));";
+var hne = "return sin(a);";
+var fne = `
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
 `;
-var ane = "return sqrt(a);";
-var ine = "return a * a;";
-var one = `
+var mne = "return sqrt(a);";
+var gne = "return a * a;";
+var bne = `
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
 `;
-var une = "return f32(i32((a)));";
-function Hr(e, t) {
+var yne = "return f32(i32((a)));";
+function qr(e, t) {
   switch (e) {
     case 0:
-      return Pte;
-    case 2:
-      return Mte;
-    case 3:
-      return Lte;
-    case 1:
-      return zte;
-    case 4:
-      return t ? Wte : Vte;
-    case 5:
-      return Ute;
-    case 6:
-      return Bte;
-    case 7:
-      return Gte;
-    case 8:
-      return Hte;
-    case 9:
       return qte;
-    case 10:
-      return jte;
-    case 11:
+    case 2:
       return Kte;
-    case 14:
-      return t ? Yte : Xte;
-    case 12:
-      return t ? ene : Qte;
-    case 13:
-      return t ? Jte : Zte;
-    case 15:
+    case 3:
+      return Xte;
+    case 1:
+      return jte;
+    case 4:
+      return t ? Zte : Qte;
+    case 5:
+      return Jte;
+    case 6:
+      return Yte;
+    case 7:
+      return ene;
+    case 8:
       return tne;
-    case 18:
+    case 9:
       return nne;
-    case 16:
+    case 10:
       return sne;
-    case 17:
+    case 11:
       return rne;
+    case 14:
+      return t ? ine : ane;
+    case 12:
+      return t ? cne : one;
+    case 13:
+      return t ? lne : une;
+    case 15:
+      return dne;
+    case 18:
+      return pne;
+    case 16:
+      return hne;
+    case 17:
+      return fne;
     case 19:
-      return ane;
+      return mne;
     case 20:
-      return ine;
+      return gne;
     case 21:
-      return one;
+      return bne;
     case 22:
-      return une;
+      return yne;
     default:
       throw new Error(`BinaryType ${e} is not implemented!`);
   }
 }
-function Zs(e, t = false) {
+function Js(e, t = false) {
   if (e === null)
     return null;
   if (e === "linear")
-    return Hr(8);
+    return qr(8);
   if (e === "relu")
-    return Hr(12, t);
+    return qr(12, t);
   if (e === "elu")
-    return Hr(4, t);
+    return qr(4, t);
   if (e === "relu6")
-    return Hr(13, t);
+    return qr(13, t);
   if (e === "prelu")
     return tc(14, t);
   if (e === "sigmoid")
-    return Hr(18, t);
+    return qr(18, t);
   if (e === "leakyrelu")
-    return Hr(14, t);
+    return qr(14, t);
   throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);
 }
-function lne(e, t) {
+function vne(e, t) {
   if (Math.max(...e) > 3)
     throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
   let n = e.length, s = e.map((a) => `${t}[${a}]`), r = new Array(n - 1);
@@ -32213,14 +32271,14 @@ function Wt(e) {
 function cd(e, t) {
   return e === "float32" ? t ? "vec4<f32>" : "f32" : e === "int32" || e === "bool" ? t ? "vec4<i32>" : "i32" : e;
 }
-function Sv() {
+function Nv() {
   return `
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
 `;
 }
-function Dr() {
+function Fr() {
   return `
-  ${Sv()}
+  ${Nv()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
@@ -32231,11 +32289,11 @@ function Dr() {
 }
 function Ue() {
   return `
-    ${Dr()}
+    ${Fr()}
       let index = getGlobalIndex();
 `;
 }
-function cne(e, t, n, s = false) {
+function xne(e, t, n, s = false) {
   let r = [];
   if (r.push(`
     let workGroupSizeX = ${n.workGroupSize[0]}u;
@@ -32273,8 +32331,8 @@ function cne(e, t, n, s = false) {
 
       @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype, n.isVec4)}>;
       @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `), [Ew, r.join(`
-`), Rw(t.shape), n.getUserCode()].join(`
+    `), [Rw, r.join(`
+`), Dw(t.shape), n.getUserCode()].join(`
 `);
   let a = "struct Uniforms { NAN : f32, ";
   n.variableNames.forEach((p, d) => {
@@ -32293,17 +32351,17 @@ function cne(e, t, n, s = false) {
   }), a !== "" && r.push(`
     @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms: Uniforms;
     `);
-  let [o, u] = gne(t.shape, n.dispatchLayout), l = [Ew, r.join(`
-`), Rw(t.shape), o, dne(t.shape.length)];
-  if (n.atomic || l.push(pne(t.shape, t.dtype, n.isVec4)), u === t.shape.length) {
-    let p = e.map((d) => hne(d, t.shape, n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
+  let [o, u] = Nne(t.shape, n.dispatchLayout), l = [Rw, r.join(`
+`), Dw(t.shape), o, wne(t.shape.length)];
+  if (n.atomic || l.push(kne(t.shape, t.dtype, n.isVec4)), u === t.shape.length) {
+    let p = e.map((d) => Ine(d, t.shape, n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
 `);
     l.push(p);
   }
   return l.push(n.getUserCode()), l.join(`
 `);
 }
-var Ew = `
+var Rw = `
   // Checks whether coordinates lie within the bounds of the shape.
   fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
     return all(coord >= vec2<i32>(0)) && all(coord < shape);
@@ -32351,7 +32409,7 @@ var Ew = `
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
 `;
-function dne(e) {
+function wne(e) {
   let t = "";
   switch (e) {
     case 0:
@@ -32390,7 +32448,7 @@ function dne(e) {
   }
   return t;
 }
-function pne(e, t, n) {
+function kne(e, t, n) {
   let s = e.length, r = cd(t, n), a;
   if (n ? a = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
@@ -32426,11 +32484,11 @@ function pne(e, t, n) {
   }
   return a;
 }
-function hne(e, t, n, s) {
-  let r = fne(e, n);
-  return e.shape.length <= t.length && (r += mne(e, t, n, s)), r;
+function Ine(e, t, n, s) {
+  let r = Sne(e, n);
+  return e.shape.length <= t.length && (r += Cne(e, t, n, s)), r;
 }
-function fne(e, t) {
+function Sne(e, t) {
   let n = e.name, s = e.shape.length, r = Wt(s), a = "get" + n.charAt(0).toUpperCase() + n.slice(1), i = ["d0", "d1", "d2", "d3"].slice(0, s), o = i.map((c) => `${c} : i32`).join(", ");
   if (s < 1)
     return t ? `
@@ -32455,7 +32513,7 @@ function fne(e, t) {
     }
    `;
 }
-function mne(e, t, n, s) {
+function Cne(e, t, n, s) {
   let r = e.name, a = r.charAt(0).toUpperCase() + r.slice(1), i = "get" + a + "ByOutput", o = e.shape.length, u = t.length, l = Wt(u);
   if (w.arraysEqual(e.shape, t) && s)
     return n ? `
@@ -32531,7 +32589,7 @@ function mne(e, t, n, s) {
     }
   `;
 }
-function gne(e, t) {
+function Nne(e, t) {
   let { x: n, y: s = [], z: r = [] } = t, a = e.length;
   if (n.length === a)
     return [`fn getOutputCoords() -> ${Wt(a)}{
@@ -32546,7 +32604,7 @@ function gne(e, t) {
       if (u += h.length, h.length === 1)
         i += `let d${h[0]} = i32(globalId[${d}]);`;
       else {
-        let f = lne(h, "uniforms.outShape");
+        let f = vne(h, "uniforms.outShape");
         i += `var index${d} = i32(globalId[${d}]);`;
         for (let m = 0; m < f.length; m++)
           i += `let d${h[m]} = index${d} / ${f[m]};`, m === f.length - 1 ? i += `let d${h[m + 1]} = index${d} - d${h[m]} * ${f[m]};` : i += `index${d} = index${d} - d${h[m]} * ${f[m]};`;
@@ -32560,7 +32618,7 @@ function gne(e, t) {
   `;
   return l.length === 0 ? p += `return ${c}(0); }` : p += `return ${c}(${l.join(",")}); }`, [p, u];
 }
-function Rw(e) {
+function Dw(e) {
   let t = e.length;
   if (t <= 1)
     return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
@@ -32583,9 +32641,9 @@ function Rw(e) {
     }
   `;
 }
-var m2 = {};
-Ae(m2, { ArrayBufferToTypedArray: () => b2, GPUBytesPerElement: () => jm, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => Cv, computeWorkGroupSizeForMatMul: () => g2, computeWorkPerThreadForConv2d: () => Nv, flatDispatchLayout: () => Be, isWebGPUSupported: () => Tv, tilesFitEvenlyIntoShape: () => Ks });
-var na = (e) => {
+var x2 = {};
+Ae(x2, { ArrayBufferToTypedArray: () => k2, GPUBytesPerElement: () => Xm, computeDispatch: () => $e, computeWorkGroupSizeForConv2d: () => Tv, computeWorkGroupSizeForMatMul: () => w2, computeWorkPerThreadForConv2d: () => $v, flatDispatchLayout: () => Le, isWebGPUSupported: () => _v, tilesFitEvenlyIntoShape: () => Ks });
+var sa = (e) => {
   let t = 1;
   for (let n = 0; n < e.length; n++)
     t *= e[n];
@@ -32596,32 +32654,32 @@ function Ks(e, t) {
     throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);
   return t.every((n, s) => n % e[s] === 0);
 }
-function _e(e, t, n = [1, 1, 1], s = [1, 1, 1]) {
-  let [r, a, i] = [Math.ceil(na(e.x.map((o) => t[o])) / (n[0] * s[0])), e.y ? Math.ceil(na(e.y.map((o) => t[o])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(na(e.z.map((o) => t[o])) / (n[2] * s[2])) : 1];
+function $e(e, t, n = [1, 1, 1], s = [1, 1, 1]) {
+  let [r, a, i] = [Math.ceil(sa(e.x.map((o) => t[o])) / (n[0] * s[0])), e.y ? Math.ceil(sa(e.y.map((o) => t[o])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(sa(e.z.map((o) => t[o])) / (n[2] * s[2])) : 1];
   return [r, a, i];
 }
-function Cv(e, t) {
-  let n = na(e.x.map((r) => t[r])), s = na(e.y.map((r) => t[r]));
+function Tv(e, t) {
+  let n = sa(e.x.map((r) => t[r])), s = sa(e.y.map((r) => t[r]));
   return n <= 4 ? [4, 16, 1] : s <= 4 ? [16, 4, 1] : [16, 16, 1];
 }
-function g2(e, t, n) {
+function w2(e, t, n) {
   return e === 1 ? [32, 1, 1] : n === 1 ? [1, 32, 1] : [8, 8, 1];
 }
-function Nv(e, t) {
-  let n = na(e.x.map((r) => t[r])), s = na(e.y.map((r) => t[r]));
+function $v(e, t) {
+  let n = sa(e.x.map((r) => t[r])), s = sa(e.y.map((r) => t[r]));
   return n <= 4 ? [1, 2, 1] : s <= 4 ? [2, 1, 1] : [2, 2, 1];
 }
-function Be(e) {
+function Le(e) {
   return { x: e.map((t, n) => n) };
 }
-function jm(e) {
+function Xm(e) {
   if (e === "float32" || e === "int32" || e === "bool" || e === "string")
     return 4;
   if (e === "complex64")
     return 8;
   throw new Error(`Unknown dtype ${e}`);
 }
-function b2(e, t) {
+function k2(e, t) {
   if (t === "float32")
     return new Float32Array(e);
   if (t === "int32")
@@ -32630,10 +32688,10 @@ function b2(e, t) {
     return Uint8Array.from(new Int32Array(e));
   throw new Error(`Unknown dtype ${t}`);
 }
-function Tv() {
+function _v() {
   return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
 }
-function y2(e, t, n, s) {
+function I2(e, t, n, s) {
   return w.assert(s % 4 === 0 && e[0] === 4, () => "tileInner must be divisible by 4. And ColPerThread must be 4"), `
   var<workgroup> mm_Asub : array<array<vec4<f32>, ${s / e[0]}>, ${t}>;
   var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n / e[0]}>, ${s}>;
@@ -32642,7 +32700,7 @@ function y2(e, t, n, s) {
   let ColPerThread = ${e[0]};
   let TileInner = ${s};
 
-  ${Dr()}
+  ${Fr()}
 
     let tileRow = ${t === 1 ? "0" : "i32(localId.y) * RowPerThread"};
     let tileCol = i32(localId.x);
@@ -32703,9 +32761,9 @@ function y2(e, t, n, s) {
     }
   }`;
 }
-var bne = class {
+var Tne = class {
   constructor(e, t, n, s = null, r = null, a = null) {
-    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] }, t[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] }, t[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
     let i = s != null, o = a != null;
     i && this.variableNames.push("bias"), o && this.variableNames.push("preluActivationWeights"), this.tileAOuter = t[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, this.aShape = e, this.addBias = i, this.activation = r, this.hasPreluActivationWeights = o, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`;
   }
@@ -32722,7 +32780,7 @@ var bne = class {
         }
         return vec4<f32>(0.0)`, n = "", s = "";
     if (this.activation) {
-      let i = Zs(this.activation, this.isVec4);
+      let i = Js(this.activation, this.isVec4);
       this.hasPreluActivationWeights ? n = `fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
                   let b = getPreluActivationWeightsByOutputCoords(outCoord);
                   ${i}
@@ -32757,16 +32815,16 @@ var bne = class {
           setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
         }
       }
-      ${y2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
+      ${I2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
     `;
   }
 };
-function $v(e, t) {
+function Av(e, t) {
   let n = t[1] * e[1], s = t[0] * e[0], r = n > s ? n : s;
   return `
     var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
     var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
-    ${Dr()}
+    ${Fr()}
       let tileRow = i32(localId.y) * ${e[1]};
       let tileCol = i32(localId.x) * ${e[0]};
 
@@ -32849,12 +32907,12 @@ function $v(e, t) {
     }
   `;
 }
-function yne(e) {
+function $ne(e) {
   return `
     let TileSize = ${e[0] * 4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
-    ${Dr()}
+    ${Fr()}
       let tileCol = i32(localId.x);
       let globalCol = i32(globalId.x);
       let globalRow = i32(globalId.y);
@@ -32895,11 +32953,11 @@ function yne(e) {
     }
   `;
 }
-var v2 = class {
+var S2 = class {
   constructor(e, t, n, s = false, r = false, a = null, i = null, o = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [16, 16, 1], this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] };
     let u = s ? e[1] : e[2];
-    this.workGroupSize = g2(t[1], u, t[2]), (t[1] === 1 || t[2] === 1) && (n = 1), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]), w.arraysEqual(this.dispatch, [1, 1, 1]) && (n = 1, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]));
+    this.workGroupSize = w2(t[1], u, t[2]), (t[1] === 1 || t[2] === 1) && (n = 1), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]), w.arraysEqual(this.dispatch, [1, 1, 1]) && (n = 1, this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [n, n, 1]));
     let l = a != null, c = o != null;
     l && this.variableNames.push("bias"), c && this.variableNames.push("preluActivationWeights"), this.workPerThread = n, this.aShape = e, this.transposeA = s, this.transposeB = r, this.addBias = l, this.activation = i, this.hasPreluActivationWeights = c;
     let p = this.outputShape[2], d = this.transposeB ? [this.outputShape[0], p, u] : [this.outputShape[0], u, p];
@@ -32930,7 +32988,7 @@ var v2 = class {
            return 0.0;`;
     let n = "", s = "";
     if (this.activation) {
-      let i = Zs(this.activation, false);
+      let i = Js(this.activation, false);
       this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${i}
@@ -32964,14 +33022,14 @@ var v2 = class {
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1] > 1 ? $v([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : yne(this.workGroupSize)}
+      ${this.outputShape[1] > 1 ? Av([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : $ne(this.workGroupSize)}
     `;
   }
 };
-function vne() {
+function _ne() {
   return `
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
-    ${Dr()}
+    ${Fr()}
       let coords = getOutputCoords();
       let batch = coords[0];
       let row = coords[1];
@@ -33002,9 +33060,9 @@ function vne() {
     }
   `;
 }
-var xne = class {
+var Ane = class {
   constructor(e, t = false, n = false, s = null, r = null, a = null) {
-    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize);
     let i = s != null, o = a != null;
     i && this.variableNames.push("bias"), o && this.variableNames.push("preluActivationWeights"), this.transposeA = t, this.transposeB = n, this.addBias = i, this.activation = r, this.hasPreluActivationWeights = o, this.shaderKey = `matMulReduce_${this.activation}_${t}_${n}`;
   }
@@ -33015,7 +33073,7 @@ var xne = class {
     this.transposeB === false ? t = "return B[batch * batchBSize + row * uniforms.dimBOuter + col];" : t = "return B[batch * batchBSize + col * uniforms.dimInner + row];";
     let n = "", s = "";
     if (this.activation) {
-      let i = Zs(this.activation, false);
+      let i = Js(this.activation, false);
       this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${i}
@@ -33046,11 +33104,11 @@ var xne = class {
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${vne()}
+      ${_ne()}
     `;
   }
 };
-function wne(e) {
+function Ene(e) {
   let t = e[1] / 2, n = e[0], s = t > n ? t : n;
   return `
   var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
@@ -33064,7 +33122,7 @@ function wne(e) {
   // arithmetic operations and others handle IO operations between barrier api,
   // makes ALUs and load/store units work simultaneously, could improves
   // the performance.
-  ${Dr()}
+  ${Fr()}
     let tileRow = i32(localId.y);
     let tileCol = i32(localId.x);
     let globalRow = i32(globalId.y);
@@ -33141,7 +33199,7 @@ function wne(e) {
   }
   `;
 }
-var kne = class {
+var Rne = class {
   constructor(e, t, n, s = null, r = null, a = null) {
     this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.workGroupSize = [8, 16, 1], w.assert(e[1] <= 16 || t[2] <= 16, () => "This program can be only used when A width or B Height are small"), this.outputShape = n, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(n[2] / this.workGroupSize[0]), Math.ceil(n[1] * 2 / this.workGroupSize[1]), n[0]];
     let i = s != null;
@@ -33158,7 +33216,7 @@ var kne = class {
          }
          return 0.0;`, n = "", s = "";
     if (this.activation) {
-      let i = Zs(this.activation, false);
+      let i = Js(this.activation, false);
       this.hasPreluActivationWeights ? n = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
             let b = getPreluActivationWeightsByOutputCoords(outCoord);
             ${i}
@@ -33190,7 +33248,7 @@ var kne = class {
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${wne(this.workGroupSize)}
+      ${Ene(this.workGroupSize)}
     `;
   }
 };
@@ -33198,12 +33256,12 @@ function Me(e) {
   let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a), o = w.sizeFromShape(i);
   return w.assert(a === o, () => `The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
 }
-var Ine = { kernelName: Eo, backendName: "webgpu", kernelFunc: Me };
-function _v({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = jo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+var Dne = { kernelName: Ro, backendName: "webgpu", kernelFunc: Me };
+function Ev({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = Ko.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
   let k = n ? [b, p, h] : [b, h, p], T = s ? [y, f, d] : [y, d, f], N = Me({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = Me({ inputs: { x: t }, backend: r, attrs: { shape: T } }), A = [N, E], P = Math.max(b, y), R = p % 4 === 0 && f % 4 === 0 && !n && !s && f >= 32, F;
-  h * f <= 32 ? F = new xne([P, h, f], n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? F = new kne(k, T, [P, h, f], a, u, i) : R ? F = new bne(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), a, u, i) : F = new v2(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), n, s, a, u, i);
+  h * f <= 32 ? F = new Ane([P, h, f], n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? F = new Rne(k, T, [P, h, f], a, u, i) : R ? F = new Tne(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), a, u, i) : F = new S2(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), n, s, a, u, i);
   let $ = [N, E];
   a && $.push(a), i && $.push(i);
   let z = [{ type: "int32", data: [h] }, { type: "int32", data: [f] }, { type: "int32", data: [p] }];
@@ -33214,14 +33272,14 @@ function _v({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = nu
     r.disposeData(K.dataId);
   return q;
 }
-function Sne(e) {
+function Fne(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
-  return _v({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
+  return Ev({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
 }
-var Cne = { kernelName: ra, backendName: "webgpu", kernelFunc: Sne };
-var Dw = class {
+var One = { kernelName: aa, backendName: "webgpu", kernelFunc: Fne };
+var Fw = class {
   constructor(e, t, n) {
-    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
+    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
   }
   getUserCode() {
     return `
@@ -33242,11 +33300,11 @@ var Dw = class {
     `;
   }
 };
-var Nne = class {
+var Pne = class {
   constructor(e, t, n, s) {
     this.variableNames = ["A", "B"], this.size = true;
     let r = 256;
-    this.workGroupSize = [r, 1, 1], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.lastDimensionSize = s ? n[0] : t[0], this.lastDimensionSize < 256 ? this.workPerThread = 1 : this.lastDimensionSize < 512 ? this.workPerThread = 2 : this.workPerThread = 4, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.useSharedMemoryWithB = s, this.op = e, this.shaderKey = `binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;
+    this.workGroupSize = [r, 1, 1], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Le(this.outputShape), this.lastDimensionSize = s ? n[0] : t[0], this.lastDimensionSize < 256 ? this.workPerThread = 1 : this.lastDimensionSize < 512 ? this.workPerThread = 2 : this.workPerThread = 4, this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.useSharedMemoryWithB = s, this.op = e, this.shaderKey = `binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;
   }
   getUserCode() {
     let e = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : "0", t = this.useSharedMemoryWithB ? `let a = getAByOutputCoords(coords);
@@ -33280,11 +33338,11 @@ var Nne = class {
         `;
   }
 };
-var Tne = class {
+var zne = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.workPerThread = 4, this.isVec4 = true, this.size = true;
     let s = 128;
-    this.workGroupSize = [s, 1, 1], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.op = e, this.shaderKey = `binaryVec4_${e}`;
+    this.workGroupSize = [s, 1, 1], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.op = e, this.shaderKey = `binaryVec4_${e}`;
   }
   getUserCode() {
     return `
@@ -33301,11 +33359,11 @@ var Tne = class {
     `;
   }
 };
-var x2 = class {
+var C2 = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.size = true;
     let s = 128;
-    this.workGroupSize = [s, 1, 1], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binary_${e}`, this.op = e;
+    this.workGroupSize = [s, 1, 1], this.outputShape = S.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binary_${e}`, this.op = e;
   }
   getUserCode() {
     return `
@@ -33322,32 +33380,32 @@ var x2 = class {
       `;
   }
 };
-function Fw(e, t, n) {
+function Ow(e, t, n) {
   if (w.arraysEqual(t, n) && w.sizeFromShape(t) % 4 === 0)
-    return new Tne(e, t, n);
+    return new zne(e, t, n);
   let r = t.length === 1 && n.length > 1 && t[0] < 1024, a = n.length === 1 && t.length > 1 && n[0] < 1024;
-  return r || a ? new Nne(e, t, n, a) : new x2(e, t, n);
+  return r || a ? new Pne(e, t, n, a) : new C2(e, t, n);
 }
-function es(e) {
+function Wn(e) {
   let { inputs: t } = e, { x: n } = t;
   return e.backend.incRef(n.dataId), { dataId: n.dataId, shape: n.shape, dtype: n.dtype };
 }
-var $ne = { kernelName: Ba, backendName: "webgpu", kernelFunc: es };
-function lu(e) {
-  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = es({ inputs: { x: s }, backend: n }), u = es({ inputs: { x: r }, backend: n });
+var Mne = { kernelName: Wa, backendName: "webgpu", kernelFunc: Wn };
+function cu(e) {
+  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = Wn({ inputs: { x: s }, backend: n }), u = Wn({ inputs: { x: r }, backend: n });
   return i.complexTensorInfos = { real: o, imag: u }, a;
 }
-var _ne = { kernelName: jd, backendName: "webgpu", kernelFunc: lu };
+var Lne = { kernelName: Xd, backendName: "webgpu", kernelFunc: cu };
 var nc = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.size = true;
     let n = 128;
-    this.workGroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.op = t, this.shaderKey = `unary_${t}`;
+    this.workGroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.op = t, this.shaderKey = `unary_${t}`;
   }
   getUserCode() {
     return `
       fn unaryOperation(a : f32) -> f32 {
-        ${Hr(this.op, false)}
+        ${qr(this.op, false)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -33376,14 +33434,14 @@ function mn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype:
       let p = u.tensorMap.get(i.dataId), d = u.tensorMap.get(o.dataId), h, f;
       if (e !== 0)
         [h, f] = [[p.complexTensorInfos.real, d.complexTensorInfos.real], [p.complexTensorInfos.imag, d.complexTensorInfos.imag]].map((g) => {
-          let [b, y] = g, v = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, x = { dataId: y.dataId, dtype: y.dtype, shape: o.shape }, k = Fw(e, i.shape, o.shape);
+          let [b, y] = g, v = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, x = { dataId: y.dataId, dtype: y.dtype, shape: o.shape }, k = Ow(e, i.shape, o.shape);
           return u.runWebGPUProgram(k, [v, x], cn(b.dtype, y.dtype));
         });
       else {
-        let g = new Dw(17, i.shape, o.shape), b = new Dw(18, i.shape, o.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: o.shape }];
+        let g = new Fw(17, i.shape, o.shape), b = new Fw(18, i.shape, o.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: o.shape }];
         h = u.runWebGPUProgram(g, y, "float32"), f = u.runWebGPUProgram(b, y, "float32");
       }
-      let m = lu({ inputs: { real: h, imag: f }, backend: u });
+      let m = cu({ inputs: { real: h, imag: f }, backend: u });
       return u.disposeData(h.dataId), u.disposeData(f.dataId), m;
     }
     let l = s || cn(i.dtype, o.dtype);
@@ -33391,18 +33449,18 @@ function mn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype:
       let p = u.tensorMap.get(i.dataId).values, d = u.tensorMap.get(o.dataId).values, h = i.dtype === "string" ? S.fromUint8ToStringArray(p) : p, f = i.dtype === "string" ? S.fromUint8ToStringArray(d) : d, [m, g] = t(i.shape, o.shape, h, f, l);
       return u.makeTensorInfo(g, l, m);
     }
-    let c = Fw(e, i.shape, o.shape);
+    let c = Ow(e, i.shape, o.shape);
     return u.runWebGPUProgram(c, [i, o], l);
   };
 }
-var { addImpl: Ane, ceilImpl: Ene, concatImpl: Rne, equalImpl: Dne, expImpl: Fne, expm1Impl: One, floorImpl: Pne, gatherNdImpl: zne, gatherV2Impl: Mne, greaterEqualImpl: Lne, greaterImpl: Bne, lessEqualImpl: Vne, lessImpl: Wne, logImpl: Une, maxImpl: Gne, maximumImpl: Hne, minimumImpl: qne, multiplyImpl: jne, negImpl: Kne, notEqualImpl: Xne, prodImpl: Yne, rangeImpl: Qne, rsqrtImpl: Zne, simpleAbsImpl: Jne, sliceImpl: ese, stridedSliceImpl: tse, stringNGramsImpl: nse, subImpl: sse, tileImpl: rse, topKImpl: ase, transposeImpl: ise, uniqueImpl: Mpe } = Zy;
-var ose = Kt({ opType: 0, cpuKernelImpl: Jne });
-var use = { kernelName: io, backendName: "webgpu", kernelFunc: ose };
-var lse = mn({ opSnippet: 1, cpuKernelImpl: Ane, supportsComplex: true });
-var cse = { kernelName: Ir, backendName: "webgpu", kernelFunc: lse };
-var dse = class {
+var { addImpl: Bne, ceilImpl: Vne, concatImpl: Wne, equalImpl: Une, expImpl: Gne, expm1Impl: Hne, floorImpl: qne, gatherNdImpl: jne, gatherV2Impl: Kne, greaterEqualImpl: Xne, greaterImpl: Yne, lessEqualImpl: Qne, lessImpl: Zne, logImpl: Jne, maxImpl: ese, maximumImpl: tse, minimumImpl: nse, multiplyImpl: sse, negImpl: rse, notEqualImpl: ase, prodImpl: ise, rangeImpl: ose, rsqrtImpl: use, simpleAbsImpl: lse, sliceImpl: cse, stridedSliceImpl: dse, stringNGramsImpl: pse, subImpl: hse, tileImpl: fse, topKImpl: mse, transposeImpl: gse, uniqueImpl: Zpe } = ev;
+var bse = Kt({ opType: 0, cpuKernelImpl: lse });
+var yse = { kernelName: uo, backendName: "webgpu", kernelFunc: bse };
+var vse = mn({ opSnippet: 1, cpuKernelImpl: Bne, supportsComplex: true });
+var xse = { kernelName: Sr, backendName: "webgpu", kernelFunc: vse };
+var wse = class {
   constructor(e) {
-    this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN";
+    this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "addN";
   }
   getUserCode() {
     let e = [];
@@ -33425,21 +33483,21 @@ var dse = class {
     `;
   }
 };
-function pse(e) {
+function kse(e) {
   let { inputs: t, backend: n } = e, s = t;
   if (s.length === 1)
-    return es({ inputs: { x: s[0] }, backend: n });
-  let r = s.map((o) => o.dtype).reduce((o, u) => cn(o, u)), a = s.map((o) => o.shape), i = new dse(a);
+    return Wn({ inputs: { x: s[0] }, backend: n });
+  let r = s.map((o) => o.dtype).reduce((o, u) => cn(o, u)), a = s.map((o) => o.shape), i = new wse(a);
   return n.runWebGPUProgram(i, s, r);
 }
-var hse = { kernelName: ka, backendName: "webgpu", kernelFunc: pse };
-var w2 = class {
+var Ise = { kernelName: Ia, backendName: "webgpu", kernelFunc: kse };
+var N2 = class {
   constructor(e, t, n) {
     this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "axis : i32, infinityValue : f32,", this.size = true;
     let s = [t];
     S.assertAxesAreInnerMostDims("arg" + n.charAt(0).toUpperCase() + n.slice(1), s, e.length), this.op = n === "min" ? "<" : ">";
     let [r] = S.computeOutAndReduceShapes(e, s);
-    this.outputShape = r.length === 0 ? [1] : r, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.inputShape = e, this.shaderKey = `argMinMax${this.op}`;
+    this.outputShape = r.length === 0 ? [1] : r, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.inputShape = e, this.shaderKey = `argMinMax${this.op}`;
   }
   getUserCode() {
     let e = `
@@ -33526,19 +33584,19 @@ var w2 = class {
     `;
   }
 };
-var fse = class {
+var Sse = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workGroupSize = [16, 16, 1];
     let n = new Array(e.length);
     for (let s = 0; s < n.length; s++)
       n[s] = e[t[s]];
-    this.outputShape = n, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]), this.shaderKey = "transposeShared";
+    this.outputShape = n, this.dispatchLayout = { x: [0], y: [1] }, this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]), this.shaderKey = "transposeShared";
   }
   getUserCode() {
     return `
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;
-      ${Sv()}
+      ${Nv()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -33560,16 +33618,16 @@ var fse = class {
     `;
   }
 };
-var mse = class {
+var Cse = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true;
     let n = new Array(e.length);
     for (let s = 0; s < n.length; s++)
       n[s] = e[t[s]];
-    this.outputShape = n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.newDim = t, this.shaderKey = `transpose_${t}`;
+    this.outputShape = n, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.newDim = t, this.shaderKey = `transpose_${t}`;
   }
   getUserCode() {
-    let e = Wt(this.outputShape.length), t = gse(this.newDim);
+    let e = Wt(this.outputShape.length), t = Nse(this.newDim);
     return `
       ${Ue()}
 
@@ -33585,7 +33643,7 @@ var mse = class {
     `;
   }
 };
-function gse(e) {
+function Nse(e) {
   let t = e.length;
   if (t > 4)
     throw Error(`Transpose for rank ${t} is not yet supported`);
@@ -33594,39 +33652,39 @@ function gse(e) {
     n[e[s]] = `resRC[${s}]`;
   return n.join();
 }
-function wi(e) {
+function Xs(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, i = n, o = r.shape.length, u = new Array(o);
   for (let c = 0; c < u.length; c++)
     u[c] = r.shape[a[c]];
   if (n.shouldExecuteOnCPU([r])) {
-    let p = i.tensorMap.get(r.dataId).values, d = ise(p, r.shape, r.dtype, a, u);
+    let p = i.tensorMap.get(r.dataId).values, d = gse(p, r.shape, r.dtype, a, u);
     return n.makeTensorInfo(u, r.dtype, d);
   }
   if (r.shape.length === 2 && w.arraysEqual(a, [1, 0])) {
-    let c = new fse(r.shape, a);
+    let c = new Sse(r.shape, a);
     return i.runWebGPUProgram(c, [r], r.dtype);
   }
-  let l = new mse(r.shape, a);
+  let l = new Cse(r.shape, a);
   return i.runWebGPUProgram(l, [r], r.dtype);
 }
-var bse = { kernelName: pi, backendName: "webgpu", kernelFunc: wi };
-function yse(e) {
+var Tse = { kernelName: mi, backendName: "webgpu", kernelFunc: Xs };
+function $se(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = wi({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
-  let c = new w2(u.shape, i[0], "max"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
+  o != null && (u = Xs({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
+  let c = new N2(u.shape, i[0], "max"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
   return l.forEach((h) => n.disposeData(h.dataId)), d;
 }
-var vse = { kernelName: Ia, backendName: "webgpu", kernelFunc: yse };
-function xse(e) {
+var _se = { kernelName: Sa, backendName: "webgpu", kernelFunc: $se };
+function Ase(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = S.getAxesPermutation(i, r.shape.length), u = r, l = [];
-  o != null && (u = wi({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
-  let c = new w2(u.shape, i[0], "min"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
+  o != null && (u = Xs({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = S.getInnerMostAxes(i.length, u.shape.length)), S.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
+  let c = new N2(u.shape, i[0], "min"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
   return l.forEach((h) => n.disposeData(h.dataId)), d;
 }
-var wse = { kernelName: ol, backendName: "webgpu", kernelFunc: xse };
-var k2 = class {
+var Ese = { kernelName: ul, backendName: "webgpu", kernelFunc: Ase };
+var T2 = class {
   constructor(e, t) {
-    this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `pool2D_${t}`, this.poolType = t;
+    this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `pool2D_${t}`, this.poolType = t;
   }
   getUserCode() {
     let e = "resultValue = max(value, resultValue);";
@@ -33668,9 +33726,9 @@ var k2 = class {
     `;
   }
 };
-var I2 = class {
+var $2 = class {
   constructor(e) {
-    this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>,", this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
+    this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>,", this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "poolWithFilterSizeEqualsOne";
   }
   getUserCode() {
     return `
@@ -33691,26 +33749,26 @@ var I2 = class {
     `;
   }
 };
-function kse(e) {
+function Rse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = S.computePool2DInfo(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
-    return es({ inputs: { x: r }, backend: n });
+    return Wn({ inputs: { x: r }, backend: n });
   let p, d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }];
-  return c.filterHeight === 1 && c.filterWidth === 1 ? p = new I2(c) : (p = new k2(c, "avg"), d.push({ type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] })), n.runWebGPUProgram(p, [r], r.dtype, d);
+  return c.filterHeight === 1 && c.filterWidth === 1 ? p = new $2(c) : (p = new T2(c, "avg"), d.push({ type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] })), n.runWebGPUProgram(p, [r], r.dtype, d);
 }
-var Ise = { kernelName: Sa, backendName: "webgpu", kernelFunc: kse };
-function Sse(e) {
+var Dse = { kernelName: Ca, backendName: "webgpu", kernelFunc: Rse };
+function Fse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
-  return _v({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
+  return Ev({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
 }
-var Cse = { kernelName: Ca, backendName: "webgpu", kernelFunc: Sse };
-var Nse = class {
+var Ose = { kernelName: Na, backendName: "webgpu", kernelFunc: Fse };
+var Pse = class {
   constructor(e, t) {
-    this.variableNames = ["source"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.rank = t.length, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Wt(e.length)}, `, this.shaderKey = "slice";
+    this.variableNames = ["source"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.rank = t.length, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.start = e, this.uniforms = `start : ${Wt(e.length)}, `, this.shaderKey = "slice";
   }
   getUserCode() {
-    let e = Wt(this.rank), t = Tse(this.rank), n;
-    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`), `
+    let e = Wt(this.rank), t = zse(this.rank), n;
+    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${Ym[a]} = uniforms.start[${a}] + coords.${Ym[a]};`), `
       ${Ue()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -33723,74 +33781,74 @@ var Nse = class {
     `;
   }
 };
-var Km = ["x", "y", "z", "w", "u", "v"];
-function Tse(e) {
+var Ym = ["x", "y", "z", "w", "u", "v"];
+function zse(e) {
   if (e === 1)
     return "sourceLoc";
   if (e <= 6)
-    return Km.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
+    return Ym.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
   throw Error(`Slicing for rank ${e} is not yet supported`);
 }
-function cu(e) {
+function du(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
   if (wt.assertParamsValid(r, o, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
-    let p = n.tensorMap.get(r.dataId), d = ese(p.values, o, u, r.shape, r.dtype);
+    let p = n.tensorMap.get(r.dataId), d = cse(p.values, o, u, r.shape, r.dtype);
     return n.makeTensorInfo(u, r.dtype, d);
   }
   if (w.sizeFromShape(u) === 0)
     return n.makeTensorInfo(u, r.dtype, []);
-  let l = new Nse(o, u), c = [{ type: "int32", data: o }];
+  let l = new Pse(o, u), c = [{ type: "int32", data: o }];
   return n.runWebGPUProgram(l, [r], r.dtype, c);
 }
-var $se = { kernelName: Po, backendName: "webgpu", kernelFunc: cu };
-var _se = (e) => {
+var Mse = { kernelName: zo, backendName: "webgpu", kernelFunc: du };
+var Lse = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = wi({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = cu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let o = a.reduce((y, v) => y * v), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = Xs({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = du({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeData(y.dataId)), b;
 };
-var Ase = { kernelName: oo, backendName: "webgpu", kernelFunc: _se };
-var S2 = mn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Xne });
-var Ese = { kernelName: So, backendName: "webgpu", kernelFunc: S2 };
+var Bse = { kernelName: lo, backendName: "webgpu", kernelFunc: Lse };
+var _2 = mn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: ase });
+var Vse = { kernelName: No, backendName: "webgpu", kernelFunc: _2 };
 function sc(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
-  return es({ inputs: { x: r.complexTensorInfos.real }, backend: n });
+  return Wn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
 }
-var Rse = { kernelName: np, backendName: "webgpu", kernelFunc: sc };
-function Dse(e, t) {
+var Wse = { kernelName: rp, backendName: "webgpu", kernelFunc: sc };
+function Use(e, t) {
   let n = new nc(e.shape, 22), s = t.runWebGPUProgram(n, [e], "int32");
   return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-function Xm(e) {
+function Qm(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
-      return es({ inputs: { x: r }, backend: n });
-    let i = $t(r.shape), o = Xm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = lu({ inputs: { real: o, imag: i }, backend: n });
+      return Wn({ inputs: { x: r }, backend: n });
+    let i = $t(r.shape), o = Qm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = cu({ inputs: { real: o, imag: i }, backend: n });
     return i.dispose(), n.disposeData(o.dataId), u;
   }
   if (r.dtype === "complex64") {
-    let i = sc({ inputs: { input: r }, backend: n }), o = Xm({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
+    let i = sc({ inputs: { input: r }, backend: n }), o = Qm({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
     return n.disposeData(i.dataId), o;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
-    let i = es({ inputs: { x: r }, backend: n });
+    let i = Wn({ inputs: { x: r }, backend: n });
     return { dataId: i.dataId, shape: i.shape, dtype: a };
   }
   if (a === "int32")
-    return Dse(r, n);
+    return Use(r, n);
   if (a === "bool") {
-    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = S2({ inputs: { a: r, b: i }, backend: n });
+    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = _2({ inputs: { a: r, b: i }, backend: n });
     return n.disposeData(i.dataId), u;
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var Fse = { kernelName: Na, backendName: "webgpu", kernelFunc: Xm };
-var Ose = Kt({ opType: 1, cpuKernelImpl: Ene });
-var Pse = { kernelName: Ta, backendName: "webgpu", kernelFunc: Ose };
-var zse = class {
+var Gse = { kernelName: Ta, backendName: "webgpu", kernelFunc: Qm };
+var Hse = Kt({ opType: 1, cpuKernelImpl: Vne });
+var qse = { kernelName: $a, backendName: "webgpu", kernelFunc: Hse };
+var jse = class {
   constructor(e) {
-    this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.isVec4 = true, this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
+    this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.isVec4 = true, this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.shaderKey = "clipVec4";
   }
   getUserCode() {
     return `
@@ -33812,9 +33870,9 @@ var zse = class {
     `;
   }
 };
-var Mse = class {
+var Kse = class {
   constructor(e) {
-    this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "clip";
+    this.variableNames = ["A"], this.uniforms = "minVal : f32, maxVal : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "clip";
   }
   getUserCode() {
     return `
@@ -33831,14 +33889,14 @@ var Mse = class {
     `;
   }
 };
-function Lse(e) {
+function Xse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o, u = [{ type: "float32", data: [a] }, { type: "float32", data: [i] }];
-  return w.sizeFromShape(r.shape) % 4 === 0 ? o = new zse(r.shape) : o = new Mse(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
+  return w.sizeFromShape(r.shape) % 4 === 0 ? o = new jse(r.shape) : o = new Kse(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
 }
-var Bse = { kernelName: Sr, backendName: "webgpu", kernelFunc: Lse };
-var Vse = class {
+var Yse = { kernelName: Cr, backendName: "webgpu", kernelFunc: Xse };
+var Qse = class {
   constructor(e) {
-    this.uniforms = "", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
+    this.uniforms = "", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = S.computeOutShape(e, 1), this.variableNames = e.map((t, n) => `T${n}`), this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.offsetLength = e.length - 1;
     for (let t = 0; t < this.offsetLength; t++)
       this.uniforms += `offset${t} : i32,`;
     this.shaderKey = "concat";
@@ -33870,15 +33928,15 @@ var Vse = class {
     `;
   }
 };
-function Zp(e) {
+function eh(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
-  return es({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
+  return Wn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
 }
-var Wse = { kernelName: Zd, backendName: "webgpu", kernelFunc: Zp };
-function Ym(e, t, n) {
+var Zse = { kernelName: ep, backendName: "webgpu", kernelFunc: eh };
+function Zm(e, t, n) {
   let s = e[0].dtype;
   if (s === "complex64") {
-    let h = e.map((y) => sc({ inputs: { input: y }, backend: n })), f = e.map((y) => Zp({ inputs: { input: y }, backend: n })), m = Ym(h, t, n), g = Ym(f, t, n), b = lu({ inputs: { real: m, imag: g }, backend: n });
+    let h = e.map((y) => sc({ inputs: { input: y }, backend: n })), f = e.map((y) => eh({ inputs: { input: y }, backend: n })), m = Zm(h, t, n), g = Zm(f, t, n), b = cu({ inputs: { real: m, imag: g }, backend: n });
     return h.forEach((y) => n.disposeData(y.dataId)), f.forEach((y) => n.disposeData(y.dataId)), n.disposeData(m.dataId), n.disposeData(g.dataId), b;
   }
   let r = n.shouldExecuteOnCPU(e);
@@ -33886,10 +33944,10 @@ function Ym(e, t, n) {
     let h = e.map((x) => {
       let k = w.sizeFromShape(x.shape.slice(t));
       return Me({ inputs: { x }, backend: n, attrs: { shape: [-1, k] } });
-    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = S.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = Rne(f, m, s, g), y = S.computeOutShape(e.map((x) => x.shape), t), v = n.makeTensorInfo(y, s, b);
+    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = S.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = Wne(f, m, s, g), y = S.computeOutShape(e.map((x) => x.shape), t), v = n.makeTensorInfo(y, s, b);
     return h.forEach((x) => n.disposeData(x.dataId)), v;
   }
-  let { tensors2D: a, outShape: i } = Use(e, t, n), o = a.map((h) => h.shape), u = new Vse(o), l = [], c = new Array(o.length - 1);
+  let { tensors2D: a, outShape: i } = Jse(e, t, n), o = a.map((h) => h.shape), u = new Qse(o), l = [], c = new Array(o.length - 1);
   if (c.length > 0) {
     c[0] = o[0][1], l.push({ type: "int32", data: [c[0]] });
     for (let h = 1; h < c.length; h++)
@@ -33900,25 +33958,25 @@ function Ym(e, t, n) {
   let d = Me({ inputs: { x: p }, backend: n, attrs: { shape: i } });
   return n.disposeData(p.dataId), d;
 }
-function Use(e, t, n) {
+function Jse(e, t, n) {
   let s = S.computeOutShape(e.map((a) => a.shape), t);
   return { tensors2D: e.map((a) => Me({ inputs: { x: a }, backend: n, attrs: { shape: [w.sizeFromShape(a.shape.slice(0, t)), w.sizeFromShape(a.shape.slice(t))] } })), outShape: s };
 }
-function C2(e) {
+function A2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = S.computeOutShape(t.map((l) => l.shape), a);
   if (w.sizeFromShape(i) === 0)
     return n.makeTensorInfo(i, t[0].dtype, []);
   let o = t.filter((l) => w.sizeFromShape(l.shape) > 0);
   if (o.length === 1)
-    return es({ inputs: { x: o[0] }, backend: n });
+    return Wn({ inputs: { x: o[0] }, backend: n });
   let u = o.map((l) => l.shape);
-  return S.assertParamsConsistent(u, a), Ym(o, a, n);
+  return S.assertParamsConsistent(u, a), Zm(o, a, n);
 }
-var Gse = { kernelName: uo, backendName: "webgpu", kernelFunc: C2 };
-var Hse = class {
+var ere = { kernelName: co, backendName: "webgpu", kernelFunc: A2 };
+var tre = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
-      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`, this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.outputShape[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.hasLeakyreluAlpha = r, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.hasLeakyreluAlpha && this.variableNames.push("leakyreluAlpha"), this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`;
+      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`, this.workGroupSize = [8, 8, 1], this.isVec4 = true, this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.outputShape[1] === 1 ? this.elementsPerThread = [4, 1, 1] : this.elementsPerThread = [4, 4, 1], this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.hasLeakyreluAlpha = r, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.hasLeakyreluAlpha && this.variableNames.push("leakyreluAlpha"), this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`;
   }
   getShapeFit() {
     let e = [this.tileAOuter, this.tileInner], t = [this.tileInner, this.tileBOuter], n = this.outputShape[1] * this.outputShape[2], s = this.outputShape[3], r = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
@@ -33947,7 +34005,7 @@ var Hse = class {
     `;
   }
   getUserCode() {
-    let e = y2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner), s = `let outRow = r / uniforms.outShape[2];
+    let e = I2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner), s = `let outRow = r / uniforms.outShape[2];
         let outCol = r % uniforms.outShape[2];
         let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
         let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
@@ -33989,7 +34047,7 @@ var Hse = class {
          return vec4<f32>(0.0);
         `, i = "", o = "";
     if (this.activation) {
-      let c = Zs(this.activation, this.isVec4);
+      let c = Js(this.activation, this.isVec4);
       if (this.hasPreluActivationWeights)
         i = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
@@ -34042,9 +34100,9 @@ var Hse = class {
       `;
   }
 };
-var qse = class {
+var nre = class {
   constructor(e, t = false, n = null, s = false) {
-    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Cv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Nv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`;
+    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Tv(this.dispatchLayout, this.outputShape), this.elementsPerThread = $v(this.dispatchLayout, this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`;
   }
   getShapeFit() {
     let e = this.workGroupSize[1] * this.elementsPerThread[1], t = this.workGroupSize[0] * this.elementsPerThread[0], n = e > t ? e : t;
@@ -34053,7 +34111,7 @@ var qse = class {
     return [Ks(s, [a, o]), Ks(r, [o, i])];
   }
   getUserCode() {
-    let e = $v(this.elementsPerThread, this.workGroupSize), t = `
+    let e = Av(this.elementsPerThread, this.workGroupSize), t = `
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -34079,7 +34137,7 @@ var qse = class {
 	 return 0.0;
 	 `, r = "", a = "";
     if (this.activation) {
-      let u = Zs(this.activation, false);
+      let u = Js(this.activation, false);
       this.hasPreluActivationWeights ? r = `fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                   let b = getPreluActivationWeightsByOutputCoords(outCoord);
                   ${u}
@@ -34117,14 +34175,14 @@ var qse = class {
   `;
   }
 };
-var jse = class {
+var sre = class {
   constructor(e, t = false, n = null, s = false) {
-    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.shaderKey = `conv2DNaive_${this.activation}`;
+    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,", this.workGroupSize = [128, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.shaderKey = `conv2DNaive_${this.activation}`;
   }
   getUserCode() {
     let e = "", t = "";
     if (this.activation) {
-      let r = Zs(this.activation);
+      let r = Js(this.activation);
       this.hasPreluActivationWeights ? e = `fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${r}
@@ -34162,7 +34220,7 @@ var jse = class {
         }
       }
 
-      ${Dr()}
+      ${Fr()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let outChannel = coords[3];
@@ -34186,10 +34244,10 @@ var jse = class {
     `;
   }
 };
-var Kse = class {
+var rre = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
-      inChannels : i32,`, this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.isChannelsLast = t, this.shaderKey = `im2col_${this.isChannelsLast}`;
+      inChannels : i32,`, this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.isChannelsLast = t, this.shaderKey = `im2col_${this.isChannelsLast}`;
   }
   getUserCode() {
     let e = this.isChannelsLast ? 0 : 1, t = this.isChannelsLast ? 1 : 2;
@@ -34225,7 +34283,7 @@ var Kse = class {
   `;
   }
 };
-function Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function are({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let u = e.shape, l = n.dataFormat === "channelsLast", c = false, p = false, d = n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID", h, f;
   if (d) {
     let b = n.inHeight * n.inWidth * n.inChannels;
@@ -34234,15 +34292,15 @@ function Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAc
     let b = l ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3];
     h = Me({ inputs: { x: e }, backend: s, attrs: { shape: [1, b, n.inChannels] } }), f = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
   }
-  let m = _v({ a: h, b: f, transposeA: c, transposeB: p, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), g = Me({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
+  let m = Ev({ a: h, b: f, transposeA: c, transposeB: p, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), g = Me({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
   return s.disposeData(h.dataId), s.disposeData(f.dataId), s.disposeData(m.dataId), g;
 }
-function Yse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function ire({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let { filterWidth: u, filterHeight: l, inChannels: c, strideWidth: p, strideHeight: d, padInfo: h, outWidth: f, outHeight: m, dilationWidth: g, dilationHeight: b, dataFormat: y } = n, v = y === "channelsLast", x = u * l * c, k = m * f, T = [k, x], N = false, E = false, A = [], P = Me({ inputs: { x: e }, backend: s, attrs: { shape: e.shape.slice(1) } }), R = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, x, -1] } });
   A.push(P), A.push(R);
-  let F = new Kse(T, v), $ = [{ type: "int32", data: [h.left, h.top] }, { type: "int32", data: [p, d] }, { type: "int32", data: [g, b] }, { type: "int32", data: [f] }, { type: "int32", data: [c * u] }, { type: "int32", data: [c] }], z = s.runWebGPUProgram(F, [P], P.dtype, $), W = Me({ inputs: { x: z }, backend: s, attrs: { shape: [1, T[0], T[1]] } });
+  let F = new rre(T, v), $ = [{ type: "int32", data: [h.left, h.top] }, { type: "int32", data: [p, d] }, { type: "int32", data: [g, b] }, { type: "int32", data: [f] }, { type: "int32", data: [c * u] }, { type: "int32", data: [c] }], z = s.runWebGPUProgram(F, [P], P.dtype, $), W = Me({ inputs: { x: z }, backend: s, attrs: { shape: [1, T[0], T[1]] } });
   A.push(z), A.push(W);
-  let q = [1, T[0], T[1]], K = new v2(q, [1, k, n.outChannels], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), N, E, r, o, a), Y = q[1], Z = q[2], te = n.outChannels, ee = [{ type: "int32", data: [Y] }, { type: "int32", data: [te] }, { type: "int32", data: [Z] }], se = [W, R];
+  let q = [1, T[0], T[1]], K = new S2(q, [1, k, n.outChannels], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), N, E, r, o, a), Y = q[1], Z = q[2], te = n.outChannels, ee = [{ type: "int32", data: [Y] }, { type: "int32", data: [te] }, { type: "int32", data: [Z] }], se = [W, R];
   r && se.push(r), a && se.push(a), o === "leakyrelu" && ($.push({ type: "float32", data: [i] }), K.uniforms += " alpha : f32,");
   let ne = s.runWebGPUProgram(K, se, W.dtype, ee), oe = v ? [1, m, f, n.outChannels] : [1, n.outChannels, m, f], re = Me({ inputs: { x: ne }, backend: s, attrs: { shape: oe } });
   A.push(ne);
@@ -34250,31 +34308,31 @@ function Yse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAc
     s.disposeData(le.dataId);
   return re;
 }
-function N2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
+function E2({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
   let u = r != null, l = a != null, c;
   if (n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID" || n.filterHeight === 1 && n.filterWidth === 1 && n.dilationHeight === 1 && n.dilationWidth === 1 && n.strideHeight === 1 && n.strideWidth === 1 && (n.padInfo.type === "SAME" || n.padInfo.type === "VALID"))
-    return Xse({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
+    return are({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
   if (X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") && e.shape[0] === 1)
-    return Yse({ x: e, filter: t, convInfo: n, backend: s, bias: r, preluActivationWeights: a, leakyreluAlpha: i, activation: o });
+    return ire({ x: e, filter: t, convInfo: n, backend: s, bias: r, preluActivationWeights: a, leakyreluAlpha: i, activation: o });
   let d = X().getBool("WEBGPU_USE_NAIVE_CONV2D"), h = (n.inChannels % 4 === 0 || n.inChannels === 3 && n.padInfo.type === "VALID") && n.outChannels % 4 === 0, f = [n.padInfo.top, n.padInfo.left], m = [{ type: "int32", data: [n.filterHeight, n.filterWidth] }, { type: "int32", data: [...f] }, { type: "int32", data: [n.strideHeight, n.strideWidth] }, { type: "int32", data: [n.dilationHeight, n.dilationWidth] }];
   if (d)
-    c = new jse(n, u, o, l);
+    c = new sre(n, u, o, l);
   else {
-    h ? c = new Hse(n, u, o, l) : c = new qse(n, u, o, l);
+    h ? c = new tre(n, u, o, l) : c = new nre(n, u, o, l);
     let b = n.outShape[1] * n.outShape[2], y = n.outShape[3], v = n.filterHeight * n.filterWidth * n.inShape[3];
     m.push({ type: "int32", data: [b] }, { type: "int32", data: [y] }, { type: "int32", data: [v] });
   }
   let g = [e, t];
   return u && g.push(r), l && g.push(a), o === "leakyrelu" && (m.push({ type: "float32", data: [i] }), c.uniforms += " alpha : f32,"), s.runWebGPUProgram(c, g, e.dtype, m);
 }
-function Qse(e) {
+function ore(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = n, p = S.convertConv2DDataFormat(u), d = S.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p);
-  return N2({ x: r, filter: a, convInfo: d, backend: s });
+  return E2({ x: r, filter: a, convInfo: d, backend: s });
 }
-var Zse = { kernelName: $a, backendName: "webgpu", kernelFunc: Qse };
-var Jse = class {
+var ure = { kernelName: _a, backendName: "webgpu", kernelFunc: ore };
+var lre = class {
   constructor(e) {
-    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Cv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Nv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
+    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Tv(this.dispatchLayout, this.outputShape), this.elementsPerThread = $v(this.dispatchLayout, this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
   }
   getUserCode() {
     return `
@@ -34330,13 +34388,13 @@ var Jse = class {
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
 
-    ${$v(this.elementsPerThread, this.workGroupSize)}
+    ${Av(this.elementsPerThread, this.workGroupSize)}
   `;
   }
 };
-var ere = class {
+var cre = class {
   constructor(e) {
-    this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
+    this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
   }
   getUserCode() {
     let e = this.isChannelsLast ? 1 : 2, t = this.isChannelsLast ? 2 : 3, n = this.isChannelsLast ? 3 : 1;
@@ -34392,27 +34450,27 @@ var ere = class {
   `;
   }
 };
-function tre(e) {
+function dre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = S.convertConv2DDataFormat(l), d = S.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = [{ type: "int32", data: [d.filterHeight, d.filterWidth] }, { type: "int32", data: [d.filterHeight - 1 - d.padInfo.top, d.filterWidth - 1 - d.padInfo.left] }, { type: "int32", data: [d.strideHeight, d.strideWidth] }, { type: "int32", data: [d.batchSize, d.outHeight, d.outWidth, d.outChannels] }], f;
   if (X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))
-    f = new ere(d);
+    f = new cre(d);
   else {
-    f = new Jse(d);
+    f = new lre(d);
     let m = d.inShape[1] * d.inShape[2], g = d.inShape[3], b = d.filterHeight * d.filterWidth * d.outChannels;
     h.push({ type: "uint32", data: [m] }, { type: "uint32", data: [g] }, { type: "uint32", data: [b] });
   }
   return n.runWebGPUProgram(f, [r, a], "float32", h);
 }
-var nre = { kernelName: _a, backendName: "webgpu", kernelFunc: tre };
-var sre = Kt({ opType: 2 });
-var rre = { kernelName: Aa, backendName: "webgpu", kernelFunc: sre };
-var are = Kt({ opType: 3 });
-var ire = { kernelName: Ea, backendName: "webgpu", kernelFunc: are };
-var ore = class {
+var pre = { kernelName: Aa, backendName: "webgpu", kernelFunc: dre };
+var hre = Kt({ opType: 2 });
+var fre = { kernelName: Ea, backendName: "webgpu", kernelFunc: hre };
+var mre = Kt({ opType: 3 });
+var gre = { kernelName: Ra, backendName: "webgpu", kernelFunc: mre };
+var bre = class {
   constructor(e, t, n, s) {
     this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true;
     let [r] = t;
-    this.outputShape = [r, n[0], n[1], e], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.methodId = s === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
+    this.outputShape = [r, n[0], n[1], e], this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.methodId = s === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
   }
   getUserCode() {
     let [e, t] = ["f32(uniforms.imageShape[1] - 1)", "f32(uniforms.imageShape[2] - 1)"], [n, s, r] = this.cropHeightBiggerThan1 ? [`(${e} / f32(uniforms.outShape[1] - 1))`, "(y2-y1) * height_ratio", `y1*${e} + f32(y)*(height_scale)`] : ["0.0", "0.0", `0.5 * (y1+y2) * ${e}`], [a, i, o] = this.cropWidthBiggerThan1 ? [`(${t} / f32(uniforms.outShape[2] - 1))`, "(x2-x1) * width_ratio", `x1*${t} + f32(x)*(width_scale)`] : ["0.0", "0.0", `0.5 * (x1+x2) * ${t}`];
@@ -34475,14 +34533,94 @@ var ore = class {
     `;
   }
 };
-var ure = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new ore(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
+var yre = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new bre(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
   return n.runWebGPUProgram(c, [r, a, i], "float32", p);
 };
-var lre = { kernelName: co, backendName: "webgpu", kernelFunc: ure };
-var cre = class {
+var vre = { kernelName: ho, backendName: "webgpu", kernelFunc: yre };
+var Pw = class {
+  constructor(e, t, n, s) {
+    this.variableNames = ["x"], this.uniforms = "index : f32,", this.size = true;
+    let r = 128;
+    this.workGroupSize = [r, 1, 1], this.outputShape = t, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.exclusive = n, this.reverse = s, this.op = e, this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
+  }
+  getUserCode() {
+    let e = this.outputShape.length, t = this.op === "*" ? "1.0" : "0.0", n = this.exclusive ? t : `getX(${zw(e, "coords", this.op)})`, s = this.outputShape[this.outputShape.length - 1], r = "", a = "";
+    return this.exclusive ? (r = this.reverse ? `end != ${s - 1}` : "end != 0", a = this.reverse ? "end + 1" : "end - 1") : (r = this.reverse ? `end + pow2 < ${s}` : "end >= pow2", a = this.reverse ? "end + pow2" : "end - pow2"), `
+      ${Ue()}
+       if (index < uniforms.size) {
+         var coords = getCoordsFromIndex(index);
+
+         let end = ${Mw(e, "coords", this.op)};
+         var val = ${n};
+         let pow2 = i32(pow(2.0, uniforms.index));
+         if (${r}) {
+           let idx = ${a};
+           ${Mw(e, "coords", this.op)} = idx;
+           val ${this.op}= getX(${zw(e, "coords", this.op)});
+         }
+         setOutputAtIndex(index, val);
+       }
+      }
+    `;
+  }
+};
+function zw(e, t, n) {
+  if (e === 1)
+    return `${t}`;
+  if (e === 2)
+    return `${t}.x, ${t}.y`;
+  if (e === 3)
+    return `${t}.x, ${t}.y, ${t}.z`;
+  if (e === 4)
+    return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
+  throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
+}
+function Mw(e, t, n) {
+  if (e === 1)
+    return `${t}`;
+  if (e === 2)
+    return `${t}.y`;
+  if (e === 3)
+    return `${t}.z`;
+  if (e === 4)
+    return `${t}.w`;
+  throw Error(`Cumulative ${n} for rank ${e} is not yet supported`);
+}
+function R2(e, t, n, s, r, a) {
+  let i = t.shape.length, o = S.getAxesPermutation([s], i), u = t;
+  o != null && (u = Xs({ inputs: { x: t }, backend: n, attrs: { perm: o } }));
+  let l = S.getInnerMostAxes(1, i)[0];
+  if (l !== i - 1)
+    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length - 1} but got axis=${s}`);
+  let c = u.shape[l], p = Wn({ inputs: { x: u }, backend: n });
+  for (let d = 0; d <= Math.ceil(Math.log2(c)) - 1; d++) {
+    let h = new Pw(e, u.shape, false, a), f = p, m = [{ type: "float32", data: [d] }];
+    p = n.runWebGPUProgram(h, [p], p.dtype, m), n.disposeData(f.dataId);
+  }
+  if (r) {
+    let d = new Pw(e, u.shape, r, a), h = p, f = [{ type: "float32", data: [0] }];
+    p = n.runWebGPUProgram(d, [p], p.dtype, f), n.disposeData(h.dataId);
+  }
+  if (o != null) {
+    let d = S.getUndoAxesPermutation(o), h = Xs({ inputs: { x: p }, backend: n, attrs: { perm: d } });
+    return n.disposeData(p.dataId), n.disposeData(u.dataId), h;
+  }
+  return p;
+}
+function xre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
+  return R2("*", r, n, a, i, o);
+}
+var wre = { kernelName: po, backendName: "webgpu", kernelFunc: xre };
+function kre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
+  return R2("+", r, n, a, i, o);
+}
+var Ire = { kernelName: Da, backendName: "webgpu", kernelFunc: kre };
+var Sre = class {
   constructor(e, t) {
-    this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${t}`, this.dataFormat = t;
+    this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32,", this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${t}`, this.dataFormat = t;
   }
   getUserCode() {
     return `
@@ -34523,19 +34661,19 @@ var cre = class {
     return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
   }
 };
-function dre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new cre(f, i);
+function Cre(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new Sre(f, i);
   return n.runWebGPUProgram(g, [r], r.dtype, m);
 }
-var pre = { kernelName: po, backendName: "webgpu", kernelFunc: dre };
-var T2 = class {
+var Nre = { kernelName: fo, backendName: "webgpu", kernelFunc: Cre };
+var D2 = class {
   constructor(e, t = false, n = null, s = false) {
-    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
+    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
   }
   getUserCode() {
     let e = "", t = "";
     if (this.activation) {
-      let r = Zs(this.activation, this.isVec4);
+      let r = Js(this.activation, this.isVec4);
       this.hasPreluActivation ? e = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
@@ -34549,7 +34687,7 @@ var T2 = class {
     return `
       ${e}
 
-      ${Sv()}
+      ${Nv()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -34614,16 +34752,16 @@ var T2 = class {
     `;
   }
 };
-var $2 = class {
+var F2 = class {
   constructor(e, t = false, n = null, s = false) {
     this.variableNames = ["x", "W"], this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
-      channelMul : i32,`, this.workGroupSize = [256, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise_${this.activation}`;
+      channelMul : i32,`, this.workGroupSize = [256, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise_${this.activation}`;
   }
   getUserCode() {
     let e = "", t = "";
     if (this.activation) {
-      let r = Zs(this.activation, false);
+      let r = Js(this.activation, false);
       this.hasPreluActivation ? e = `fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
@@ -34645,7 +34783,7 @@ var $2 = class {
         }
       }
 
-      ${Dr()}
+      ${Fr()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
@@ -34710,20 +34848,20 @@ var $2 = class {
     `;
   }
 };
-function hre(e) {
+function Tre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
   c == null && (c = [1, 1]);
   let p = S.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d = [{ type: "int32", data: [p.padInfo.top, p.padInfo.left] }, { type: "int32", data: [p.strideHeight, p.strideWidth] }, { type: "int32", data: [p.dilationHeight, p.dilationWidth] }, { type: "int32", data: [p.inHeight, p.inWidth] }], h;
-  return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.dilationHeight === 1 && p.dilationWidth === 1 && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new T2(p) : (h = new $2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
+  return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.dilationHeight === 1 && p.dilationWidth === 1 && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new D2(p) : (h = new F2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
 }
-var fre = { kernelName: Ra, backendName: "webgpu", kernelFunc: hre };
-var _2 = mn({ opSnippet: 0, cpuKernelImpl: jne, supportsComplex: true });
-var mre = { kernelName: Ya, backendName: "webgpu", kernelFunc: _2 };
-var gre = class {
+var $re = { kernelName: Fa, backendName: "webgpu", kernelFunc: Tre };
+var O2 = mn({ opSnippet: 0, cpuKernelImpl: sse, supportsComplex: true });
+var _re = { kernelName: Za, backendName: "webgpu", kernelFunc: O2 };
+var Are = class {
   constructor(e, t) {
     this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32,", this.size = true, this.inputShape = [e.batchSize, e.inSize];
     let [n] = S.computeOutAndReduceShapes(this.inputShape, [1]);
-    this.outputShape = n.length === 0 ? [1] : n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t, this.shaderKey = `reduce_${t}`;
+    this.outputShape = n.length === 0 ? [1] : n, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t, this.shaderKey = `reduce_${t}`;
   }
   getUserCode() {
     let e = "", t = "0.0";
@@ -34782,7 +34920,7 @@ var gre = class {
 };
 function rc(e, t, n, s, r) {
   let a = e.shape.length, i = [], o = w.parseAxisParam(t, e.shape), u = o, l = S.getAxesPermutation(u, a), c = e;
-  l != null && (c = wi({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = S.getInnerMostAxes(u.length, a), i.push(c)), S.assertAxesAreInnerMostDims(s, u, a);
+  l != null && (c = Xs({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = S.getInnerMostAxes(u.length, a), i.push(c)), S.assertAxesAreInnerMostDims(s, u, a);
   let [p, d] = S.computeOutAndReduceShapes(c.shape, u), h = p;
   n && (h = S.expandShapeToKeepDim(p, o));
   let f;
@@ -34790,63 +34928,63 @@ function rc(e, t, n, s, r) {
     let m = r.tensorMap.get(c.dataId).values;
     switch (s) {
       case "max":
-        let g = Gne(m, w.sizeFromShape(d), h, e.dtype);
+        let g = ese(m, w.sizeFromShape(d), h, e.dtype);
         f = r.makeTensorInfo(h, e.dtype, g);
         break;
       case "prod":
-        let { outVals: b, outShape: y, outDtype: v } = Yne(c.shape, c.dtype, m, u);
+        let { outVals: b, outShape: y, outDtype: v } = ise(c.shape, c.dtype, m, u);
         f = r.makeTensorInfo(y, v, b);
         break;
       default:
         throw new Error(`${s} CPU implementation is not yet supported.`);
     }
   } else {
-    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : cp(e.dtype), x = [{ type: "int32", data: [m] }], k = new gre(y, s), T = r.runWebGPUProgram(k, [c], v, x);
+    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : pp(e.dtype), x = [{ type: "int32", data: [m] }], k = new Are(y, s), T = r.runWebGPUProgram(k, [c], v, x);
     i.push(T), f = Me({ inputs: { x: T }, attrs: { shape: h }, backend: r });
   }
   return i.forEach((m) => r.disposeData(m.dataId)), f;
 }
-function Av(e) {
+function Rv(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   return rc(r, a, i, "sum", n);
 }
-var bre = { kernelName: oi, backendName: "webgpu", kernelFunc: Av };
-function yre(e) {
+var Ere = { kernelName: ci, backendName: "webgpu", kernelFunc: Rv };
+function Rre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = S.decodeEinsumEquation(r, a.length);
   S.checkEinsumDimSizes(i.length, u, a);
   let { path: l, steps: c } = S.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
   for (let m = 0; m < p; ++m) {
     for (let g of c[m]) {
       let { permutationIndices: b, expandDims: y } = S.getEinsumPermutation(h, u[g]), v;
-      S.isIdentityPermutation(b) ? v = a[g] : (v = wi({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
+      S.isIdentityPermutation(b) ? v = a[g] : (v = Xs({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
       let x = v.shape.slice();
       for (let k = 0; k < y.length; ++k)
         x.splice(y[k], 0, 1);
-      w.arraysEqual(v.shape, x) || (v = Me({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = _2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
+      w.arraysEqual(v.shape, x) || (v = Me({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = O2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = Av({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = Rv({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeData(m.dataId);
   return d;
 }
-var vre = { kernelName: Qd, backendName: "webgpu", kernelFunc: yre };
-var xre = Kt({ opType: 4 });
-var wre = { kernelName: Fa, backendName: "webgpu", kernelFunc: xre };
-var kre = mn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: Dne });
-var Ire = { kernelName: ho, backendName: "webgpu", kernelFunc: kre };
-var A2 = Kt({ opType: 5, cpuKernelImpl: Fne, dtype: "float32" });
-var Sre = { kernelName: Oa, backendName: "webgpu", kernelFunc: A2 };
-function Qm(e) {
+var Dre = { kernelName: Jd, backendName: "webgpu", kernelFunc: Rre };
+var Fre = Kt({ opType: 4 });
+var Ore = { kernelName: Pa, backendName: "webgpu", kernelFunc: Fre };
+var Pre = mn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: Une });
+var zre = { kernelName: mo, backendName: "webgpu", kernelFunc: Pre };
+var P2 = Kt({ opType: 5, cpuKernelImpl: Gne, dtype: "float32" });
+var Mre = { kernelName: za, backendName: "webgpu", kernelFunc: P2 };
+function Jm(e) {
   let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
   return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), Me({ inputs: { x: a }, backend: s, attrs: { shape: o } });
 }
-var Cre = { kernelName: fo, backendName: "webgpu", kernelFunc: Qm };
-var Nre = Kt({ opType: 6, cpuKernelImpl: One });
-var Tre = { kernelName: mo, backendName: "webgpu", kernelFunc: Nre };
-var $re = class {
+var Lre = { kernelName: go, backendName: "webgpu", kernelFunc: Jm };
+var Bre = Kt({ opType: 6, cpuKernelImpl: Hne });
+var Vre = { kernelName: bo, backendName: "webgpu", kernelFunc: Bre };
+var Wre = class {
   constructor(e) {
-    this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "fill";
+    this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "fill";
   }
   getUserCode() {
     return `
@@ -34858,20 +34996,20 @@ var $re = class {
   `;
   }
 };
-function du(e) {
+function pu(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
   if (a = a || w.inferDtype(r), a === "string") {
     let i = w.getArrayFromDType(a, w.sizeFromShape(s));
     return i.fill(r), t.makeTensorInfo(s, a, i);
   } else {
-    let i = new $re(s), o = [{ type: "float32", data: [r] }];
+    let i = new Wre(s), o = [{ type: "float32", data: [r] }];
     return t.runWebGPUProgram(i, [], a, o);
   }
 }
-var _re = { kernelName: ml, backendName: "webgpu", kernelFunc: du };
-var Are = class {
+var Ure = { kernelName: ml, backendName: "webgpu", kernelFunc: pu };
+var Gre = class {
   constructor(e) {
-    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "flipLeftRight";
+    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "flipLeftRight";
   }
   getUserCode() {
     return `
@@ -34886,29 +35024,29 @@ var Are = class {
     `;
   }
 };
-var Ere = { kernelName: go, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { image: n } = e, s = t, r = new Are(n.shape);
+var Hre = { kernelName: yo, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { image: n } = e, s = t, r = new Gre(n.shape);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
-var Rre = Kt({ opType: 7, cpuKernelImpl: Pne });
-var Dre = { kernelName: Pa, backendName: "webgpu", kernelFunc: Rre };
-var Fre = mn({ opSnippet: 12, dtype: "int32" });
-var Ore = { kernelName: za, backendName: "webgpu", kernelFunc: Fre };
-var Pre = (e, t, n, s, r) => {
+var qre = Kt({ opType: 7, cpuKernelImpl: qne });
+var jre = { kernelName: Ma, backendName: "webgpu", kernelFunc: qre };
+var Kre = mn({ opSnippet: 12, dtype: "int32" });
+var Xre = { kernelName: La, backendName: "webgpu", kernelFunc: Kre };
+var Yre = (e, t, n, s, r) => {
   let a = [s, ...n];
   return r && a.push(r), e.createBindGroup({ layout: t, entries: a.map((i, o) => ({ binding: o, resource: i })) });
 };
-var E2 = (e, t, n, s, r, a = false) => {
-  let i = { dtype: r.dtype, shape: r.shape }, o = cne(s, i, t, a), u = e.createShaderModule({ code: o, label: t.constructor.name });
+var z2 = (e, t, n, s, r, a = false) => {
+  let i = { dtype: r.dtype, shape: r.shape }, o = xne(s, i, t, a), u = e.createShaderModule({ code: o, label: t.constructor.name });
   return e.createComputePipeline({ layout: n, compute: { module: u, entryPoint: "main" }, label: t.constructor.name });
 };
-function R2(e, t, n, s = "", r = "") {
+function M2(e, t, n, s = "", r = "") {
   return e.shaderKey + "_" + (e.workGroupSize ? e.workGroupSize.join(",") : "") + t.map((i) => i.length).join(",") + n.join(",") + e.variableNames.join(",") + s + r;
 }
-function Ow(e) {
+function Lw(e) {
   let { externalImage: t, backend: n, attrs: s, outShape: r, useImport: a } = e, { numChannels: i } = s, o = w.sizeFromShape(r), u = w.computeStrides(r), l = n.makeTensorInfo(r, "int32"), c = n.getFromPixelsProgram(a ? "import" : "copyExternal");
   c.updateOutputShape(r);
-  let p = [l.shape], d = [l.dtype, a ? "import" : "copyExternal"], h = R2(c, p, d), f = c.getLayout(n.device), m = n.getAndSavePipeline(h, () => E2(n.device, c, f.pipelineLayout, [], l, true));
+  let p = [l.shape], d = [l.dtype, a ? "import" : "copyExternal"], h = M2(c, p, d), f = c.getLayout(n.device), m = n.getAndSavePipeline(h, () => z2(n.device, c, f.pipelineLayout, [], l, true));
   c.setPipeline(m), a || n.queue.copyExternalImageToTexture({ source: t, origin: { x: 0, y: 0 } }, { texture: c.makeInputTexture(n.device, r[1], r[0]) }, [r[1], r[0]]);
   let g = n.tensorMap.get(l.dataId);
   g.bufferInfo.buffer = n.acquireBuffer(g.bufferInfo.byteSize);
@@ -34922,17 +35060,17 @@ function Ow(e) {
     y = c.inputTexture.createView();
   return n.runFromPixelsProgram(c, g.bufferInfo.buffer, f, y, l.dataId), l;
 }
-var zre = { kernelName: fd, backendName: "webgpu", kernelFunc: Mre };
-var Bi;
-function Mre(e) {
+var Qre = { kernelName: fd, backendName: "webgpu", kernelFunc: Zre };
+var Wi;
+function Zre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s;
   if (r == null)
     throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
   let i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, u = typeof HTMLCanvasElement != "undefined" && r instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && r instanceof OffscreenCanvas, l = typeof ImageBitmap != "undefined" && r instanceof ImageBitmap, [c, p] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], d = [p, c, a];
   if (X().getBool("WEBGPU_USE_IMPORT") && i)
-    return Ow({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
-  if ((i || o) && (Bi == null && (Bi = document.createElement("canvas").getContext("2d")), Bi.canvas.width = c, Bi.canvas.height = p, Bi.drawImage(r, 0, 0, c, p), r = Bi.canvas), l || u || i || o)
-    return Ow({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: false });
+    return Lw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
+  if ((i || o) && (Wi == null && (Wi = document.createElement("canvas").getContext("2d")), Wi.canvas.width = c, Wi.canvas.height = p, Wi.drawImage(r, 0, 0, c, p), r = Wi.canvas), l || u || i || o)
+    return Lw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: false });
   let h = r.data, f = h;
   if (a != null && a !== 4) {
     f = new Uint8Array(r.width * r.height * a);
@@ -34943,9 +35081,9 @@ function Mre(e) {
   let m = n.makeTensorInfo(d, "int32"), g = n.tensorMap.get(m.dataId);
   return g.values = new Int32Array(f), n.maybeReleaseBuffer(m.dataId), n.uploadToGPU(m.dataId), m;
 }
-var Lre = class {
+var Jre = class {
   constructor(e, t, n, s, r) {
-    this.uniforms = "varianceEpsilon : f32,", this.workGroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t), S.assertAndGetBroadcastShape(e, n), this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset")), r != null && (S.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale")), this.offsetShape = s, this.scaleShape = r, this.shaderKey = "batchNorm";
+    this.uniforms = "varianceEpsilon : f32,", this.workGroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], S.assertAndGetBroadcastShape(e, t), S.assertAndGetBroadcastShape(e, n), this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), s != null && (S.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset")), r != null && (S.assertAndGetBroadcastShape(e, r), this.variableNames.push("scale")), this.offsetShape = s, this.scaleShape = r, this.shaderKey = "batchNorm";
   }
   getUserCode() {
     let e = "0.0";
@@ -34967,31 +35105,31 @@ var Lre = class {
   `;
   }
 };
-var Bre = { kernelName: Ma, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var eae = { kernelName: Ba, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s, scale: r, offset: a, mean: i, variance: o } = e, { varianceEpsilon: u } = t, l = n, c = [s, i, o], p = null;
   a != null && (p = a.shape, c.push(a));
   let d = null;
   r != null && (d = r.shape, c.push(r));
-  let h = new Lre(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
+  let h = new Jre(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
   return l.runWebGPUProgram(h, c, s.dtype, f);
 } };
-function Vre(e) {
+function tae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = S.convertConv2DDataFormat(c), g = S.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m);
-  return N2({ x: r, filter: a, convInfo: g, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: f, activation: h });
+  return E2({ x: r, filter: a, convInfo: g, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: f, activation: h });
 }
-var Wre = { kernelName: aa, backendName: "webgpu", kernelFunc: Vre };
-function Ure(e) {
+var nae = { kernelName: ia, backendName: "webgpu", kernelFunc: tae };
+function sae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = c;
   f == null && (f = [1, 1]), w.assert(S.eitherStridesOrDilationsAreOne(u, f), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);
   let m = S.computeConv2DInfo(r.shape, a.shape, u, f, l, p, true), g = [r, a], b = i != null, y = o != null;
   b && g.push(i), y && g.push(o);
   let v = [{ type: "int32", data: [m.padInfo.top, m.padInfo.left] }, { type: "int32", data: [m.strideHeight, m.strideWidth] }, { type: "int32", data: [m.dilationHeight, m.dilationWidth] }, { type: "int32", data: [m.inHeight, m.inWidth] }], x;
-  return m.batchSize === 1 && m.inHeight === m.outHeight && m.inWidth === m.outWidth && m.strideHeight === 1 && m.strideWidth === 1 && m.filterHeight === m.filterWidth && m.inChannels === m.outChannels && m.dilationHeight === 1 && m.dilationWidth === 1 && m.filterHeight === 3 && m.inChannels % 4 === 0 ? x = new T2(m, b, d, y) : (x = new $2(m, b, d, y), v.push({ type: "int32", data: [m.filterHeight] }, { type: "int32", data: [m.filterWidth] }, { type: "int32", data: [m.outChannels / m.inChannels] })), d === "leakyrelu" && (v.push({ type: "float32", data: [h] }), x.uniforms += " alpha : f32,"), n.runWebGPUProgram(x, g, "float32", v);
+  return m.batchSize === 1 && m.inHeight === m.outHeight && m.inWidth === m.outWidth && m.strideHeight === 1 && m.strideWidth === 1 && m.filterHeight === m.filterWidth && m.inChannels === m.outChannels && m.dilationHeight === 1 && m.dilationWidth === 1 && m.filterHeight === 3 && m.inChannels % 4 === 0 ? x = new D2(m, b, d, y) : (x = new F2(m, b, d, y), v.push({ type: "int32", data: [m.filterHeight] }, { type: "int32", data: [m.filterWidth] }, { type: "int32", data: [m.outChannels / m.inChannels] })), d === "leakyrelu" && (v.push({ type: "float32", data: [h] }), x.uniforms += " alpha : f32,"), n.runWebGPUProgram(x, g, "float32", v);
 }
-var Gre = { kernelName: ia, backendName: "webgpu", kernelFunc: Ure };
-var Hre = class {
+var rae = { kernelName: oa, backendName: "webgpu", kernelFunc: sae };
+var aae = class {
   constructor(e, t) {
-    this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Wt(e)},`;
+    this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `gathernd_${e}`, this.sliceDim = e, this.uniforms = `sliceDim : i32, strides : ${Wt(e)},`;
   }
   getUserCode() {
     let e;
@@ -35012,22 +35150,22 @@ var Hre = class {
       `;
   }
 };
-function qre(e) {
+function iae(e) {
   let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = S.prepareAndValidate(s, r), d = Me({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = Me({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
   if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
-    let y = n.readSync(r.dataId), v = n.bufferSync(s), x = zne(y, v, s.dtype, l, i, c, p, s.shape, o);
+    let y = n.readSync(r.dataId), v = n.bufferSync(s), x = jne(y, v, s.dtype, l, i, c, p, s.shape, o);
     return n.makeTensorInfo(u, s.dtype, x.values);
   }
-  let f = new Hre(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: u } });
+  let f = new aae(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: u } });
   return n.disposeData(d.dataId), n.disposeData(h.dataId), n.disposeData(g.dataId), b;
 }
-var jre = { kernelName: yo, backendName: "webgpu", kernelFunc: qre };
-var Kre = class {
+var oae = { kernelName: xo, backendName: "webgpu", kernelFunc: iae };
+var uae = class {
   constructor(e, t) {
-    this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "gather";
+    this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "gather";
   }
   getUserCode() {
-    let e = Xre(this.aShape);
+    let e = lae(this.aShape);
     return `
       ${Ue()}
         if (index < uniforms.size) {
@@ -35040,78 +35178,78 @@ var Kre = class {
     `;
   }
 };
-function Xre(e) {
+function lae(e) {
   let t = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], n = [];
   for (let s = 0; s < e.length; s++)
     s === 2 ? n.push("indexZ") : n.push(`${t[s]}`);
   return n.join();
 }
-function D2(e) {
+function L2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = S.segment_util.collectGatherOpShapeInfo(r, a, u, o), c = w.sizeFromShape(a.shape), p = [], d = Me({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = Me({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
   p.push(d), p.push(h);
   let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
   if (n.shouldExecuteOnCPU([r, a])) {
-    let v = n.tensorMap.get(h.dataId).values, x = De(h.shape, h.dtype, v), T = n.tensorMap.get(d.dataId).values, N = De(d.shape, d.dtype, T), E = Mne(N, x, f);
+    let v = n.tensorMap.get(h.dataId).values, x = De(h.shape, h.dtype, v), T = n.tensorMap.get(d.dataId).values, N = De(d.shape, d.dtype, T), E = Kne(N, x, f);
     return p.forEach((A) => n.disposeData(A.dataId)), n.makeTensorInfo(l.outputShape, E.dtype, E.values);
   }
-  let m = new Kre(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
+  let m = new uae(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
   p.push(g);
   let b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeData(y.dataId)), b;
 }
-var Yre = { kernelName: bo, backendName: "webgpu", kernelFunc: D2 };
-var Qre = mn({ opSnippet: 5, cpuKernelImpl: Bne, dtype: "bool" });
-var Zre = { kernelName: vo, backendName: "webgpu", kernelFunc: Qre };
-var Jre = mn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: Lne });
-var eae = { kernelName: La, backendName: "webgpu", kernelFunc: Jre };
-function tae(e) {
+var cae = { kernelName: vo, backendName: "webgpu", kernelFunc: L2 };
+var dae = mn({ opSnippet: 5, cpuKernelImpl: Yne, dtype: "bool" });
+var pae = { kernelName: wo, backendName: "webgpu", kernelFunc: dae };
+var hae = mn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: Xne });
+var fae = { kernelName: Va, backendName: "webgpu", kernelFunc: hae };
+function mae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new nc(r.shape, 14);
   return o.uniforms = "alpha : f32,", n.runWebGPUProgram(o, [r], "float32", i);
 }
-var nae = { kernelName: Va, backendName: "webgpu", kernelFunc: tae };
-var sae = mn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: Wne });
-var rae = { kernelName: xo, backendName: "webgpu", kernelFunc: sae };
-var aae = mn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: Vne });
-var iae = { kernelName: wo, backendName: "webgpu", kernelFunc: aae };
-var oae = Kt({ opType: 9, cpuKernelImpl: Une });
-var uae = { kernelName: Wa, backendName: "webgpu", kernelFunc: oae };
-var lae = mn({ opSnippet: 9, dtype: "bool" });
-var cae = { kernelName: ko, backendName: "webgpu", kernelFunc: lae };
-var dae = Kt({ opType: 10 });
-var pae = { kernelName: xl, backendName: "webgpu", kernelFunc: dae };
-function F2(e) {
+var gae = { kernelName: Ua, backendName: "webgpu", kernelFunc: mae };
+var bae = mn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: Zne });
+var yae = { kernelName: ko, backendName: "webgpu", kernelFunc: bae };
+var vae = mn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: Qne });
+var xae = { kernelName: Io, backendName: "webgpu", kernelFunc: vae };
+var wae = Kt({ opType: 9, cpuKernelImpl: Jne });
+var kae = { kernelName: Ga, backendName: "webgpu", kernelFunc: wae };
+var Iae = mn({ opSnippet: 9, dtype: "bool" });
+var Sae = { kernelName: So, backendName: "webgpu", kernelFunc: Iae };
+var Cae = Kt({ opType: 10 });
+var Nae = { kernelName: xl, backendName: "webgpu", kernelFunc: Cae };
+function B2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s;
   return rc(r, a, i, "max", n);
 }
-var hae = { kernelName: Ua, backendName: "webgpu", kernelFunc: F2 };
-var fae = mn({ opSnippet: 15, cpuKernelImpl: Hne });
-var mae = { kernelName: Ga, backendName: "webgpu", kernelFunc: fae };
-function gae(e) {
+var Tae = { kernelName: Ha, backendName: "webgpu", kernelFunc: B2 };
+var $ae = mn({ opSnippet: 15, cpuKernelImpl: tse });
+var _ae = { kernelName: qa, backendName: "webgpu", kernelFunc: $ae };
+function Aae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = S.computePool2DInfo(r.shape, a, i, l, o, u), p, d = [];
   if (c.filterHeight === 1 && c.filterWidth === 1) {
     if (w.arraysEqual(c.inShape, c.outShape))
-      return es({ inputs: { x: r }, backend: n });
-    p = new I2(c), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] });
+      return Wn({ inputs: { x: r }, backend: n });
+    p = new $2(c), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] });
   } else
-    p = new k2(c, "max"), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] });
+    p = new T2(c, "max"), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] }, { type: "int32", data: [c.padInfo.top, c.padInfo.left] }, { type: "int32", data: [c.dilationHeight, c.dilationWidth] }, { type: "int32", data: [c.inHeight, c.inWidth] }, { type: "int32", data: [c.effectiveFilterHeight, c.effectiveFilterWidth] });
   return n.runWebGPUProgram(p, [r], r.dtype, d);
 }
-var bae = { kernelName: Ha, backendName: "webgpu", kernelFunc: gae };
-function yae(e) {
+var Eae = { kernelName: ja, backendName: "webgpu", kernelFunc: Aae };
+function Rae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: i } = s;
   return rc(r, i, a, "mean", n);
 }
-var vae = { kernelName: qa, backendName: "webgpu", kernelFunc: yae };
-function xae(e) {
+var Dae = { kernelName: Ka, backendName: "webgpu", kernelFunc: Rae };
+function Fae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   return rc(r, a, i, "min", n);
 }
-var wae = { kernelName: ja, backendName: "webgpu", kernelFunc: xae };
-var kae = mn({ opSnippet: 16, cpuKernelImpl: qne });
-var Iae = { kernelName: Ka, backendName: "webgpu", kernelFunc: kae };
-var Sae = class {
+var Oae = { kernelName: Xa, backendName: "webgpu", kernelFunc: Fae };
+var Pae = mn({ opSnippet: 16, cpuKernelImpl: nse });
+var zae = { kernelName: Ya, backendName: "webgpu", kernelFunc: Pae };
+var Mae = class {
   constructor(e, t, n) {
-    this.uniforms = "", this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((s, r) => s[0] + e[r] + s[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.xShape = e, t.map((s, r) => {
+    this.uniforms = "", this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((s, r) => s[0] + e[r] + s[1]), this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.xShape = e, t.map((s, r) => {
       this.uniforms += ` pad${r} : vec2<i32>,`;
     }), this.offset = n === "reflect" ? 0 : 1, this.shaderKey = `mirrorPad_${n}`;
   }
@@ -35137,70 +35275,70 @@ var Sae = class {
     `;
   }
 };
-var Cae = { kernelName: Xa, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { x: s } = e, { paddings: r, mode: a } = t, i = n, o = r.map((c) => ({ type: "int32", data: [c[0], c[1]] })), u = new Sae(s.shape, r, a);
+var Lae = { kernelName: Qa, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { x: s } = e, { paddings: r, mode: a } = t, i = n, o = r.map((c) => ({ type: "int32", data: [c[0], c[1]] })), u = new Mae(s.shape, r, a);
   return i.runWebGPUProgram(u, [s], s.dtype, o);
 } };
-function Nae(e) {
+function Bae(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s])) {
-    let a = n.tensorMap.get(s.dataId), [i, o] = Kne(a.values, s.shape, s.dtype);
+    let a = n.tensorMap.get(s.dataId), [i, o] = rse(a.values, s.shape, s.dtype);
     return n.makeTensorInfo(o, s.dtype, i);
   }
   let r = new nc(s.shape, 11);
   return n.runWebGPUProgram(r, [s], s.dtype);
 }
-var Tae = { kernelName: Io, backendName: "webgpu", kernelFunc: Nae };
-function $ae(e) {
+var Vae = { kernelName: Co, backendName: "webgpu", kernelFunc: Bae };
+function Wae(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = xs.nonMaxSuppressionV3Impl(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var _ae = { kernelName: Co, backendName: "webgpu", kernelFunc: $ae };
-function Aae(e) {
+var Uae = { kernelName: To, backendName: "webgpu", kernelFunc: Wae };
+function Gae(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = xs.nonMaxSuppressionV5Impl(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var Eae = { kernelName: No, backendName: "webgpu", kernelFunc: Aae };
-function Ld(e) {
+var Hae = { kernelName: $o, backendName: "webgpu", kernelFunc: Gae };
+function Vd(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
-    let r = sc({ inputs: { input: s }, backend: n }), a = Ld({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = lu({ inputs: { real: a, imag: o }, backend: n });
+    let r = sc({ inputs: { input: s }, backend: n }), a = Vd({ inputs: { x: r }, backend: n }), i = eh({ inputs: { input: s }, backend: n }), o = Vd({ inputs: { x: i }, backend: n }), u = cu({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
   } else
-    return du({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
+    return pu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var Rae = { kernelName: Ho, backendName: "webgpu", kernelFunc: Ld };
-function O2(e) {
+var qae = { kernelName: qo, backendName: "webgpu", kernelFunc: Vd };
+function V2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported under string dtype");
   if (s.dtype === "complex64") {
-    let r = sc({ inputs: { input: s }, backend: n }), a = O2({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = lu({ inputs: { real: a, imag: o }, backend: n });
+    let r = sc({ inputs: { input: s }, backend: n }), a = V2({ inputs: { x: r }, backend: n }), i = eh({ inputs: { input: s }, backend: n }), o = Vd({ inputs: { x: i }, backend: n }), u = cu({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
   } else
-    return du({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
+    return pu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var Dae = { kernelName: To, backendName: "webgpu", kernelFunc: O2 };
-function Fae(e) {
+var jae = { kernelName: _o, backendName: "webgpu", kernelFunc: V2 };
+function Kae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Qm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return Jm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = Qm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = Jm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
-  }), l = C2({ inputs: u, backend: n, attrs: { axis: r } });
+  }), l = A2({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var Oae = { kernelName: _o, backendName: "webgpu", kernelFunc: Fae };
-var Pae = class {
+var Xae = { kernelName: Eo, backendName: "webgpu", kernelFunc: Kae };
+var Yae = class {
   constructor(e, t) {
-    this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), t.map((n, s) => {
+    this.variableNames = ["x"], this.uniforms = "constantValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), t.map((n, s) => {
       this.uniforms += ` pad${s} : vec2<i32>,`;
     }), this.xShape = e, this.shaderKey = "pad";
   }
@@ -35224,46 +35362,46 @@ var Pae = class {
     `;
   }
 };
-var P2 = (e) => {
+var W2 = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
   if (a.every((l) => w.arraysEqual(l, [0, 0])))
-    return es({ inputs: { x: r }, backend: n });
+    return Wn({ inputs: { x: r }, backend: n });
   if (w.sizeFromShape(r.shape) === 0) {
     let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
-    return du({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
+    return pu({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
   }
   let o = [{ type: "float32", data: [i] }];
   a.map((l) => o.push({ type: "int32", data: [l[0], l[1]] }));
-  let u = new Pae(r.shape, a);
+  let u = new Yae(r.shape, a);
   return n.runWebGPUProgram(u, [r], r.dtype, o);
 };
-var zae = { kernelName: Qa, backendName: "webgpu", kernelFunc: P2 };
-var Mae = mn({ opSnippet: 13 });
-var Lae = { kernelName: Za, backendName: "webgpu", kernelFunc: Mae };
-function Bae(e) {
-  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new x2(14, s.shape, r.shape);
+var Qae = { kernelName: Ja, backendName: "webgpu", kernelFunc: W2 };
+var Zae = mn({ opSnippet: 13 });
+var Jae = { kernelName: ei, backendName: "webgpu", kernelFunc: Zae };
+function eie(e) {
+  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new C2(14, s.shape, r.shape);
   return n.runWebGPUProgram(a, [s, r], "float32");
 }
-var Vae = { kernelName: Ja, backendName: "webgpu", kernelFunc: Bae };
-function Wae(e) {
+var tie = { kernelName: ti, backendName: "webgpu", kernelFunc: eie };
+function nie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   return rc(r, a, i, "prod", n);
 }
-var Uae = { kernelName: Ao, backendName: "webgpu", kernelFunc: Wae };
-var Gae = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = Qne(s, r, a, i);
+var sie = { kernelName: ni, backendName: "webgpu", kernelFunc: nie };
+var rie = (e) => {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = ose(s, r, a, i);
   return t.makeTensorInfo([o.length], i, o);
 };
-var Hae = { kernelName: Il, backendName: "webgpu", kernelFunc: Gae };
-var z2 = mn({ opSnippet: 3 });
-var qae = { kernelName: Da, backendName: "webgpu", kernelFunc: z2 };
-var jae = Kt({ opType: 12 });
-var Kae = { kernelName: ei, backendName: "webgpu", kernelFunc: jae };
-var Xae = Kt({ opType: 13 });
-var Yae = { kernelName: ni, backendName: "webgpu", kernelFunc: Xae };
-var Qae = class {
+var aie = { kernelName: Il, backendName: "webgpu", kernelFunc: rie };
+var U2 = mn({ opSnippet: 3 });
+var iie = { kernelName: Oa, backendName: "webgpu", kernelFunc: U2 };
+var oie = Kt({ opType: 12 });
+var uie = { kernelName: si, backendName: "webgpu", kernelFunc: oie };
+var lie = Kt({ opType: 13 });
+var cie = { kernelName: ai, backendName: "webgpu", kernelFunc: lie };
+var die = class {
   constructor(e, t, n) {
-    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "resizeBilinear";
+    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "resizeBilinear";
   }
   getUserCode() {
     return `
@@ -35312,14 +35450,14 @@ var Qae = class {
     `;
   }
 };
-function Zae(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, size: i, halfPixelCenters: o } = s, [u, l] = i, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [o ? 0.5 : 0] }], f = new Qae(r.shape, u, l);
+function pie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, size: i, halfPixelCenters: o } = s, [u, l] = i, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [o ? 0.5 : 0] }], f = new die(r.shape, u, l);
   return n.runWebGPUProgram(f, [r], "float32", h);
 }
-var Jae = { kernelName: ti, backendName: "webgpu", kernelFunc: Zae };
-var eie = class {
+var hie = { kernelName: ri, backendName: "webgpu", kernelFunc: pie };
+var fie = class {
   constructor(e, t, n, s) {
-    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.halfPixelCenters = s, this.shaderKey = `resizeNearest_${s}`;
+    this.variableNames = ["x"], this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = [e[0], t, n, e[3]], this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.halfPixelCenters = s, this.shaderKey = `resizeNearest_${s}`;
   }
   getUserCode() {
     let e;
@@ -35357,14 +35495,14 @@ var eie = class {
     `;
   }
 };
-function tie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new eie(r.shape, u, l, i);
+function mie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new fie(r.shape, u, l, i);
   return n.runWebGPUProgram(f, [r], r.dtype, h);
 }
-var nie = { kernelName: Cl, backendName: "webgpu", kernelFunc: tie };
-var sie = class {
+var gie = { kernelName: Cl, backendName: "webgpu", kernelFunc: mie };
+var bie = class {
   constructor(e, t) {
-    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
+    this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`, this.shaderKey = "rotate", this.outputShape = e, typeof t == "number" ? (this.uniforms += " fillValue : f32,", this.fillSnippet = "var outputValue = uniforms.fillValue;", this.shaderKey += "_float") : (this.uniforms += " fillValue : vec3<f32>,", this.fillSnippet = "var outputValue = uniforms.fillValue[coords[3]];", this.shaderKey += "_vec3");
   }
   getUserCode() {
@@ -35392,15 +35530,15 @@ var sie = class {
       `;
   }
 };
-var rie = { kernelName: qo, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
-  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new sie(s.shape, a), [l, c] = S.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
+var yie = { kernelName: jo, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new bie(s.shape, a), [l, c] = S.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
   return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), o.runWebGPUProgram(u, [s], s.dtype, p);
 } };
-var aie = Kt({ opType: 15, cpuKernelImpl: Zne });
-var iie = { kernelName: si, backendName: "webgpu", kernelFunc: aie };
-var oie = class {
+var vie = Kt({ opType: 15, cpuKernelImpl: use });
+var xie = { kernelName: ii, backendName: "webgpu", kernelFunc: vie };
+var wie = class {
   constructor(e, t, n, s, r, a, i) {
-    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.dispatchLayout = Be(e), this.dispatch = _e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;
+    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.dispatchLayout = Le(e), this.dispatch = $e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;
     let o = Wt(r.length);
     this.uniforms = `sliceDim : i32, strides: ${o}, size: i32,`, this.updatesRank = s, this.indicesRank = n;
   }
@@ -35450,17 +35588,17 @@ var oie = class {
       }`;
   }
 };
-function uie(e) {
+function kie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = S.calculateShapes(a, r, i), d = [p / l, l];
   if (p === 0)
     return n.makeTensorInfo(i, r.dtype);
-  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = du({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new oie(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
+  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = pu({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new wie(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(x.dataId), k;
 }
-var lie = { kernelName: Fo, backendName: "webgpu", kernelFunc: uie };
-var cie = class {
+var Iie = { kernelName: Oo, backendName: "webgpu", kernelFunc: kie };
+var Sie = class {
   constructor(e, t, n) {
-    this.variableNames = ["c", "a", "b"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.cRank = e, this.rank = n, this.shaderKey = "select";
+    this.variableNames = ["c", "a", "b"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.cRank = e, this.rank = n, this.shaderKey = "select";
   }
   getUserCode() {
     let e, t;
@@ -35489,38 +35627,38 @@ var cie = class {
     `;
   }
 };
-function die(e) {
-  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new cie(s.shape.length, r.shape, r.shape.length);
+function Cie(e) {
+  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new Sie(s.shape.length, r.shape, r.shape.length);
   return n.runWebGPUProgram(i, [s, r, a], cn(r.dtype, a.dtype));
 }
-var pie = { kernelName: Oo, backendName: "webgpu", kernelFunc: die };
-var hie = Kt({ opType: 18 });
-var fie = { kernelName: ai, backendName: "webgpu", kernelFunc: hie };
-var mie = Kt({ opType: 16 });
-var gie = { kernelName: ri, backendName: "webgpu", kernelFunc: mie };
-var bie = Kt({ opType: 17 });
-var yie = { kernelName: zo, backendName: "webgpu", kernelFunc: bie };
-var M2 = mn({ opSnippet: 2, cpuKernelImpl: sse, supportsComplex: true });
-var vie = { kernelName: ci, backendName: "webgpu", kernelFunc: M2 };
-function xie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = F2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = S.expandShapeToKeepDim(o.shape, i), l = Me({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = M2({ inputs: { a: r, b: l }, backend: n }), p = A2({ inputs: { x: c }, backend: n }), d = Av({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = Me({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = z2({ inputs: { a: p, b: h }, backend: n });
+var Nie = { kernelName: Po, backendName: "webgpu", kernelFunc: Cie };
+var Tie = Kt({ opType: 18 });
+var $ie = { kernelName: ui, backendName: "webgpu", kernelFunc: Tie };
+var _ie = Kt({ opType: 16 });
+var Aie = { kernelName: oi, backendName: "webgpu", kernelFunc: _ie };
+var Eie = Kt({ opType: 17 });
+var Rie = { kernelName: Mo, backendName: "webgpu", kernelFunc: Eie };
+var G2 = mn({ opSnippet: 2, cpuKernelImpl: hse, supportsComplex: true });
+var Die = { kernelName: hi, backendName: "webgpu", kernelFunc: G2 };
+function Fie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = B2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = S.expandShapeToKeepDim(o.shape, i), l = Me({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = G2({ inputs: { a: r, b: l }, backend: n }), p = P2({ inputs: { x: c }, backend: n }), d = Rv({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = Me({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = U2({ inputs: { a: p, b: h }, backend: n });
   return n.disposeData(o.dataId), n.disposeData(l.dataId), n.disposeData(c.dataId), n.disposeData(p.dataId), n.disposeData(d.dataId), n.disposeData(h.dataId), f;
 }
-var wie = { kernelName: ui, backendName: "webgpu", kernelFunc: xie };
-var kie = (e) => {
+var Oie = { kernelName: di, backendName: "webgpu", kernelFunc: Fie };
+var Pie = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
   let o = a.reduce((b, y) => b * y), u = [[0, 0]];
   u.push(...i);
   for (let b = 1 + a.length; b < r.shape.length; ++b)
     u.push([0, 0]);
-  let l = [], c = P2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = S.getReshaped(c.shape, a, o, false), d = S.getPermuted(p.length, a.length, false), h = S.getReshapedPermuted(c.shape, a, o, false), f = Me({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = wi({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: h } });
+  let l = [], c = W2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = S.getReshaped(c.shape, a, o, false), d = S.getPermuted(p.length, a.length, false), h = S.getReshapedPermuted(c.shape, a, o, false), f = Me({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = Xs({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeData(b.dataId)), g;
 };
-var Iie = { kernelName: Mo, backendName: "webgpu", kernelFunc: kie };
-var Sie = class {
+var zie = { kernelName: Lo, backendName: "webgpu", kernelFunc: Pie };
+var Mie = class {
   constructor(e, t, n, s, r, a, i = true) {
-    this.variableNames = ["updates", "indices", "defaultValue"], this.workGroupSize = [64, 1, 1], this.workPerThread = 4, this.size = true, this.outputShape = a, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.variableNames = ["updates", "indices", "defaultValue"], this.workGroupSize = [64, 1, 1], this.workPerThread = 4, this.size = true, this.outputShape = a, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     let o = t > 1;
     this.shaderKey = `scatter_${n}_${s}_${o}`;
     let u = Wt(r.length);
@@ -35564,32 +35702,32 @@ var Sie = class {
       }`;
   }
 };
-function Cie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = S.calculateShapes(a, r, o), d = false, h = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: c }], f = new Sie(l, u, r.shape.length, a.shape.length, c, [p, 1], d), m = n.runWebGPUProgram(f, [a, r, i], a.dtype, h), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: o } });
+function Lie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = S.calculateShapes(a, r, o), d = false, h = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: c }], f = new Mie(l, u, r.shape.length, a.shape.length, c, [p, 1], d), m = n.runWebGPUProgram(f, [a, r, i], a.dtype, h), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: o } });
   return n.disposeData(m.dataId), g;
 }
-var Nie = { kernelName: ip, backendName: "webgpu", kernelFunc: Cie };
-function Tie(e) {
+var Bie = { kernelName: up, backendName: "webgpu", kernelFunc: Lie };
+function Vie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = S.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
   return u.map((d) => {
     let h = [...p];
     h[o] = d;
-    let f = cu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    let f = du({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
     return c[o] += d, f;
   });
 }
-var $ie = { kernelName: Lo, backendName: "webgpu", kernelFunc: Tie };
-var _ie = Kt({ opType: 19 });
-var Aie = { kernelName: ii, backendName: "webgpu", kernelFunc: _ie };
-var Eie = { kernelName: Al, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+var Wie = { kernelName: Bo, backendName: "webgpu", kernelFunc: Vie };
+var Uie = Kt({ opType: 19 });
+var Gie = { kernelName: li, backendName: "webgpu", kernelFunc: Uie };
+var Hie = { kernelName: Al, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
   let { x: n } = e, s = t, r = new nc(n.shape, 20);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
-var Rie = mn({ opSnippet: 11 });
-var Die = { kernelName: li, backendName: "webgpu", kernelFunc: Rie };
-var Fie = class {
+var qie = mn({ opSnippet: 11 });
+var jie = { kernelName: pi, backendName: "webgpu", kernelFunc: qie };
+var Kie = class {
   constructor(e) {
-    this.variableNames = ["x"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.variableNames = ["x"], this.workPerThread = 1, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     let t = Wt(this.outputShape.length);
     this.uniforms = `begin : ${t},  strides : ${t}, `, this.shaderKey = "stridedSlice";
   }
@@ -35611,41 +35749,41 @@ var Fie = class {
      `;
   }
 };
-function Oie(e) {
+function Xie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
   if (m)
     k = Me({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let T = wt.computeOutShape(y, v, x), N = cu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
+    let T = wt.computeOutShape(y, v, x), N = du({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
     k = Me({ inputs: { x: N }, backend: n, attrs: { shape: f } }), n.disposeData(N.dataId);
   } else if (n.shouldExecuteOnCPU([r])) {
-    let N = n.readSync(r.dataId), E = De(r.shape, r.dtype, N), A = tse(h, E, x, y);
+    let N = n.readSync(r.dataId), E = De(r.shape, r.dtype, N), A = dse(h, E, x, y);
     k = n.makeTensorInfo(f, r.dtype, A.values);
   } else {
-    let N = new Fie(h), E = [{ type: "int32", data: y }, { type: "int32", data: x }], A = n.runWebGPUProgram(N, [r], r.dtype, E);
+    let N = new Kie(h), E = [{ type: "int32", data: y }, { type: "int32", data: x }], A = n.runWebGPUProgram(N, [r], r.dtype, E);
     k = Me({ inputs: { x: A }, backend: n, attrs: { shape: f } }), n.disposeData(A.dataId);
   }
   return k;
 }
-var Pie = { kernelName: Bo, backendName: "webgpu", kernelFunc: Oie };
-function zie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = nse(d, h, r, a, i, o, u, l);
+var Yie = { kernelName: Vo, backendName: "webgpu", kernelFunc: Xie };
+function Qie(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = pse(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var Mie = { kernelName: op, backendName: "webgpu", kernelFunc: zie };
-var Lie = Kt({ opType: 21 });
-var Bie = { kernelName: di, backendName: "webgpu", kernelFunc: Lie };
-var Vie = class {
+var Zie = { kernelName: lp, backendName: "webgpu", kernelFunc: Qie };
+var Jie = Kt({ opType: 21 });
+var eoe = { kernelName: fi, backendName: "webgpu", kernelFunc: Jie };
+var toe = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.workGroupSize = [64, 1, 1], this.size = true;
     let n = new Array(e.length);
     for (let s = 0; s < n.length; s++)
       n[s] = e[s] * t[s];
-    this.outputShape = n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
+    this.outputShape = n, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.rank = this.outputShape.length, this.shaderKey = "tile";
   }
   getUserCode() {
-    let e = Wie(this.rank, "uniforms.");
+    let e = noe(this.rank, "uniforms.");
     return `
       ${Ue()}
         if (index < uniforms.size) {
@@ -35656,7 +35794,7 @@ var Vie = class {
     `;
   }
 };
-function Wie(e, t = "") {
+function noe(e, t = "") {
   if (e >= 5)
     throw Error(`Tile for rank ${e} is not yet supported`);
   if (e === 1)
@@ -35666,19 +35804,19 @@ function Wie(e, t = "") {
     s.push(`(${n[r]} % ${t}aShape[${r}])`);
   return s.join();
 }
-function Uie(e) {
+function soe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
   if (n.shouldExecuteOnCPU([r]) || r.dtype === "string" || r.shape.length >= 5) {
-    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = rse(c, a);
+    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = fse(c, a);
     return n.makeTensorInfo(p.shape, p.dtype, p.values);
   }
-  let i = new Vie(r.shape, a);
+  let i = new toe(r.shape, a);
   return n.runWebGPUProgram(i, [r], r.dtype);
 }
-var Gie = { kernelName: Cr, backendName: "webgpu", kernelFunc: Uie };
-var Hie = class {
+var roe = { kernelName: Nr, backendName: "webgpu", kernelFunc: soe };
+var aoe = class {
   constructor(e) {
-    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
+    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`, this.shaderKey = "swap";
   }
   getUserCode() {
@@ -35752,9 +35890,9 @@ var Hie = class {
       `;
   }
 };
-var qie = class {
+var ioe = class {
   constructor(e) {
-    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
+    this.variableNames = ["x", "indices"], this.workGroupSize = [256, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = "inputSize : i32, firstPass : i32, k : i32,", this.shaderKey = "merge";
   }
   getUserCode() {
     return `
@@ -35819,28 +35957,28 @@ var qie = class {
       `;
   }
 };
-function Vi(e, t) {
+function Ui(e, t) {
   t !== null && e.disposeData(t.dataId);
 }
-function Pw(e) {
+function Bw(e) {
   let t = 1;
   for (; t < e; )
     t *= 2;
   return t;
 }
-function jie(e) {
+function ooe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = r.shape, u = o[o.length - 1];
   if (n.shouldExecuteOnCPU([r])) {
-    let k = n.readSync(r.dataId), [T, N] = ase(k, o, r.dtype, a, i);
+    let k = n.readSync(r.dataId), [T, N] = mse(k, o, r.dtype, a, i);
     return [n.makeTensorInfo(T.shape, T.dtype, T.values), n.makeTensorInfo(N.shape, N.dtype, N.values)];
   }
   if (a === 0)
     return o[o.length - 1] = 0, [n.makeTensorInfo(o, r.dtype, []), n.makeTensorInfo(o, "int32", [])];
   if (u === 1)
-    return [r, du({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
-  let c = w.sizeFromShape(o) / u, p = Me({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Pw(a), h = Pw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, T, N) => {
-    let E = m(), A = new Hie(N), R = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [T] }], F = f;
-    f = n.runWebGPUProgram(A, E, "int32", R), Vi(n, F);
+    return [r, pu({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
+  let c = w.sizeFromShape(o) / u, p = Me({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Bw(a), h = Bw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, T, N) => {
+    let E = m(), A = new aoe(N), R = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [T] }], F = f;
+    f = n.runWebGPUProgram(A, E, "int32", R), Ui(n, F);
   };
   for (let k = 1; k < d; k *= 2) {
     let T = k * 2;
@@ -35848,25 +35986,25 @@ function jie(e) {
       g(T, N, [c, h]);
   }
   for (let k = h; k > d; k /= 2) {
-    let T = m(), N = new qie([c, k / 2]), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
-    f = n.runWebGPUProgram(N, T, "int32", A), Vi(n, P);
+    let T = m(), N = new ioe([c, k / 2]), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
+    f = n.runWebGPUProgram(N, T, "int32", A), Ui(n, P);
     let R = d / 2, F = R * 2;
     for (let $ = R; $ >= 1; $ /= 2)
       g(F, $, f.shape);
   }
   let b = f;
-  f = cu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Vi(n, b);
-  let y = D2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
-  Vi(n, p);
+  f = du({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Ui(n, b);
+  let y = L2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
+  Ui(n, p);
   let v = o.slice(0, -1);
-  v.push(a), b = f, f = Me({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Vi(n, b);
+  v.push(a), b = f, f = Me({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Ui(n, b);
   let x = y;
-  return y = Me({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Vi(n, x), [y, f];
+  return y = Me({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Ui(n, x), [y, f];
 }
-var Kie = { kernelName: Wo, backendName: "webgpu", kernelFunc: jie };
-var Xie = class {
+var uoe = { kernelName: Uo, backendName: "webgpu", kernelFunc: ooe };
+var loe = class {
   constructor(e) {
-    this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "transform";
+    this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "transform";
   }
   getUserCode() {
     return `
@@ -35988,8 +36126,8 @@ var Xie = class {
         `;
   }
 };
-function Yie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Xie(g), y = i === "nearest" ? 1 : 2, v;
+function coe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new loe(g), y = i === "nearest" ? 1 : 2, v;
   switch (o) {
     case "constant":
       v = 1;
@@ -36010,8 +36148,8 @@ function Yie(e) {
   let x = [{ type: "int32", data: [y] }, { type: "int32", data: [v] }, { type: "float32", data: [u] }];
   return n.runWebGPUProgram(b, [r, a], "float32", x);
 }
-var Qie = { kernelName: Uo, backendName: "webgpu", kernelFunc: Yie };
-function Zie(e) {
+var doe = { kernelName: Go, backendName: "webgpu", kernelFunc: coe };
+function poe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
   let i = r, o = i.shape.length, u = r.shape[a], l = new Array(o - 1), c = 0;
@@ -36022,16 +36160,16 @@ function Zie(e) {
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = cu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = du({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeData(m.dataId)), f;
 }
-var Jie = { kernelName: Go, backendName: "webgpu", kernelFunc: Zie };
-var eoe = [Cne, use, cse, hse, vse, wse, Ise, Cse, Ase, Fse, Pse, Bse, _ne, Gse, Zse, nre, rre, ire, lre, pre, fre, vre, wre, Ire, Sre, Cre, Tre, _re, Ere, zre, Dre, Ore, Bre, Wre, Gre, jre, Yre, Zre, eae, $ne, Wse, nae, rae, iae, uae, cae, pae, hae, mae, bae, vae, wae, Iae, Cae, mre, Tae, _ae, Eae, Ese, Dae, Oae, zae, Lae, Vae, Uae, Hae, Rse, qae, Kae, Yae, Ine, Jae, nie, rie, iie, lie, pie, fie, gie, yie, $se, Pie, Mie, wie, Iie, Nie, $ie, Aie, Eie, Die, vie, bre, Bie, Gie, Kie, Qie, bse, Jie, Rae];
-for (let e of eoe)
+var hoe = { kernelName: Ho, backendName: "webgpu", kernelFunc: poe };
+var foe = [One, yse, xse, Ise, _se, Ese, Dse, Ose, Bse, Gse, qse, Yse, Lne, ere, ure, pre, fre, gre, vre, wre, Ire, Nre, $re, Dre, Ore, zre, Mre, Lre, Vre, Ure, Hre, Qre, jre, Xre, eae, nae, rae, oae, cae, pae, fae, Mne, Zse, gae, yae, xae, kae, Sae, Nae, Tae, _ae, Eae, Dae, Oae, zae, Lae, _re, Vae, Uae, Hae, Vse, jae, Xae, Qae, Jae, tie, sie, aie, Wse, iie, uie, cie, Dne, hie, gie, yie, xie, Iie, Nie, $ie, Aie, Rie, Mse, Yie, Zie, Oie, zie, Bie, Wie, Gie, Hie, jie, Die, Ere, eoe, roe, uoe, doe, Tse, hoe, qae];
+for (let e of foe)
   El(e);
-var toe = class {
+var moe = class {
   constructor(e) {
     this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
@@ -36039,7 +36177,7 @@ var toe = class {
     return this.acquireBuffer(e, t, true);
   }
   acquireBuffer(e, t, n = false) {
-    let s = zw(e, t);
+    let s = Vw(e, t);
     if (this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.usedBuffers.has(s) || this.usedBuffers.set(s, []), this.numBytesUsed += e, this.numUsedBuffers++, this.freeBuffers.get(s).length > 0) {
       this.numFreeBuffers--;
       let a = this.freeBuffers.get(s).shift();
@@ -36052,7 +36190,7 @@ var toe = class {
   releaseBuffer(e, t, n) {
     if (this.freeBuffers.size === 0)
       return;
-    let s = zw(t, n);
+    let s = Vw(t, n);
     this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.freeBuffers.get(s).push(e), this.numFreeBuffers++, this.numUsedBuffers--;
     let r = this.usedBuffers.get(s), a = r.indexOf(e);
     if (a < 0)
@@ -36083,15 +36221,15 @@ var toe = class {
     }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
 };
-function zw(e, t) {
+function Vw(e, t) {
   return `${e}_${t}`;
 }
-var L2 = class {
+var H2 = class {
   constructor() {
     this.outputShape = [0], this.variableNames = [], this.workGroupSize = [256, 1, 1], this.lastUniformData = [], this.inputTexture = null, this.layout = null, this.lastPixelSize = { width: 0, height: 0 }, this.disposed = false, this.shaderKey = "fromPixels", this.useImport = false;
   }
   updateOutputShape(e) {
-    w.arraysEqual(this.outputShape, e) || (this.outputShape = e, this.workPerThread = e[2], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]));
+    w.arraysEqual(this.outputShape, e) || (this.outputShape = e, this.workPerThread = e[2], this.dispatchLayout = Le(this.outputShape), this.dispatch = $e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]));
   }
   makeFromPixelsSource() {
     let e = this.useImport ? "textureLoad(src, vec2<i32>(coords.yx));" : "textureLoad(src, vec2<i32>(coords.yx), 0)";
@@ -36140,7 +36278,7 @@ var L2 = class {
     return { bindGroupLayout: n, pipelineLayout: s };
   }
 };
-var noe = class extends L2 {
+var goe = class extends H2 {
   constructor() {
     super(...arguments), this.layout = null, this.useImport = true;
   }
@@ -36157,8 +36295,8 @@ var noe = class extends L2 {
     return { bindGroupLayout: n, pipelineLayout: s };
   }
 };
-var soe = X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
-var Mw = (e, t) => {
+var boe = X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
+var Ww = (e, t) => {
   let n = e.limits.maxComputeWorkgroupsPerDimension, s = t.dispatchLayout, r = t.dispatch;
   if (r.every((i) => i <= n))
     return r;
@@ -36166,14 +36304,14 @@ var Mw = (e, t) => {
   let a = Math.ceil(Math.sqrt(r[0]));
   return a > n ? (a = Math.ceil(Math.cbrt(r[0])), w.assert(a <= n, () => "Total dispatch size exceeds WebGPU maximum."), [a, a, a]) : [a, a, 1];
 };
-var B2 = class extends nl {
+var q2 = class extends sl {
   constructor(e, t = false) {
-    if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, !Tv())
+    if (super(), this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, !_v())
       throw new Error("WebGPU is not supported on this device");
-    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new toe(this.device), this.tensorMap = new Ud(this, Ss()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), X().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
+    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new moe(this.device), this.tensorMap = new Hd(this, Ss()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), X().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
   }
   nextDataId() {
-    return B2.nextDataId++;
+    return q2.nextDataId++;
   }
   floatPrecision() {
     return 32;
@@ -36228,13 +36366,13 @@ var B2 = class extends nl {
   write(e, t, n) {
     if (n === "complex64" && e != null)
       throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
-    let s = { id: this.nextDataId() }, r = w.sizeFromShape(t) * jm(n);
+    let s = { id: this.nextDataId() }, r = w.sizeFromShape(t) * Xm(n);
     return this.tensorMap.set(s, { dtype: n, values: e, bufferInfo: { byteSize: r, usage: this.defaultGpuBufferUsage() }, refCount: 1 }), s;
   }
   move(e, t, n, s, r) {
     if (s === "complex64")
       throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
-    let a = w.sizeFromShape(n) * jm(s);
+    let a = w.sizeFromShape(n) * Xm(s);
     this.tensorMap.set(e, { dtype: s, values: t, bufferInfo: { byteSize: a, usage: this.defaultGpuBufferUsage() }, refCount: r });
   }
   submitQueue() {
@@ -36246,9 +36384,9 @@ var B2 = class extends nl {
   getFromPixelsProgram(e) {
     switch (e) {
       case "copyExternal":
-        return this.fromPixelProgram || (this.fromPixelProgram = new L2()), this.fromPixelProgram;
+        return this.fromPixelProgram || (this.fromPixelProgram = new H2()), this.fromPixelProgram;
       case "import":
-        return this.fromPixelImportProgram || (this.fromPixelImportProgram = new noe()), this.fromPixelImportProgram;
+        return this.fromPixelImportProgram || (this.fromPixelImportProgram = new goe()), this.fromPixelImportProgram;
       default:
         w.assert(false, () => "Unsupported fromPixels shape");
         return;
@@ -36258,7 +36396,7 @@ var B2 = class extends nl {
     this.currentCommandEncoder || (this.currentCommandEncoder = this.device.createCommandEncoder());
   }
   ensureComputePassEnded() {
-    this.currentComputePass && (this.currentComputePass.endPass(), this.currentComputePass = null);
+    this.currentComputePass && (this.currentComputePass.end(), this.currentComputePass = null);
   }
   getComputePass() {
     return this.currentComputePass || (this.currentComputePass = this.currentCommandEncoder.beginComputePass()), this.currentComputePass;
@@ -36293,7 +36431,7 @@ var B2 = class extends nl {
       s = S.mergeRealAndImagArrays(a, i);
     } else {
       let r = await this.getBufferData(t);
-      s = b2(r, t.dtype);
+      s = k2(r, t.dtype);
     }
     return this.convertAndCacheOnCPU(e, s), s;
   }
@@ -36396,7 +36534,7 @@ var B2 = class extends nl {
       }
       this.uploadToGPU(r.dataId);
     }
-    e.dispatch = Mw(this.device, e);
+    e.dispatch = Ww(this.device, e);
     let a = [{ type: "float32", data: [NaN] }], i = t.concat(r).map((N) => N.shape), o = "int32";
     i.map((N) => {
       a.push({ type: o, data: N });
@@ -36411,7 +36549,7 @@ var B2 = class extends nl {
       if (N.dtype === "complex64")
         throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");
       return this.uploadToGPU(N.dataId), { dtype: this.tensorMap.get(N.dataId).dtype, shape: N.shape, name: e.variableNames[E] };
-    }), p = c.map((N) => N.dtype).concat(r.dtype), d = c.map((N) => S.getBroadcastDims(N.shape, r.shape)), h = c.map((N) => w.arraysEqual(N.shape, r.shape)).join("_"), f = d.map((N) => N.join("_")).join(";"), m = R2(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => E2(this.device, e, b, c, r)), v = this.activeTimers != null, x = Pre(this.device, g, t.map((N) => this.tensorToBinding(N)), this.tensorToBinding(r), l);
+    }), p = c.map((N) => N.dtype).concat(r.dtype), d = c.map((N) => S.getBroadcastDims(N.shape, r.shape)), h = c.map((N) => w.arraysEqual(N.shape, r.shape)).join("_"), f = d.map((N) => N.join("_")).join(";"), m = M2(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => z2(this.device, e, b, c, r)), v = this.activeTimers != null, x = Yre(this.device, g, t.map((N) => this.tensorToBinding(N)), this.tensorToBinding(r), l);
     this.ensureCommandEncoderReady();
     let k = this.getComputePass();
     v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 0), k.setPipeline(y), k.setBindGroup(0, x), k.dispatch(e.dispatch[0], e.dispatch[1], e.dispatch[2]), v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t.forEach((N) => {
@@ -36421,7 +36559,7 @@ var B2 = class extends nl {
     return this.uniformDisposalQueue.push(T), X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), v && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), r;
   }
   runFromPixelsProgram(e, t, n, s, r) {
-    e.dispatch = Mw(this.device, e);
+    e.dispatch = Ww(this.device, e);
     let a = this.device.createBindGroup({ layout: n.bindGroupLayout, entries: [{ binding: 0, resource: { buffer: t } }, { binding: 1, resource: s }, { binding: 2, resource: { buffer: e.uniform } }] });
     this.ensureCommandEncoderReady();
     let i = this.getComputePass(), o = this.activeTimers != null;
@@ -36433,7 +36571,7 @@ var B2 = class extends nl {
     let s = new BigUint64Array(n.getMappedRange()), r = Number(s[1] - s[0]);
     return n.unmap(), this.bufferManager.releaseBuffer(n, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), r / 1e6;
   }
-  shouldExecuteOnCPU(e, t = soe) {
+  shouldExecuteOnCPU(e, t = boe) {
     return X().getBool("WEBGPU_CPU_FORWARD") && e.every((n) => this.tensorMap.get(n.dataId).bufferInfo.buffer == null && w.sizeFromShape(n.shape) < t);
   }
   numDataIds() {
@@ -36443,24 +36581,24 @@ var B2 = class extends nl {
     this.disposed || (this.bufferManager.dispose(), this.fromPixelProgram && this.fromPixelProgram.dispose(), this.fromPixelImportProgram && this.fromPixelImportProgram.dispose(), this.disposed = true);
   }
 };
-var Ev = B2;
-Ev.nextDataId = 0;
-var roe = {};
-Ae(roe, { WebGPUBackend: () => Ev, webgpu_util: () => m2 });
-Tv() && pp("webgpu", async () => {
+var Dv = q2;
+Dv.nextDataId = 0;
+var yoe = {};
+Ae(yoe, { WebGPUBackend: () => Dv, webgpu_util: () => x2 });
+_v() && fp("webgpu", async () => {
   X().set("CHECK_COMPUTATION_FOR_ERRORS", false);
   let e = { powerPreference: X().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t = await navigator.gpu.requestAdapter(e), n = t.limits, s = {}, r = t.features.has("timestamp-query");
   s.requiredLimits = { maxComputeWorkgroupStorageSize: n.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: n.maxComputeWorkgroupsPerDimension }, r ? s.requiredFeatures = ["timestamp-query"] : console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");
   let a = await t.requestDevice(s);
-  return new Ev(a, r);
+  return new Dv(a, r);
 }, 3);
 var It = ((e) => (e[e.float32 = 0] = "float32", e[e.int32 = 1] = "int32", e[e.bool = 2] = "bool", e[e.string = 3] = "string", e[e.complex64 = 4] = "complex64", e))(It || {});
-var Jp = ((e) => (e[e.linear = 0] = "linear", e[e.relu = 1] = "relu", e[e.relu6 = 2] = "relu6", e[e.prelu = 3] = "prelu", e[e.leakyrelu = 4] = "leakyrelu", e[e.sigmoid = 5] = "sigmoid", e[e.elu = 6] = "elu", e))(Jp || {});
-var V2;
-function aoe(e) {
-  V2 = e.wasm.cwrap(ra, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var th = ((e) => (e[e.linear = 0] = "linear", e[e.relu = 1] = "relu", e[e.relu6 = 2] = "relu6", e[e.prelu = 3] = "prelu", e[e.leakyrelu = 4] = "leakyrelu", e[e.sigmoid = 5] = "sigmoid", e[e.elu = 6] = "elu", e))(th || {});
+var j2;
+function voe(e) {
+  j2 = e.wasm.cwrap(aa, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function ioe(e) {
+function xoe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t;
   if (r.dtype !== "float32" || a.dtype !== "float32")
     throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");
@@ -36471,13 +36609,13 @@ function ioe(e) {
       throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);
     f = E.id;
   }
-  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = Jp[c];
+  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = th[c];
   if (g == null)
     throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
-  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, T = new Uint8Array(new Int32Array(r.shape).buffer), N = new Uint8Array(new Int32Array(a.shape).buffer);
-  return V2(d, T, r.shape.length, h, N, a.shape.length, u, l, g, f, m, p || 0, k), x;
+  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = Ko.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, T = new Uint8Array(new Int32Array(r.shape).buffer), N = new Uint8Array(new Int32Array(a.shape).buffer);
+  return j2(d, T, r.shape.length, h, N, a.shape.length, u, l, g, f, m, p || 0, k), x;
 }
-var ooe = { kernelName: ra, backendName: "wasm", setupFunc: aoe, kernelFunc: ioe };
+var woe = { kernelName: aa, backendName: "wasm", setupFunc: voe, kernelFunc: xoe };
 function Xt(e, t) {
   let n;
   function s(a) {
@@ -36489,7 +36627,7 @@ function Xt(e, t) {
   }
   return { kernelName: e, backendName: "wasm", setupFunc: s, kernelFunc: r };
 }
-var uoe = Xt(io);
+var koe = Xt(uo);
 function gn(e, t, n) {
   let s;
   function r(i) {
@@ -36504,48 +36642,48 @@ function gn(e, t, n) {
   }
   return { kernelName: e, backendName: "wasm", setupFunc: r, kernelFunc: a };
 }
-var loe = true;
-var coe = gn(Ir, loe);
-var W2;
-function doe(e) {
-  W2 = e.wasm.cwrap(ka, null, ["array", "number", "number", "number"]);
+var Ioe = true;
+var Soe = gn(Sr, Ioe);
+var K2;
+function Coe(e) {
+  K2 = e.wasm.cwrap(Ia, null, ["array", "number", "number", "number"]);
 }
-function poe(e) {
+function Noe(e) {
   let { inputs: t, backend: n } = e, s = n.makeOutput(t[0].shape, t[0].dtype);
   if (w.sizeFromShape(s.shape) === 0)
     return s;
   let r = t.map((o) => n.dataIdMap.get(o.dataId).id), a = new Uint8Array(new Int32Array(r).buffer), i = n.dataIdMap.get(s.dataId).id;
-  return W2(a, r.length, It[s.dtype], i), s;
+  return K2(a, r.length, It[s.dtype], i), s;
 }
-var hoe = { kernelName: ka, backendName: "wasm", setupFunc: doe, kernelFunc: poe };
-function eh(e) {
+var Toe = { kernelName: Ia, backendName: "wasm", setupFunc: Coe, kernelFunc: Noe };
+function nh(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype), r = n.typedArrayFromHeap(t);
   return n.typedArrayFromHeap(s).set(r), s;
 }
-var foe = { kernelName: Ba, backendName: "wasm", kernelFunc: eh };
-var U2;
-function moe(e) {
-  U2 = e.wasm.cwrap(pi, null, ["number", "array", "number", "number", "number", "array", "number"]);
+var $oe = { kernelName: Wa, backendName: "wasm", kernelFunc: nh };
+var X2;
+function _oe(e) {
+  X2 = e.wasm.cwrap(mi, null, ["number", "array", "number", "number", "number", "array", "number"]);
 }
-function wr(e) {
-  let { inputs: t, backend: n, attrs: s } = e, [r, a] = boe(t.x.shape, s.perm), i = true;
+function kr(e) {
+  let { inputs: t, backend: n, attrs: s } = e, [r, a] = Eoe(t.x.shape, s.perm), i = true;
   for (let f = 0; f < a.length; f++)
     a[f] !== f && (i = false);
-  let o = goe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
+  let o = Aoe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
   if (i) {
-    let f = eh({ inputs: t, backend: n });
+    let f = nh({ inputs: t, backend: n });
     return f.shape = o, f;
   }
   let l = n.makeOutput(o, u.dtype), c = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(l.dataId).id, d = new Uint8Array(new Int32Array(a).buffer), h = new Uint8Array(new Int32Array(u.shape).buffer);
-  return U2(c, h, u.shape.length, It[u.dtype], p, d, a.length), l;
+  return X2(c, h, u.shape.length, It[u.dtype], p, d, a.length), l;
 }
-function goe(e, t) {
+function Aoe(e, t) {
   let n = new Array(e.length);
   for (let s = 0; s < n.length; s++)
     n[s] = e[t[s]];
   return n;
 }
-function boe(e, t) {
+function Eoe(e, t) {
   let n = [], s = [];
   for (let r = 0; r < e.length; ++r)
     e[r] !== 1 && n.push(e[r]), e[t[r]] !== 1 && s.push(t[r]);
@@ -36557,25 +36695,25 @@ function boe(e, t) {
   }
   return [n, s];
 }
-var yoe = { kernelName: pi, backendName: "wasm", kernelFunc: wr, setupFunc: moe };
-function Fr(e, t, n) {
+var Roe = { kernelName: mi, backendName: "wasm", kernelFunc: kr, setupFunc: _oe };
+function Or(e, t, n) {
   let s = e.shape, r = e.shape.length, a = w.parseAxisParam(t, s), i = a, o = S.getAxesPermutation(i, r), u = null, l = false;
   if (o != null) {
     let c = new Array(r);
     for (let h = 0; h < c.length; h++)
       c[h] = s[o[h]];
-    i = S.getInnerMostAxes(i.length, r), u = wr({ inputs: { x: e }, attrs: { perm: o }, backend: n });
+    i = S.getInnerMostAxes(i.length, r), u = kr({ inputs: { x: e }, attrs: { perm: o }, backend: n });
     let p = n.dataIdMap.get(e.dataId).id;
     n.dataIdMap.get(u.dataId).id !== p && (l = true);
   }
   return { transposed: u, originalAxes: a, axes: i, inputWasTransposed: l };
 }
-var G2;
-function voe(e) {
-  G2 = e.wasm.cwrap(al, null, ["number, number, number"]);
+var Y2;
+function Doe(e) {
+  Y2 = e.wasm.cwrap(il, null, ["number, number, number"]);
 }
-function xoe(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
+function Foe(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     l = c, u = v;
@@ -36585,7 +36723,7 @@ function xoe(e) {
   let [m, g] = S.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    G2(u, b, v);
+    Y2(u, b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = S.expandShapeToKeepDim(y.shape, d);
@@ -36593,13 +36731,13 @@ function xoe(e) {
   }
   return y;
 }
-var woe = { kernelName: al, backendName: "wasm", setupFunc: voe, kernelFunc: xoe };
-var H2;
-function koe(e) {
-  H2 = e.wasm.cwrap(il, null, ["number, number, number"]);
+var Ooe = { kernelName: il, backendName: "wasm", setupFunc: Doe, kernelFunc: Foe };
+var Q2;
+function Poe(e) {
+  Q2 = e.wasm.cwrap(ol, null, ["number, number, number"]);
 }
-function Ioe(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
+function zoe(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     l = c, u = v;
@@ -36609,7 +36747,7 @@ function Ioe(e) {
   let [m, g] = S.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    H2(u, b, v);
+    Q2(u, b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = S.expandShapeToKeepDim(y.shape, d);
@@ -36617,85 +36755,85 @@ function Ioe(e) {
   }
   return y;
 }
-var Soe = { kernelName: il, backendName: "wasm", setupFunc: koe, kernelFunc: Ioe };
-var q2;
-function Coe(e) {
-  q2 = e.wasm.cwrap(Ia, null, ["number", "number", "number", "number", "number"]);
+var Moe = { kernelName: ol, backendName: "wasm", setupFunc: Poe, kernelFunc: zoe };
+var Z2;
+function Loe(e) {
+  Z2 = e.wasm.cwrap(Sa, null, ["number", "number", "number", "number", "number"]);
 }
-function Noe(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = i, u = a, { transposed: l, axes: c, inputWasTransposed: p } = Fr(a, r, t);
+function Boe(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = i, u = a, { transposed: l, axes: c, inputWasTransposed: p } = Or(a, r, t);
   if (p) {
     let b = t.dataIdMap.get(l.dataId).id;
     b !== i && (u = l, o = b);
   }
   let d = u.shape.slice(0, -1), h = t.makeOutput(d, "int32"), f = t.dataIdMap.get(h.dataId).id, m = w.sizeFromShape(h.shape), g = u.shape[c[0]];
-  return q2(o, It[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
+  return Z2(o, It[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
 }
-var Toe = { kernelName: Ia, backendName: "wasm", kernelFunc: Noe, setupFunc: Coe };
-var j2;
-function $oe(e) {
-  j2 = e.wasm.cwrap(Sa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Voe = { kernelName: Sa, backendName: "wasm", kernelFunc: Boe, setupFunc: Loe };
+var J2;
+function Woe(e) {
+  J2 = e.wasm.cwrap(Ca, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function _oe(e) {
+function Uoe(e) {
   let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = S.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.strideHeight, y = c.strideWidth, v = c.inChannels;
   if (c.dataFormat !== "channelsLast")
     throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
   if (c.dilationWidth !== 1 || c.dilationHeight !== 1)
     throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);
   let x = s.makeOutput(c.outShape, "float32"), k = s.dataIdMap.get(x.dataId).id;
-  return j2(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
+  return J2(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
 }
-var Aoe = { kernelName: Sa, backendName: "wasm", setupFunc: $oe, kernelFunc: _oe };
+var Goe = { kernelName: Ca, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe };
 function yn(e) {
   let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a);
   return w.assert(a === w.sizeFromShape(i), () => `new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
 }
-var Eoe = { kernelName: Eo, backendName: "wasm", kernelFunc: yn };
-var K2;
-function Roe(e) {
-  K2 = e.wasm.cwrap(Ca, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
+var Hoe = { kernelName: Ro, backendName: "wasm", kernelFunc: yn };
+var eN;
+function qoe(e) {
+  eN = e.wasm.cwrap(Na, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
 }
-function Doe(e) {
+function joe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
   if (r.dtype !== "float32" || a.dtype !== "float32")
     throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = Ko.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
   w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
   let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], T = yn({ inputs: { x: r }, backend: n, attrs: { shape: x } }), N = yn({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = n.dataIdMap.get(T.dataId).id, A = n.dataIdMap.get(N.dataId).id, P = i ? T.shape[2] : T.shape[1], R = o ? N.shape[1] : N.shape[2], F = Math.max(g, b), $ = n.makeOutput([F, P, R], T.dtype), z = n.dataIdMap.get($.dataId).id, W = new Uint8Array(new Int32Array(T.shape).buffer), q = new Uint8Array(new Int32Array(N.shape).buffer);
-  return K2(E, W, T.shape.length, A, q, N.shape.length, i, o, z), n.disposeData(T.dataId), n.disposeData(N.dataId), $.shape = v, $;
+  return eN(E, W, T.shape.length, A, q, N.shape.length, i, o, z), n.disposeData(T.dataId), n.disposeData(N.dataId), $.shape = v, $;
 }
-var Foe = { kernelName: Ca, backendName: "wasm", setupFunc: Roe, kernelFunc: Doe };
-function va(e) {
+var Koe = { kernelName: Na, backendName: "wasm", setupFunc: qoe, kernelFunc: joe };
+function xa(e) {
   let { inputs: { x: t }, attrs: { begin: n, size: s }, backend: r } = e, [a, i] = wt.parseSliceParams(t, n, s), o = wt.isSliceContinous(t.shape, a, i), u = r.readSync(t.dataId), l = r.makeOutput(i, t.dtype), c = w.computeStrides(t.shape), p = r.dataIdMap.get(l.dataId);
   if (o) {
     let f = wt.computeFlatOffset(a, c);
     return t.dtype === "string" ? p.stringBytes = u.slice(f, f + w.sizeFromShape(i)) : r.typedArrayFromHeap(l).set(u.subarray(f, f + w.sizeFromShape(i))), l;
   }
   if (t.dtype === "string") {
-    let f = Fd(u, a, i, t.shape, t.dtype);
+    let f = Pd(u, a, i, t.shape, t.dtype);
     return p.stringBytes = f, l;
   }
   let d = r.typedArrayFromHeap(l), h = t.shape.length;
   if (h === 2)
-    Ooe(u, c[0], d, a, i);
+    Xoe(u, c[0], d, a, i);
   else if (h === 3)
-    Poe(u, c[0], c[1], d, a, i);
+    Yoe(u, c[0], c[1], d, a, i);
   else if (h === 4)
-    zoe(u, c[0], c[1], c[2], d, a, i);
+    Qoe(u, c[0], c[1], c[2], d, a, i);
   else {
-    let f = Fd(u, a, i, t.shape, t.dtype);
+    let f = Pd(u, a, i, t.shape, t.dtype);
     d.set(f);
   }
   return l;
 }
-function Ooe(e, t, n, s, r) {
+function Xoe(e, t, n, s, r) {
   let a = 0, i = s[0], o = s[1], u = i + r[0];
   for (let l = i; l < u; l++) {
     let c = l * t + o;
     n.set(e.subarray(c, c + r[1]), a), a += r[1];
   }
 }
-function Poe(e, t, n, s, r, a) {
+function Yoe(e, t, n, s, r, a) {
   let i = 0, o = r[0], u = r[1], l = r[2], c = o + a[0], p = u + a[1];
   for (let d = o; d < c; d++)
     for (let h = u; h < p; h++) {
@@ -36703,7 +36841,7 @@ function Poe(e, t, n, s, r, a) {
       s.set(e.subarray(f, f + a[2]), i), i += a[2];
     }
 }
-function zoe(e, t, n, s, r, a, i) {
+function Qoe(e, t, n, s, r, a, i) {
   let o = 0, u = a[0], l = a[1], c = a[2], p = u + i[0], d = l + i[1], h = c + i[2], f = a[3];
   for (let m = u; m < p; m++)
     for (let g = l; g < d; g++)
@@ -36712,31 +36850,31 @@ function zoe(e, t, n, s, r, a, i) {
         r.set(e.subarray(y, y + i[3]), o), o += i[3];
       }
 }
-var Moe = { kernelName: Po, backendName: "wasm", kernelFunc: va };
-function Loe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s, o = a.reduce((b, y) => b * y), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = yn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = wr({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = yn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = va({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
+var Zoe = { kernelName: zo, backendName: "wasm", kernelFunc: xa };
+function Joe(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s, o = a.reduce((b, y) => b * y), u = S.getReshaped(r.shape, a, o), l = S.getPermuted(u.length, a.length), c = S.getReshapedPermuted(r.shape, a, o), p = S.getSliceBeginCoords(i, a.length), d = S.getSliceSize(c, i, a.length), h = yn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = kr({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = yn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = xa({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(h.dataId), g;
 }
-var Boe = { kernelName: oo, backendName: "wasm", kernelFunc: Loe };
+var eue = { kernelName: lo, backendName: "wasm", kernelFunc: Joe };
 function ac(e) {
   let { inputs: { x: t }, attrs: { dtype: n }, backend: s } = e, r = s.makeOutput(t.shape, n), a = s.typedArrayFromHeap(t);
   return s.typedArrayFromHeap(r).set(a), r;
 }
-var Voe = { kernelName: Na, backendName: "wasm", kernelFunc: ac };
-var Woe = Xt(Ta);
-var X2;
-function Uoe(e) {
-  X2 = e.wasm.cwrap(Sr, null, ["number", "number", "number", "number"]);
+var tue = { kernelName: Ta, backendName: "wasm", kernelFunc: ac };
+var nue = Xt($a);
+var tN;
+function sue(e) {
+  tN = e.wasm.cwrap(Cr, null, ["number", "number", "number", "number"]);
 }
-function Goe(e) {
+function rue(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o = n.dataIdMap.get(r.dataId).id, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(u.dataId).id;
-  return X2(o, a, i, l), u;
+  return tN(o, a, i, l), u;
 }
-var Hoe = { kernelName: Sr, backendName: "wasm", setupFunc: Uoe, kernelFunc: Goe };
-function Y2(e) {
+var aue = { kernelName: Cr, backendName: "wasm", setupFunc: sue, kernelFunc: rue };
+function nN(e) {
   let { inputs: t, backend: n } = e, s = w.parseAxisParam(e.attrs.axis, t[0].shape)[0], r = S.computeOutShape(t.map((h) => h.shape), s), a = t.filter((h) => w.sizeFromShape(h.shape) > 0);
   if (a.length === 1)
-    return eh({ inputs: { x: a[0] }, backend: n });
+    return nh({ inputs: { x: a[0] }, backend: n });
   let i = n.makeOutput(r, t[0].dtype);
   if (w.sizeFromShape(r) === 0)
     return i;
@@ -36747,7 +36885,7 @@ function Y2(e) {
       return yn({ inputs: { x: v }, backend: n, attrs: { shape: [-1, x] } });
     }), f = h.map((v) => ({ vals: n.readSync(v.dataId), shape: v.shape }));
     r = S.computeOutShape(h.map((v) => v.shape), 1);
-    let m = h[0].shape[0] === 1, g = tv(f, r, t[0].dtype, m), b = S.computeOutShape(a.map((v) => v.shape), s);
+    let m = h[0].shape[0] === 1, g = sv(f, r, t[0].dtype, m), b = S.computeOutShape(a.map((v) => v.shape), s);
     i.shape = b;
     let y = n.dataIdMap.get(i.dataId);
     return y.stringBytes = S.fromStringArrayToUint8(g), h.forEach((v) => n.disposeData(v.dataId)), i;
@@ -36765,149 +36903,149 @@ function Y2(e) {
   }
   return i;
 }
-var qoe = { kernelName: uo, backendName: "wasm", kernelFunc: Y2 };
-var Q2;
-function joe(e) {
-  Q2 = e.wasm.cwrap($a, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var iue = { kernelName: co, backendName: "wasm", kernelFunc: nN };
+var sN;
+function oue(e) {
+  sN = e.wasm.cwrap(_a, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Koe(e) {
+function uue(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p, dataFormat: d } = n, h = S.convertConv2DDataFormat(d), f = S.computeConv2DInfo(r.shape, a.shape, u, l, c, p, false, h), m = f.filterHeight, g = f.filterWidth, b = f.padInfo.top, y = f.padInfo.right, v = f.padInfo.bottom, x = f.padInfo.left, k = f.dilationHeight, T = f.dilationWidth, N = f.strideHeight, E = f.strideWidth, A = f.inChannels, P = f.outChannels, R = f.padInfo.type === "SAME" ? 1 : 0;
   if (f.dataFormat !== "channelsLast")
     throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);
   let F = s.makeOutput(f.outShape, "float32"), $ = s.dataIdMap.get(F.dataId).id;
-  return Q2(i, r.shape[0], r.shape[1], r.shape[2], o, m, g, b, y, v, x, R, k, T, N, E, A, P, $), F;
+  return sN(i, r.shape[0], r.shape[1], r.shape[2], o, m, g, b, y, v, x, R, k, T, N, E, A, P, $), F;
 }
-var Xoe = { kernelName: $a, backendName: "wasm", setupFunc: joe, kernelFunc: Koe };
-var Z2;
-function Yoe(e) {
-  Z2 = e.wasm.cwrap(_a, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var lue = { kernelName: _a, backendName: "wasm", setupFunc: oue, kernelFunc: uue };
+var rN;
+function cue(e) {
+  rN = e.wasm.cwrap(Aa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Qoe(e) {
+function due(e) {
   let { backend: t, inputs: n, attrs: s } = e, { dy: r, filter: a } = n, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, inputShape: c } = s, p = 1, d = S.convertConv2DDataFormat(u), h = S.computeConv2DInfo(c, a.shape, i, p, o, l, false, d), { batchSize: f, filterHeight: m, filterWidth: g, inChannels: b, inHeight: y, inWidth: v, outChannels: x, outHeight: k, outWidth: T, strideHeight: N, strideWidth: E } = h, A = m - 1 - h.padInfo.top, P = g - 1 - h.padInfo.left, R = h.dataFormat === "channelsLast", F = w.computeStrides(h.inShape), $ = w.computeStrides(r.shape), [z, W, q] = w.computeStrides(a.shape), K = F[0], Y = R ? F[1] : F[2], Z = R ? F[2] : 1, te = R ? 1 : F[1], ee = $[0], se = R ? $[1] : $[2], ne = R ? $[2] : 1, oe = R ? 1 : $[1], re = t.makeOutput(h.inShape, "float32"), le = t.dataIdMap.get(re.dataId).id, me = t.dataIdMap.get(r.dataId).id, ke = t.dataIdMap.get(a.dataId).id;
-  return Z2(me, ke, f, m, g, y, v, b, k, T, x, N, E, A, P, z, W, q, K, Y, Z, te, ee, se, ne, oe, le), re;
+  return rN(me, ke, f, m, g, y, v, b, k, T, x, N, E, A, P, z, W, q, K, Y, Z, te, ee, se, ne, oe, le), re;
 }
-var Zoe = { kernelName: _a, backendName: "wasm", setupFunc: Yoe, kernelFunc: Qoe };
-var Joe = Xt(Aa);
-var eue = Xt(Ea);
-var J2 = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(J2 || {});
-var eN;
-function tue(e) {
-  eN = e.wasm.cwrap(co, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+var pue = { kernelName: Aa, backendName: "wasm", setupFunc: cue, kernelFunc: due };
+var hue = Xt(Ea);
+var fue = Xt(Ra);
+var aN = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(aN || {});
+var iN;
+function mue(e) {
+  iN = e.wasm.cwrap(ho, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
 }
-function nue(e) {
+function gue(e) {
   let { backend: t, inputs: n, attrs: s } = e, { method: r, extrapolationValue: a, cropSize: i } = s, { image: o, boxes: u, boxInd: l } = n, c = u.shape[0], [p, d] = i, h = [c, p, d, o.shape[3]], f = t.dataIdMap.get(o.dataId), m;
   o.dtype !== "float32" && (m = ac({ backend: t, inputs: { x: o }, attrs: { dtype: "float32" } }), f = t.dataIdMap.get(m.dataId));
   let g = f.id, b = t.dataIdMap.get(u.dataId).id, y = t.dataIdMap.get(l.dataId).id, v = t.makeOutput(h, "float32"), x = t.dataIdMap.get(v.dataId).id, k = new Uint8Array(new Int32Array(o.shape).buffer);
-  return eN(g, b, y, c, k, p, d, J2[r], a, x), m != null && t.disposeData(m.dataId), v;
+  return iN(g, b, y, c, k, p, d, aN[r], a, x), m != null && t.disposeData(m.dataId), v;
 }
-var sue = { kernelName: co, backendName: "wasm", setupFunc: tue, kernelFunc: nue };
-var tN;
-function rue(e) {
-  tN = e.wasm.cwrap(hl, null, ["number", "number", "number", "number", "number", "number"]);
+var bue = { kernelName: ho, backendName: "wasm", setupFunc: mue, kernelFunc: gue };
+var oN;
+function yue(e) {
+  oN = e.wasm.cwrap(po, null, ["number", "number", "number", "number", "number", "number"]);
 }
-function aue(e) {
+function vue(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
   w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumprod does not support ${r.dtype} tensors in the WASM backend`);
   let l = S.getAxesPermutation([a], u), c = r;
-  l !== null && (c = wr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
+  l !== null && (c = kr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
   let p = S.getInnerMostAxes(1, u)[0];
   S.assertAxesAreInnerMostDims("cumprod", [p], u);
   let d = n.makeOutput(c.shape, c.dtype), h = c.shape[p], f = n.dataIdMap.get(c.dataId).id, m = n.dataIdMap.get(d.dataId).id;
-  tN(f, i ? 1 : 0, o ? 1 : 0, h, m, It[r.dtype]);
+  oN(f, i ? 1 : 0, o ? 1 : 0, h, m, It[r.dtype]);
   let g = d;
   if (l !== null) {
     let b = S.getUndoAxesPermutation(l);
-    g = wr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
+    g = kr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
   }
   return g;
 }
-var iue = { kernelName: hl, backendName: "wasm", setupFunc: rue, kernelFunc: aue };
-var nN;
-function oue(e) {
-  nN = e.wasm.cwrap(lo, null, ["number", "number", "number", "number", "number", "number"]);
+var xue = { kernelName: po, backendName: "wasm", setupFunc: yue, kernelFunc: vue };
+var uN;
+function wue(e) {
+  uN = e.wasm.cwrap(Da, null, ["number", "number", "number", "number", "number", "number"]);
 }
-function uue(e) {
+function kue(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
   w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumsum does not support ${r.dtype} tensors in the WASM backend`);
   let l = S.getAxesPermutation([a], u), c = r;
-  l !== null && (c = wr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
+  l !== null && (c = kr({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
   let p = S.getInnerMostAxes(1, u)[0];
   S.assertAxesAreInnerMostDims("cumsum", [p], u);
   let d = n.makeOutput(c.shape, c.dtype), h = c.shape[p], f = n.dataIdMap.get(c.dataId).id, m = n.dataIdMap.get(d.dataId).id;
-  nN(f, i ? 1 : 0, o ? 1 : 0, h, m, It[r.dtype]);
+  uN(f, i ? 1 : 0, o ? 1 : 0, h, m, It[r.dtype]);
   let g = d;
   if (l !== null) {
     let b = S.getUndoAxesPermutation(l);
-    g = wr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
+    g = kr({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
   }
   return g;
 }
-var lue = { kernelName: lo, backendName: "wasm", setupFunc: oue, kernelFunc: uue };
-var sN;
-function cue(e) {
-  sN = e.wasm.cwrap(po, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
+var Iue = { kernelName: Da, backendName: "wasm", setupFunc: wue, kernelFunc: kue };
+var lN;
+function Sue(e) {
+  lN = e.wasm.cwrap(fo, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
 }
-function due(e) {
+function Cue(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = t.makeOutput(f, "float32"), b = t.dataIdMap.get(r.dataId).id, y = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), v = new Uint8Array(new Int32Array(f).buffer), x = new Uint8Array(new Int32Array(w.computeStrides(f)).buffer), k = t.dataIdMap.get(m.dataId).id;
-  return sN(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
+  return lN(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
 }
-var pue = { kernelName: po, backendName: "wasm", setupFunc: cue, kernelFunc: due };
-var rN;
-function hue(e) {
-  rN = e.wasm.cwrap(Ra, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Nue = { kernelName: fo, backendName: "wasm", setupFunc: Sue, kernelFunc: Cue };
+var cN;
+function Tue(e) {
+  cN = e.wasm.cwrap(Fa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function fue(e) {
+function $ue(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a } = t, i = s.dataIdMap.get(r.dataId).id, o = s.dataIdMap.get(a.dataId).id, { strides: u, dilations: l, pad: c, dimRoundingMode: p } = n, d = l == null ? [1, 1] : l, h = S.computeConv2DInfo(r.shape, a.shape, u, d, c, p, true), f = h.filterHeight, m = h.filterWidth, g = h.padInfo.top, b = h.padInfo.right, y = h.padInfo.bottom, v = h.padInfo.left, x = h.dilationHeight, k = h.dilationWidth, T = h.strideHeight, N = h.strideWidth, E = h.inChannels, A = h.outChannels, P = h.padInfo.type === "SAME" ? 1 : 0;
   if (h.dataFormat !== "channelsLast")
     throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);
   let R = s.makeOutput(h.outShape, "float32"), F = s.dataIdMap.get(R.dataId).id;
-  return rN(i, r.shape[0], r.shape[1], r.shape[2], o, f, m, g, b, y, v, P, x, k, T, N, E, A, F), R;
+  return cN(i, r.shape[0], r.shape[1], r.shape[2], o, f, m, g, b, y, v, P, x, k, T, N, E, A, F), R;
 }
-var mue = { kernelName: Ra, backendName: "wasm", setupFunc: hue, kernelFunc: fue };
-var gue = Xt(Fa);
-var bue = false;
-var yue = gn(ho, bue, "bool");
-var vue = Xt(Oa, "float32");
-function Zm(e) {
+var _ue = { kernelName: Fa, backendName: "wasm", setupFunc: Tue, kernelFunc: $ue };
+var Aue = Xt(Pa);
+var Eue = false;
+var Rue = gn(mo, Eue, "bool");
+var Due = Xt(za, "float32");
+function eg(e) {
   let { inputs: t, attrs: n, backend: s } = e, { input: r } = t, { dim: a } = n, i = r.shape.length, o = r.shape.slice(), u = a;
   return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), yn({ inputs: { x: r }, backend: s, attrs: { shape: o } });
 }
-var xue = { kernelName: fo, backendName: "wasm", kernelFunc: Zm };
-function aN(e) {
+var Fue = { kernelName: go, backendName: "wasm", kernelFunc: eg };
+function dN(e) {
   let { attrs: { shape: t, value: n, dtype: s }, backend: r } = e, a = r.makeOutput(t, s);
   return r.typedArrayFromHeap(a).fill(n), a;
 }
-var wue = { kernelName: ml, backendName: "wasm", kernelFunc: aN };
-var iN;
-function kue(e) {
-  iN = e.wasm.cwrap(go, null, ["number", "number", "number", "number", "number", "number"]);
+var Oue = { kernelName: ml, backendName: "wasm", kernelFunc: dN };
+var pN;
+function Pue(e) {
+  pN = e.wasm.cwrap(yo, null, ["number", "number", "number", "number", "number", "number"]);
 }
-function Iue(e) {
+function zue(e) {
   let { inputs: t, backend: n } = e, { image: s } = t, r = n.makeOutput(s.shape, s.dtype), a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, [o, u, l, c] = s.shape;
-  return iN(a, o, u, l, c, i), r;
+  return pN(a, o, u, l, c, i), r;
 }
-var Sue = { kernelName: go, backendName: "wasm", kernelFunc: Iue, setupFunc: kue };
-var Cue = Xt(Pa);
-var Nue = false;
-var Tue = gn(za, Nue);
-var oN;
-function $ue(e) {
-  oN = e.wasm.cwrap(Ma, null, ["number", "number", "number", "number", "number", "number", "number"]);
+var Mue = { kernelName: yo, backendName: "wasm", kernelFunc: zue, setupFunc: Pue };
+var Lue = Xt(Ma);
+var Bue = false;
+var Vue = gn(La, Bue);
+var hN;
+function Wue(e) {
+  hN = e.wasm.cwrap(Ba, null, ["number", "number", "number", "number", "number", "number", "number"]);
 }
-function _ue(e) {
+function Uue(e) {
   let { backend: t, inputs: n, attrs: s } = e, { varianceEpsilon: r } = s, { x: a, mean: i, variance: o, offset: u, scale: l } = n, c = t.dataIdMap.get(a.dataId).id, p = t.dataIdMap.get(i.dataId).id, d = t.dataIdMap.get(o.dataId).id, h = u != null ? t.dataIdMap.get(u.dataId).id : 0, f = l != null ? t.dataIdMap.get(l.dataId).id : 0, m = t.makeOutput(a.shape, a.dtype);
   if (w.sizeFromShape(a.shape) === 0)
     return m;
   let g = t.dataIdMap.get(m.dataId).id;
-  return oN(c, p, d, h, f, r, g), m;
+  return hN(c, p, d, h, f, r, g), m;
 }
-var Aue = { kernelName: Ma, backendName: "wasm", setupFunc: $ue, kernelFunc: _ue };
-var uN;
-function Eue(e) {
-  uN = e.wasm.cwrap(aa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Gue = { kernelName: Ba, backendName: "wasm", setupFunc: Wue, kernelFunc: Uue };
+var fN;
+function Hue(e) {
+  fN = e.wasm.cwrap(ia, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Rue(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = S.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = Jp[h];
+function que(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = S.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = th[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);
   let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
@@ -36923,15 +37061,15 @@ function Rue(e) {
   if (p !== "NHWC")
     throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);
   let te = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
-  return uN(b, K, Y, Z, y, k, T, x, N, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
+  return fN(b, K, Y, Z, y, k, T, x, N, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
 }
-var Due = { kernelName: aa, backendName: "wasm", setupFunc: Eue, kernelFunc: Rue };
-var lN;
-function Fue(e) {
-  lN = e.wasm.cwrap(ia, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var jue = { kernelName: ia, backendName: "wasm", setupFunc: Hue, kernelFunc: que };
+var mN;
+function Kue(e) {
+  mN = e.wasm.cwrap(oa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Oue(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = S.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = Jp[h];
+function Xue(e) {
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = S.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = th[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
   let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
@@ -36947,26 +37085,26 @@ function Oue(e) {
   if (p !== "NHWC")
     throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);
   let te = s.makeOutput(m.outShape, "float32"), ee = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
-  return lN(b, K, Y, Z, y, k, T, x, N, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
+  return mN(b, K, Y, Z, y, k, T, x, N, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
 }
-var Pue = { kernelName: ia, backendName: "wasm", setupFunc: Fue, kernelFunc: Oue };
-var cN;
-function zue(e) {
-  cN = e.wasm.cwrap(yo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
+var Yue = { kernelName: oa, backendName: "wasm", setupFunc: Kue, kernelFunc: Xue };
+var gN;
+function Que(e) {
+  gN = e.wasm.cwrap(xo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
 }
-function Mue(e) {
-  let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = $k.prepareAndValidate(s, r), l = t.makeOutput(a, s.dtype);
+function Zue(e) {
+  let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = Rk.prepareAndValidate(s, r), l = t.makeOutput(a, s.dtype);
   if (i === 0)
     return l;
   let c = r.shape, p = c[c.length - 1], h = t.dataIdMap.get(s.dataId).id, m = t.dataIdMap.get(r.dataId).id, g = new Uint8Array(new Int32Array(u).buffer), b = t.dataIdMap.get(l.dataId).id;
-  return cN(h, It[s.dtype], m, i, p, o, g, b), l;
+  return gN(h, It[s.dtype], m, i, p, o, g, b), l;
 }
-var Lue = { kernelName: yo, backendName: "wasm", setupFunc: zue, kernelFunc: Mue };
-var dN;
-function Bue(e) {
-  dN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
+var Jue = { kernelName: xo, backendName: "wasm", setupFunc: Que, kernelFunc: Zue };
+var bN;
+function ele(e) {
+  bN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
 }
-function Vue(e) {
+function tle(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r, indices: a } = n, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = t.readSync(a.dataId), c = r.shape[u];
   for (let A = 0; A < l.length; ++A) {
     let P = l[A];
@@ -36976,39 +37114,39 @@ function Vue(e) {
   if (w.sizeFromShape(r.shape) === 0)
     return g;
   let b = d.shape.length - 1, v = t.dataIdMap.get(d.dataId).id, k = t.dataIdMap.get(f.dataId).id, T = t.dataIdMap.get(g.dataId).id, N = new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer), E = new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);
-  return dN(v, It[r.dtype], N, b, k, p.batchSize, E, T), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
+  return bN(v, It[r.dtype], N, b, k, p.batchSize, E, T), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
 }
-var Wue = { kernelName: bo, backendName: "wasm", setupFunc: Bue, kernelFunc: Vue };
-var Uue = false;
-var Gue = gn(vo, Uue, "bool");
-var Hue = false;
-var que = gn(La, Hue, "bool");
-var pN;
-function jue(e) {
-  pN = e.wasm.cwrap(Va, null, ["number", "number", "number", "number"]);
+var nle = { kernelName: vo, backendName: "wasm", setupFunc: ele, kernelFunc: tle };
+var sle = false;
+var rle = gn(wo, sle, "bool");
+var ale = false;
+var ile = gn(Va, ale, "bool");
+var yN;
+function ole(e) {
+  yN = e.wasm.cwrap(Ua, null, ["number", "number", "number", "number"]);
 }
-function Kue(e) {
+function ule(e) {
   let { inputs: { x: t }, attrs: { alpha: n }, backend: s } = e, r = s.dataIdMap.get(t.dataId).id, a = s.makeOutput(t.shape, "float32");
   if (w.sizeFromShape(t.shape) !== 0) {
     let i = s.dataIdMap.get(a.dataId).id;
-    pN(r, It[t.dtype], n, i);
+    yN(r, It[t.dtype], n, i);
   }
   return a;
 }
-var Xue = { kernelName: Va, backendName: "wasm", setupFunc: jue, kernelFunc: Kue };
-var Yue = false;
-var Que = gn(xo, Yue, "bool");
-var Zue = false;
-var Jue = gn(wo, Zue, "bool");
-var ele = Xt(Wa);
-var tle = false;
-var nle = gn(ko, tle, "bool");
-var hN;
-function sle(e) {
-  hN = e.wasm.cwrap(Ua, null, ["number", "number", "number", "number"]);
+var lle = { kernelName: Ua, backendName: "wasm", setupFunc: ole, kernelFunc: ule };
+var cle = false;
+var dle = gn(ko, cle, "bool");
+var ple = false;
+var hle = gn(Io, ple, "bool");
+var fle = Xt(Ga);
+var mle = false;
+var gle = gn(So, mle, "bool");
+var vN;
+function ble(e) {
+  vN = e.wasm.cwrap(Ha, null, ["number", "number", "number", "number"]);
 }
-function rle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { reductionIndices: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
+function yle(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { reductionIndices: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     l = c, u = v;
@@ -37018,7 +37156,7 @@ function rle(e) {
   let [m, g] = S.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, i.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    hN(u, It[i.dtype], b, v);
+    vN(u, It[i.dtype], b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = S.expandShapeToKeepDim(y.shape, d);
@@ -37026,29 +37164,29 @@ function rle(e) {
   }
   return y;
 }
-var ale = { kernelName: Ua, backendName: "wasm", setupFunc: sle, kernelFunc: rle };
-var ile = false;
-var ole = gn(Ga, ile);
-var fN;
-function ule(e) {
-  fN = e.wasm.cwrap(Ha, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var vle = { kernelName: Ha, backendName: "wasm", setupFunc: ble, kernelFunc: yle };
+var xle = false;
+var wle = gn(qa, xle);
+var xN;
+function kle(e) {
+  xN = e.wasm.cwrap(ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function lle(e) {
+function Ile(e) {
   let { inputs: t, attrs: n, backend: s } = e, r = t.x, a = s.dataIdMap.get(r.dataId).id;
   w.assert(r.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);
   let { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = S.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.dilationHeight, y = c.dilationWidth, v = c.strideHeight, x = c.strideWidth, k = c.inChannels, T = c.outChannels;
   if (c.dataFormat !== "channelsLast")
     throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
   let N = s.makeOutput(c.outShape, "float32"), E = s.dataIdMap.get(N.dataId).id;
-  return fN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, T, E), N;
+  return xN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, T, E), N;
 }
-var cle = { kernelName: Ha, backendName: "wasm", setupFunc: ule, kernelFunc: lle };
-var mN;
-function dle(e) {
-  mN = e.wasm.cwrap(qa, null, ["number, number, number"]);
+var Sle = { kernelName: ja, backendName: "wasm", setupFunc: kle, kernelFunc: Ile };
+var wN;
+function Cle(e) {
+  wN = e.wasm.cwrap(Ka, null, ["number, number, number"]);
 }
-function ple(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t), f = p;
+function Nle(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t), f = p;
   if (h) {
     let x = t.dataIdMap.get(c.dataId).id;
     x !== o && (l = c, u = x, f = S.getInnerMostAxes(f.length, l.shape.length));
@@ -37059,7 +37197,7 @@ function ple(e) {
   let v = t.makeOutput(m, "float32");
   if (w.sizeFromShape(l.shape) !== 0) {
     let x = t.dataIdMap.get(v.dataId).id;
-    mN(u, b, x);
+    wN(u, b, x);
   }
   if (h && t.disposeData(c.dataId), a) {
     let x = S.expandShapeToKeepDim(v.shape, d);
@@ -37067,13 +37205,13 @@ function ple(e) {
   }
   return l.dtype !== "float32" && t.disposeData(y.dataId), v;
 }
-var hle = { kernelName: qa, backendName: "wasm", setupFunc: dle, kernelFunc: ple };
-var gN;
-function fle(e) {
-  gN = e.wasm.cwrap(ja, null, ["number", "number", "number", "number"]);
+var Tle = { kernelName: Ka, backendName: "wasm", setupFunc: Cle, kernelFunc: Nle };
+var kN;
+function $le(e) {
+  kN = e.wasm.cwrap(Xa, null, ["number", "number", "number", "number"]);
 }
-function mle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t);
+function _le(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t);
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     v !== o && (l = c, u = v);
@@ -37083,7 +37221,7 @@ function mle(e) {
   let [m, g] = S.computeOutAndReduceShapes(l.shape, p), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    gN(u, It[i.dtype], b, v);
+    kN(u, It[i.dtype], b, v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = S.expandShapeToKeepDim(y.shape, d);
@@ -37091,119 +37229,119 @@ function mle(e) {
   }
   return y;
 }
-var gle = { kernelName: ja, backendName: "wasm", setupFunc: fle, kernelFunc: mle };
-var ble = false;
-var yle = gn(Ka, ble);
-var bN = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))(bN || {});
-var yN;
-function vle(e) {
-  yN = e.wasm.cwrap(Xa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
+var Ale = { kernelName: Xa, backendName: "wasm", setupFunc: $le, kernelFunc: _le };
+var Ele = false;
+var Rle = gn(Ya, Ele);
+var IN = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))(IN || {});
+var SN;
+function Dle(e) {
+  SN = e.wasm.cwrap(Qa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
 }
-function xle(e) {
+function Fle(e) {
   let { inputs: { x: t }, backend: n, attrs: { paddings: s, mode: r } } = e, a = s.map((f, m) => f[0] + t.shape[m] + f[1]), i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), u = n.dataIdMap.get(o.dataId).id, l = new Uint8Array(new Int32Array(t.shape).buffer), c = s.map((f) => f[0]), p = s.map((f) => f[1]), d = new Uint8Array(new Int32Array(c).buffer), h = new Uint8Array(new Int32Array(p).buffer);
-  return yN(i, l, t.shape.length, It[t.dtype], d, h, bN[r], u), o;
+  return SN(i, l, t.shape.length, It[t.dtype], d, h, IN[r], u), o;
 }
-var wle = { kernelName: Xa, backendName: "wasm", kernelFunc: xle, setupFunc: vle };
-var kle = true;
-var Ile = gn(Ya, kle);
-var Sle = Xt(Io);
-function Rv(e, t) {
+var Ole = { kernelName: Qa, backendName: "wasm", kernelFunc: Fle, setupFunc: Dle };
+var Ple = true;
+var zle = gn(Za, Ple);
+var Mle = Xt(Co);
+function Fv(e, t) {
   let n = new Int32Array(e.wasm.HEAPU8.buffer, t, 4), s = n[0], r = n[1], a = n[2], i = n[3];
   return e.wasm._free(t), { pSelectedIndices: s, selectedSize: r, pSelectedScores: a, pValidOutputs: i };
 }
-var vN;
-function Cle(e) {
-  vN = e.wasm.cwrap(Co, "number", ["number", "number", "number", "number", "number"]);
+var CN;
+function Lle(e) {
+  CN = e.wasm.cwrap(To, "number", ["number", "number", "number", "number", "number"]);
 }
-function Nle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i } = s, { boxes: o, scores: u } = n, l = t.dataIdMap.get(o.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = vN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = Rv(t, p);
+function Ble(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i } = s, { boxes: o, scores: u } = n, l = t.dataIdMap.get(o.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = CN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = Fv(t, p);
   return t.wasm._free(f), t.wasm._free(m), t.makeOutput([h], "int32", d);
 }
-var Tle = { kernelName: Co, backendName: "wasm", setupFunc: Cle, kernelFunc: Nle };
-var xN;
-function $le(e) {
-  xN = e.wasm.cwrap(kl, "number", ["number", "number", "number", "number", "number", "bool"]);
+var Vle = { kernelName: To, backendName: "wasm", setupFunc: Lle, kernelFunc: Ble };
+var NN;
+function Wle(e) {
+  NN = e.wasm.cwrap(kl, "number", ["number", "number", "number", "number", "number", "bool"]);
 }
-function _le(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, padToMaxOutputSize: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = xN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Rv(t, d);
+function Ule(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, padToMaxOutputSize: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = NN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Fv(t, d);
   t.wasm._free(m);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([], "int32", g);
   return [b, y];
 }
-var Ale = { kernelName: kl, backendName: "wasm", setupFunc: $le, kernelFunc: _le };
-var wN;
-function Ele(e) {
-  wN = e.wasm.cwrap(No, "number", ["number", "number", "number", "number", "number", "number"]);
+var Gle = { kernelName: kl, backendName: "wasm", setupFunc: Wle, kernelFunc: Ule };
+var TN;
+function Hle(e) {
+  TN = e.wasm.cwrap($o, "number", ["number", "number", "number", "number", "number", "number"]);
 }
-function Rle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, softNmsSigma: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = wN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Rv(t, d);
+function qle(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, softNmsSigma: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = TN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Fv(t, d);
   t.wasm._free(g);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([f], "float32", m);
   return [b, y];
 }
-var Dle = { kernelName: No, backendName: "wasm", setupFunc: Ele, kernelFunc: Rle };
-var Fle = false;
-var Ole = gn(So, Fle, "bool");
-var kN;
-function Ple(e) {
-  kN = e.wasm.cwrap($o, null, ["number", "number", "number", "number", "number"]);
+var jle = { kernelName: $o, backendName: "wasm", setupFunc: Hle, kernelFunc: qle };
+var Kle = false;
+var Xle = gn(No, Kle, "bool");
+var $N;
+function Yle(e) {
+  $N = e.wasm.cwrap(Ao, null, ["number", "number", "number", "number", "number"]);
 }
-function zle(e) {
+function Qle(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = n.makeOutput([...r.shape, a], "int32"), l = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(r.dataId).id;
-  return kN(p, a, i, o, l), u;
+  return $N(p, a, i, o, l), u;
 }
-var Mle = { kernelName: $o, backendName: "wasm", setupFunc: Ple, kernelFunc: zle };
-function Lle(e) {
+var Zle = { kernelName: Ao, backendName: "wasm", setupFunc: Yle, kernelFunc: Qle };
+function Jle(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(1), s;
 }
-var Ble = { kernelName: To, backendName: "wasm", kernelFunc: Lle };
-function Vle(e) {
+var ece = { kernelName: _o, backendName: "wasm", kernelFunc: Jle };
+function tce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Zm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return eg({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = Zm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = eg({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
-  }), l = Y2({ inputs: u, backend: n, attrs: { axis: r } });
+  }), l = nN({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var Wle = { kernelName: _o, backendName: "wasm", kernelFunc: Vle };
-var IN;
-function Ule(e) {
-  IN = e.wasm.cwrap(Qa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
+var nce = { kernelName: Eo, backendName: "wasm", kernelFunc: tce };
+var _N;
+function sce(e) {
+  _N = e.wasm.cwrap(Ja, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
 }
-function Gle(e) {
+function rce(e) {
   let { inputs: { x: t }, backend: n, attrs: { paddings: s, constantValue: r } } = e, a = s.map((m, g) => m[0] + t.shape[g] + m[1]);
   if (w.sizeFromShape(t.shape) === 0)
-    return aN({ backend: n, attrs: { shape: a, value: r, dtype: t.dtype } });
+    return dN({ backend: n, attrs: { shape: a, value: r, dtype: t.dtype } });
   let i = n.dataIdMap.get(t.dataId).id, o = n.makeOutput(a, t.dtype), l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(t.shape).buffer), p = s.map((m) => m[0]), d = s.map((m) => m[1]), h = new Uint8Array(new Int32Array(p).buffer), f = new Uint8Array(new Int32Array(d).buffer);
-  return IN(i, c, t.shape.length, It[t.dtype], h, f, r, l), o;
+  return _N(i, c, t.shape.length, It[t.dtype], h, f, r, l), o;
 }
-var SN = { kernelName: Qa, backendName: "wasm", kernelFunc: Gle, setupFunc: Ule };
-var Hle = false;
-var qle = gn(Za, Hle);
-var CN;
-function jle(e) {
-  CN = e.wasm.cwrap(Ja, null, ["number", "number", "number"]);
+var AN = { kernelName: Ja, backendName: "wasm", kernelFunc: rce, setupFunc: sce };
+var ace = false;
+var ice = gn(ei, ace);
+var EN;
+function oce(e) {
+  EN = e.wasm.cwrap(ti, null, ["number", "number", "number"]);
 }
-function Kle(e) {
+function uce(e) {
   let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, o = a, u = s, l = u;
   u.dtype !== "float32" && (l = ac({ backend: n, inputs: { x: s }, attrs: { dtype: "float32" } }), o = n.dataIdMap.get(l.dataId).id);
   let c = n.makeOutput(s.shape, "float32"), p = n.dataIdMap.get(c.dataId).id;
-  return CN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
+  return EN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
 }
-var Xle = { kernelName: Ja, backendName: "wasm", setupFunc: jle, kernelFunc: Kle };
-var NN;
-function Yle(e) {
-  NN = e.wasm.cwrap(Ao, null, ["number", "number", "number", "number"]);
+var lce = { kernelName: ti, backendName: "wasm", setupFunc: oce, kernelFunc: uce };
+var RN;
+function cce(e) {
+  RN = e.wasm.cwrap(ni, null, ["number", "number", "number", "number"]);
 }
-function Qle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t), f = p;
+function dce(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t), f = p;
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     v !== o && (l = c, u = v, f = S.getInnerMostAxes(f.length, l.shape.length));
@@ -37212,7 +37350,7 @@ function Qle(e) {
   let [m, g] = S.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    NN(u, b, It[y.dtype], v);
+    RN(u, b, It[y.dtype], v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = S.expandShapeToKeepDim(y.shape, d);
@@ -37220,110 +37358,110 @@ function Qle(e) {
   }
   return y;
 }
-var Zle = { kernelName: Ao, backendName: "wasm", setupFunc: Yle, kernelFunc: Qle };
-var Jle = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = rv(s, r, a, i), u = t.makeOutput([o.length], i);
+var pce = { kernelName: ni, backendName: "wasm", setupFunc: cce, kernelFunc: dce };
+var hce = (e) => {
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = iv(s, r, a, i), u = t.makeOutput([o.length], i);
   return t.typedArrayFromHeap(u).set(o), u;
 };
-var ece = { kernelName: Il, backendName: "wasm", kernelFunc: Jle };
-var tce = true;
-var nce = gn(Da, tce);
-var sce = Xt(ei);
-var rce = Xt(ni);
-var TN;
-function ace(e) {
-  TN = e.wasm.cwrap(ti, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var fce = { kernelName: Il, backendName: "wasm", kernelFunc: hce };
+var mce = true;
+var gce = gn(Oa, mce);
+var bce = Xt(si);
+var yce = Xt(ai);
+var DN;
+function vce(e) {
+  DN = e.wasm.cwrap(ri, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function ice(e) {
+function xce(e) {
   let { backend: t, inputs: n, attrs: s } = e, { images: r } = n, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, [c, p, d, h] = r.shape, f = [c, u, l, h], m = t.dataIdMap.get(r.dataId), g;
   m.dtype !== "float32" && (g = ac({ backend: t, inputs: { x: r }, attrs: { dtype: "float32" } }), m = t.dataIdMap.get(g.dataId));
   let b = m.id, y = t.makeOutput(f, "float32");
   if (w.sizeFromShape(r.shape) === 0)
     return y;
   let v = t.dataIdMap.get(y.dataId).id;
-  return TN(b, c, p, d, h, u, l, a ? 1 : 0, i ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
+  return DN(b, c, p, d, h, u, l, a ? 1 : 0, i ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
 }
-var oce = { kernelName: ti, backendName: "wasm", setupFunc: ace, kernelFunc: ice };
-var $N;
-function uce(e) {
-  $N = e.wasm.cwrap(Ro, null, ["number", "array", "number", "array", "number", "number"]);
+var wce = { kernelName: ri, backendName: "wasm", setupFunc: vce, kernelFunc: xce };
+var FN;
+function kce(e) {
+  FN = e.wasm.cwrap(Do, null, ["number", "array", "number", "array", "number", "number"]);
 }
-function lce(e) {
+function Ice(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = w.parseAxisParam(a, r.shape);
   if (r.shape.length === 0)
-    return eh({ inputs: { x: r }, backend: n });
+    return nh({ inputs: { x: r }, backend: n });
   let o = n.makeOutput(r.shape, r.dtype), u = n.dataIdMap.get(r.dataId).id, l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(i).buffer), p = new Uint8Array(new Int32Array(r.shape).buffer);
-  $N(u, c, i.length, p, r.shape.length, l);
+  FN(u, c, i.length, p, r.shape.length, l);
   let d = yn({ inputs: { x: o }, attrs: { shape: r.shape }, backend: n });
   return n.disposeData(o.dataId), d;
 }
-var cce = { kernelName: Ro, backendName: "wasm", kernelFunc: lce, setupFunc: uce };
-var _N;
-function dce(e) {
-  _N = e.wasm.cwrap(qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
+var Sce = { kernelName: Do, backendName: "wasm", kernelFunc: Ice, setupFunc: kce };
+var ON;
+function Cce(e) {
+  ON = e.wasm.cwrap(jo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
 }
-function pce(e) {
+function Nce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { image: r } = t, { radians: a, fillValue: i, center: o } = s, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(r.dataId).id, c = n.dataIdMap.get(u.dataId).id, [p, d, h, f] = r.shape, [m, g] = S.getImageCenter(o, d, h), b = i === 0, y = 255, v = typeof i == "number" ? [i, i, i, b ? 0 : y] : [...i, y], x = new Uint8Array(new Int32Array(v).buffer);
-  return _N(l, p, d, h, f, a, m, g, x, v.length, c), u;
+  return ON(l, p, d, h, f, a, m, g, x, v.length, c), u;
 }
-var hce = { kernelName: qo, backendName: "wasm", kernelFunc: pce, setupFunc: dce };
-var fce = Xt(Do);
-var mce = Xt(si);
-var AN;
-function gce(e) {
-  AN = e.wasm.cwrap(Fo, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
+var Tce = { kernelName: jo, backendName: "wasm", kernelFunc: Nce, setupFunc: Cce };
+var $ce = Xt(Fo);
+var _ce = Xt(ii);
+var PN;
+function Ace(e) {
+  PN = e.wasm.cwrap(Oo, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
 }
-function bce(e) {
+function Ece(e) {
   let { backend: t, inputs: n, attrs: s } = e, { indices: r, updates: a } = n, { shape: i } = s, o = t.makeOutput(i, a.dtype);
   if (w.sizeFromShape(i) === 0)
     return o;
-  let { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = Ak.calculateShapes(a, r, i), f = t.dataIdMap.get(r.dataId).id, g = t.dataIdMap.get(a.dataId).id, b = new Uint8Array(new Int32Array(p).buffer), y = t.dataIdMap.get(o.dataId).id;
-  return AN(f, g, It[a.dtype], u, l, c, b, d, y), o;
+  let { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = Fk.calculateShapes(a, r, i), f = t.dataIdMap.get(r.dataId).id, g = t.dataIdMap.get(a.dataId).id, b = new Uint8Array(new Int32Array(p).buffer), y = t.dataIdMap.get(o.dataId).id;
+  return PN(f, g, It[a.dtype], u, l, c, b, d, y), o;
 }
-var yce = { kernelName: Fo, backendName: "wasm", setupFunc: gce, kernelFunc: bce };
-var EN;
-function vce(e) {
-  EN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
+var Rce = { kernelName: Oo, backendName: "wasm", setupFunc: Ace, kernelFunc: Ece };
+var zN;
+function Dce(e) {
+  zN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
 }
-function xce(e) {
+function Fce(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = n.dataIdMap.get(s.dataId).id, o = n.dataIdMap.get(r.dataId).id, u = n.dataIdMap.get(a.dataId).id, l = n.makeOutput(r.shape, r.dtype), c = n.dataIdMap.get(l.dataId).id, p = s.shape.length, d = r.shape.length, h = p === 0 || p > 1 || d === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
-  return EN(i, o, u, h, c), l;
+  return zN(i, o, u, h, c), l;
 }
-var wce = { kernelName: Oo, backendName: "wasm", kernelFunc: xce, setupFunc: vce };
-var RN;
-function kce(e) {
-  RN = e.wasm.cwrap(ai, null, ["number", "number"]);
+var Oce = { kernelName: Po, backendName: "wasm", kernelFunc: Fce, setupFunc: Dce };
+var MN;
+function Pce(e) {
+  MN = e.wasm.cwrap(ui, null, ["number", "number"]);
 }
-function Ice(e) {
+function zce(e) {
   let { backend: t, inputs: { x: n } } = e, s = t.dataIdMap.get(n.dataId).id, r = t.makeOutput(n.shape, n.dtype), a = t.dataIdMap.get(r.dataId).id;
-  return w.sizeFromShape(r.shape) === 0 || RN(s, a), r;
+  return w.sizeFromShape(r.shape) === 0 || MN(s, a), r;
 }
-var Sce = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: kce, kernelFunc: Ice };
-var Cce = Xt(ri);
-var DN;
-function Nce(e) {
-  DN = e.wasm.cwrap(ui, null, ["number", "number", "number", "number"]);
+var Mce = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: Pce, kernelFunc: zce };
+var Lce = Xt(oi);
+var LN;
+function Bce(e) {
+  LN = e.wasm.cwrap(di, null, ["number", "number", "number", "number"]);
 }
-function Tce(e) {
+function Vce(e) {
   let { backend: t, inputs: { logits: n }, attrs: { dim: s } } = e, r = t.dataIdMap.get(n.dataId).id, a = t.makeOutput(n.shape, n.dtype), i = t.dataIdMap.get(a.dataId).id, o = n.shape[s], u = w.sizeFromShape(n.shape) / o;
-  return w.sizeFromShape(a.shape) === 0 || DN(r, i, o, u), a;
+  return w.sizeFromShape(a.shape) === 0 || LN(r, i, o, u), a;
 }
-var $ce = { kernelName: ui, backendName: "wasm", setupFunc: Nce, kernelFunc: Tce };
-function _ce(e) {
+var Wce = { kernelName: di, backendName: "wasm", setupFunc: Bce, kernelFunc: Vce };
+function Uce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s, o = w.sizeFromShape(a), u = [[0, 0]];
   u.push(...i);
   for (let T = 1 + a.length; T < r.shape.length; ++T)
     u.push([0, 0]);
-  let l = SN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = S.getReshaped(l.shape, a, o, false), p = S.getPermuted(c.length, a.length, false), d = S.getReshapedPermuted(l.shape, a, o, false), m = yn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = wr({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = yn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
+  let l = AN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = S.getReshaped(l.shape, a, o, false), p = S.getPermuted(c.length, a.length, false), d = S.getReshapedPermuted(l.shape, a, o, false), m = yn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = kr({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = yn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
   return n.disposeData(l.dataId), n.disposeData(m.dataId), n.disposeData(y.dataId), k;
 }
-var Ace = { kernelName: Mo, backendName: "wasm", kernelFunc: _ce };
-var FN;
-function Ece(e) {
-  FN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Gce = { kernelName: Lo, backendName: "wasm", kernelFunc: Uce };
+var BN;
+function Hce(e) {
+  BN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function Rce(e) {
-  let { backend: t, inputs: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = n, o = s.shape[0], u = s.shape[1], l = t.readSync(a.dataId)[0], c = [o + l, u], p = t.dataIdMap.get(s.dataId).id, d = t.dataIdMap.get(r.dataId).id, h = t.dataIdMap.get(i.dataId).id, f = t.makeOutput(c, s.dtype), m = t.dataIdMap.get(f.dataId).id, g = t.makeOutput(c.slice(0, 1), r.dtype), b = t.dataIdMap.get(g.dataId).id, y = t.makeOutput([l], "bool"), v = t.dataIdMap.get(y.dataId).id, x = t.makeOutput([o], s.dtype), k = t.dataIdMap.get(x.dataId).id, T = t.makeOutput([4], "int32"), N = t.dataIdMap.get(T.dataId).id, E = FN(p, d, It[r.dtype], o, l, u, h, m, b, v, k, N), A = t.readSync(T.dataId), P;
+function qce(e) {
+  let { backend: t, inputs: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = n, o = s.shape[0], u = s.shape[1], l = t.readSync(a.dataId)[0], c = [o + l, u], p = t.dataIdMap.get(s.dataId).id, d = t.dataIdMap.get(r.dataId).id, h = t.dataIdMap.get(i.dataId).id, f = t.makeOutput(c, s.dtype), m = t.dataIdMap.get(f.dataId).id, g = t.makeOutput(c.slice(0, 1), r.dtype), b = t.dataIdMap.get(g.dataId).id, y = t.makeOutput([l], "bool"), v = t.dataIdMap.get(y.dataId).id, x = t.makeOutput([o], s.dtype), k = t.dataIdMap.get(x.dataId).id, T = t.makeOutput([4], "int32"), N = t.dataIdMap.get(T.dataId).id, E = BN(p, d, It[r.dtype], o, l, u, h, m, b, v, k, N), A = t.readSync(T.dataId), P;
   switch (A[0]) {
     case 1: {
       P = S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);
@@ -37342,14 +37480,14 @@ function Rce(e) {
   if (t.disposeData(T.dataId), P)
     throw t.disposeData(f.dataId), t.disposeData(g.dataId), t.disposeData(y.dataId), t.disposeData(x.dataId), new Error(P);
   let R = f, F = g;
-  return E !== c[0] && (R = va({ inputs: { x: f }, attrs: { begin: 0, size: [E, u] }, backend: t }), F = va({ inputs: { x: g }, attrs: { begin: 0, size: E }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [R, F, y, x];
+  return E !== c[0] && (R = xa({ inputs: { x: f }, attrs: { begin: 0, size: [E, u] }, backend: t }), F = xa({ inputs: { x: g }, attrs: { begin: 0, size: E }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [R, F, y, x];
 }
-var Dce = { kernelName: sp, backendName: "wasm", setupFunc: Ece, kernelFunc: Rce };
-var ON;
-function Fce(e) {
-  ON = e.wasm.cwrap(_l, null, ["number", "number", "number", "number", "number", "number", "number"]);
+var jce = { kernelName: ap, backendName: "wasm", setupFunc: Hce, kernelFunc: qce };
+var VN;
+function Kce(e) {
+  VN = e.wasm.cwrap(_l, null, ["number", "number", "number", "number", "number", "number", "number"]);
 }
-function Oce(e) {
+function Xce(e) {
   let { backend: t, inputs: n } = e, { inputIndices: s, inputShape: r, newShape: a } = n;
   if (s.shape.length !== 2)
     throw new Error(`Input indices should be a matrix but received shape
@@ -37360,7 +37498,7 @@ function Oce(e) {
   if (a.shape.length !== 1)
     throw new Error(`Target shape should be a vector but received shape ${a.shape}`);
   let i = t.dataIdMap.get(s.dataId).id, o = t.dataIdMap.get(r.dataId).id, u = t.dataIdMap.get(a.dataId).id, l = s.shape[0], c = w.sizeFromShape(a.shape), p = t.makeOutput([l, c], s.dtype), d = t.dataIdMap.get(p.dataId).id, h = t.makeOutput([c], a.dtype), f = t.dataIdMap.get(h.dataId).id, m = t.makeOutput([3], "int32"), g = t.dataIdMap.get(m.dataId).id;
-  ON(i, o, u, l, d, f, g);
+  VN(i, o, u, l, d, f, g);
   let b = t.readSync(m.dataId), y;
   switch (b[0]) {
     case 0: {
@@ -37391,19 +37529,19 @@ function Oce(e) {
     throw t.disposeData(p.dataId), t.disposeData(h.dataId), new Error(y);
   return [p, h];
 }
-var Pce = { kernelName: _l, backendName: "wasm", setupFunc: Fce, kernelFunc: Oce };
-var PN;
-function zN(e) {
-  PN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
+var Yce = { kernelName: _l, backendName: "wasm", setupFunc: Kce, kernelFunc: Xce };
+var WN;
+function UN(e) {
+  WN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
-function MN(e, t) {
+function GN(e, t) {
   let { backend: n, inputs: s } = e, { data: r, indices: a, segmentIds: i } = s, o = a.shape[0], u = n.readSync(i.dataId, o - 1, o)[0], c = o > 0 ? u + 1 : 0;
   if (c < 0)
     throw new Error(S.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
   let p = r.shape.slice();
   p[0] = c;
   let d = n.dataIdMap.get(r.dataId).id, h = n.dataIdMap.get(a.dataId).id, f = n.dataIdMap.get(i.dataId).id, m = n.makeOutput(p, r.dtype), g = n.dataIdMap.get(m.dataId).id, b = n.makeOutput([4], "int32"), y = n.dataIdMap.get(b.dataId).id;
-  PN(d, It[r.dtype], r.shape[0], h, f, g, y, t, 0);
+  WN(d, It[r.dtype], r.shape[0], h, f, g, y, t, 0);
   let v = n.readSync(b.dataId), x;
   switch (v[0]) {
     case 0: {
@@ -37427,64 +37565,64 @@ function MN(e, t) {
     throw n.disposeData(m.dataId), new Error(x);
   return m;
 }
-function zce(e) {
-  return MN(e, true);
+function Qce(e) {
+  return GN(e, true);
 }
-var Mce = { kernelName: rp, backendName: "wasm", setupFunc: zN, kernelFunc: zce };
-function Lce(e) {
-  return MN(e, false);
+var Zce = { kernelName: ip, backendName: "wasm", setupFunc: UN, kernelFunc: Qce };
+function Jce(e) {
+  return GN(e, false);
 }
-var Bce = { kernelName: ap, backendName: "wasm", setupFunc: zN, kernelFunc: Lce };
-function Vce(e) {
+var ede = { kernelName: op, backendName: "wasm", setupFunc: UN, kernelFunc: Jce };
+function tde(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = n, o = w.parseAxisParam(i, r.shape)[0], u = S.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
   return u.map((p) => {
     let d = [...c];
     d[o] = p;
-    let h = va({ inputs: { x: r }, attrs: { begin: l, size: d }, backend: s });
+    let h = xa({ inputs: { x: r }, attrs: { begin: l, size: d }, backend: s });
     return l[o] += p, h;
   });
 }
-var Wce = { kernelName: Lo, backendName: "wasm", kernelFunc: Vce };
-var Uce = Xt(ii);
-var Gce = Xt(Al);
-var Hce = true;
-var qce = gn(li, Hce);
-var LN;
-function jce(e) {
-  LN = e.wasm.cwrap(hi, null, ["number", "number", "number", "number"]);
+var nde = { kernelName: Bo, backendName: "wasm", kernelFunc: tde };
+var sde = Xt(li);
+var rde = Xt(Al);
+var ade = true;
+var ide = gn(pi, ade);
+var HN;
+function ode(e) {
+  HN = e.wasm.cwrap(gi, null, ["number", "number", "number", "number"]);
 }
-function Kce(e) {
+function ude(e) {
   let { backend: t, inputs: n, attrs: s } = e, { alpha: r } = s, { x: a } = n, i = t.dataIdMap.get(a.dataId).id, o = t.makeOutput(a.shape, a.dtype), u = t.dataIdMap.get(o.dataId).id;
-  return LN(i, r, It[a.dtype], u), o;
+  return HN(i, r, It[a.dtype], u), o;
 }
-var Xce = { kernelName: hi, backendName: "wasm", setupFunc: jce, kernelFunc: Kce };
-var BN;
-function Yce(e) {
-  BN = e.wasm.cwrap(Bo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
+var lde = { kernelName: gi, backendName: "wasm", setupFunc: ode, kernelFunc: ude };
+var qN;
+function cde(e) {
+  qN = e.wasm.cwrap(Vo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
 }
-function Qce(e) {
+function dde(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
   if (m)
     k = yn({ inputs: { x: r }, backend: t, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let T = wt.computeOutShape(y, v, x), N = va({ inputs: { x: r }, backend: t, attrs: { begin: y, size: T } });
+    let T = wt.computeOutShape(y, v, x), N = xa({ inputs: { x: r }, backend: t, attrs: { begin: y, size: T } });
     k = yn({ inputs: { x: N }, backend: t, attrs: { shape: f } }), t.disposeData(N.dataId);
   } else {
     let T = t.makeOutput(h, "float32"), N = t.dataIdMap.get(r.dataId).id, E = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), A = new Uint8Array(new Int32Array(y).buffer), P = new Uint8Array(new Int32Array(v).buffer), R = new Uint8Array(new Int32Array(x).buffer), F = new Uint8Array(new Int32Array(h).buffer), $ = new Uint8Array(new Int32Array(w.computeStrides(h)).buffer), z = t.dataIdMap.get(T.dataId).id;
-    BN(N, E, r.shape.length, A, P, R, F, $, h.length, z), k = yn({ inputs: { x: T }, backend: t, attrs: { shape: f } }), t.disposeData(T.dataId);
+    qN(N, E, r.shape.length, A, P, R, F, $, h.length, z), k = yn({ inputs: { x: T }, backend: t, attrs: { shape: f } }), t.disposeData(T.dataId);
   }
   return k;
 }
-var Zce = { kernelName: Bo, backendName: "wasm", setupFunc: Yce, kernelFunc: Qce };
-var Jce = true;
-var ede = gn(ci, Jce);
-var VN;
-function tde(e) {
-  VN = e.wasm.cwrap(oi, null, ["number", "number", "number", "number"]);
+var pde = { kernelName: Vo, backendName: "wasm", setupFunc: cde, kernelFunc: dde };
+var hde = true;
+var fde = gn(hi, hde);
+var jN;
+function mde(e) {
+  jN = e.wasm.cwrap(ci, null, ["number", "number", "number", "number"]);
 }
-function nde(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Fr(i, r, t), f = p;
+function gde(e) {
+  let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Or(i, r, t), f = p;
   if (h) {
     let v = t.dataIdMap.get(c.dataId).id;
     v !== o && (l = c, u = v, f = S.getInnerMostAxes(f.length, l.shape.length));
@@ -37493,7 +37631,7 @@ function nde(e) {
   let [m, g] = S.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = t.makeOutput(m, l.dtype);
   if (w.sizeFromShape(l.shape) !== 0) {
     let v = t.dataIdMap.get(y.dataId).id;
-    VN(u, b, It[y.dtype], v);
+    jN(u, b, It[y.dtype], v);
   }
   if (h && t.disposeData(c.dataId), a) {
     let v = S.expandShapeToKeepDim(y.shape, d);
@@ -37501,37 +37639,37 @@ function nde(e) {
   }
   return y;
 }
-var sde = { kernelName: oi, backendName: "wasm", setupFunc: tde, kernelFunc: nde };
-var rde = Xt(Vo);
-var ade = Xt(di);
-var WN;
-function ide(e) {
-  WN = e.wasm.cwrap(Cr, null, ["number", "array", "number", "array", "number", "number"]);
+var bde = { kernelName: ci, backendName: "wasm", setupFunc: mde, kernelFunc: gde };
+var yde = Xt(Wo);
+var vde = Xt(fi);
+var KN;
+function xde(e) {
+  KN = e.wasm.cwrap(Nr, null, ["number", "array", "number", "array", "number", "number"]);
 }
-function ode(e) {
+function wde(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, a = n.dataIdMap.get(r.dataId).id, { reps: i } = s, o = new Array(r.shape.length);
   for (let d = 0; d < o.length; d++)
     o[d] = r.shape[d] * i[d];
   let u = new Uint8Array(new Int32Array(r.shape).buffer), l = new Uint8Array(new Int32Array(o).buffer), c = n.makeOutput(o, r.dtype), p = n.dataIdMap.get(c.dataId).id;
-  return WN(a, u, r.shape.length, l, o.length, It[c.dtype], p), c;
+  return KN(a, u, r.shape.length, l, o.length, It[c.dtype], p), c;
 }
-var ude = { kernelName: Cr, backendName: "wasm", setupFunc: ide, kernelFunc: ode };
-var UN;
-function lde(e) {
-  UN = e.wasm.cwrap(Wo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
+var kde = { kernelName: Nr, backendName: "wasm", setupFunc: xde, kernelFunc: wde };
+var XN;
+function Ide(e) {
+  XN = e.wasm.cwrap(Uo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
 }
-var cde = ({ inputs: e, backend: t, attrs: n }) => {
+var Sde = ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s } = e, { k: r, sorted: a } = n, i = t.dataIdMap.get(s.dataId).id, o = new Uint8Array(new Int32Array(s.shape).buffer), u = s.shape.slice();
   u[u.length - 1] = r;
   let l = t.makeOutput(u, s.dtype), c = t.dataIdMap.get(l.dataId).id, p = t.makeOutput(u, "int32"), d = t.dataIdMap.get(p.dataId).id;
-  return UN(i, o, s.shape.length, It[s.dtype], r, a, c, d), [l, p];
+  return XN(i, o, s.shape.length, It[s.dtype], r, a, c, d), [l, p];
 };
-var dde = { kernelName: Wo, backendName: "wasm", setupFunc: lde, kernelFunc: cde };
-var GN;
-function pde(e) {
-  GN = e.wasm.cwrap(Uo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+var Cde = { kernelName: Uo, backendName: "wasm", setupFunc: Ide, kernelFunc: Sde };
+var YN;
+function Nde(e) {
+  YN = e.wasm.cwrap(Go, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
 }
-function hde(e) {
+function Tde(e) {
   let { backend: t, inputs: n, attrs: s } = e, { image: r, transforms: a } = n, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), y = t.makeOutput(g, r.dtype), v = t.dataIdMap.get(y.dataId).id, k = t.dataIdMap.get(r.dataId).id, N = t.dataIdMap.get(a.dataId).id, E = i === "nearest" ? 1 : 2, A;
   switch (o) {
     case "constant":
@@ -37550,10 +37688,10 @@ function hde(e) {
       A = 1;
       break;
   }
-  return GN(k, N, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, E, A, u, v), y;
+  return YN(k, N, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, E, A, u, v), y;
 }
-var fde = { kernelName: Uo, backendName: "wasm", setupFunc: pde, kernelFunc: hde };
-function mde(e) {
+var $de = { kernelName: Go, backendName: "wasm", setupFunc: Nde, kernelFunc: Tde };
+function _de(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
   let i = r.shape[a], o = r.shape.length, u = new Array(o - 1), l = 0;
@@ -37562,22 +37700,22 @@ function mde(e) {
   let c = new Array(i), p = new Array(o).fill(0), d = r.shape.slice();
   d[a] = 1;
   for (let h = 0; h < c.length; h++)
-    p[a] = h, c[h] = va({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
+    p[a] = h, c[h] = xa({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
   return c.map(({ dataId: h, dtype: f }) => ({ dataId: h, dtype: f, shape: u }));
 }
-var gde = { kernelName: Go, backendName: "wasm", kernelFunc: mde };
-function bde(e) {
+var Ade = { kernelName: Ho, backendName: "wasm", kernelFunc: _de };
+function Ede(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(0), s;
 }
-var yde = { kernelName: Ho, backendName: "wasm", kernelFunc: bde };
-var vde = [ooe, uoe, coe, hoe, woe, Soe, Toe, Aoe, Foe, Boe, Voe, Woe, Hoe, qoe, Xoe, Zoe, Joe, eue, sue, iue, lue, pue, mue, gue, yue, vue, xue, wue, Sue, Cue, Tue, Aue, Due, Pue, Lue, Wue, Gue, que, foe, Xue, Que, Jue, ele, nle, ale, ole, cle, hle, gle, yle, wle, Ile, Sle, Tle, Ale, Dle, Ole, Mle, Ble, Wle, SN, qle, Xle, Zle, ece, nce, sce, rce, Eoe, oce, cce, hce, fce, mce, yce, wce, Sce, Cce, Moe, $ce, Ace, Dce, Pce, Mce, Bce, Wce, Uce, Gce, qce, Xce, Zce, ede, sde, rde, ade, ude, dde, fde, yoe, gde, yde];
-for (let e of vde)
+var Rde = { kernelName: qo, backendName: "wasm", kernelFunc: Ede };
+var Dde = [woe, koe, Soe, Toe, Ooe, Moe, Voe, Goe, Koe, eue, tue, nue, aue, iue, lue, pue, hue, fue, bue, xue, Iue, Nue, _ue, Aue, Rue, Due, Fue, Oue, Mue, Lue, Vue, Gue, jue, Yue, Jue, nle, rle, ile, $oe, lle, dle, hle, fle, gle, vle, wle, Sle, Tle, Ale, Rle, Ole, zle, Mle, Vle, Gle, jle, Xle, Zle, ece, nce, AN, ice, lce, pce, fce, gce, bce, yce, Hoe, wce, Sce, Tce, $ce, _ce, Rce, Oce, Mce, Lce, Zoe, Wce, Gce, jce, Yce, Zce, ede, nde, sde, rde, ide, lde, pde, fde, bde, yde, vde, kde, Cde, $de, Roe, Ade, Rde];
+for (let e of Dde)
   El(e);
-var Jm = X();
-Jm.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
-Jm.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
-  if (Jm.get("IS_NODE"))
+var tg = X();
+tg.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
+tg.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
+  if (tg.get("IS_NODE"))
     return false;
   try {
     return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11]));
@@ -37585,13 +37723,13 @@ Jm.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
     return false;
   }
 });
-var Lw = xa(a$());
-var xde = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
+var Uw = wa(d$());
+var Fde = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
 ");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});`;
-var wde = xa(i$());
-var kde = class extends nl {
+var Ode = wa(p$());
+var Pde = class extends sl {
   constructor(e) {
-    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(HN), eg = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Ud(this, Ss());
+    super(), this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(QN), ng = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Hd(this, Ss());
   }
   write(e, t, n) {
     let s = { id: this.dataIdNextNumber++ };
@@ -37623,7 +37761,7 @@ var kde = class extends nl {
       return (t == null || t === 0) && (n == null || n >= i.length) ? i : i.slice(t, n);
     t = t || 0, n = n || w.sizeFromShape(a);
     let o = w.bytesPerElement(r), u = this.wasm.HEAPU8.slice(s + t * o, s + n * o);
-    return Cde(u.buffer, r);
+    return Lde(u.buffer, r);
   }
   disposeData(e, t = false) {
     if (this.dataIdMap.has(e)) {
@@ -37679,7 +37817,7 @@ var kde = class extends nl {
     }
   }
 };
-function Ide(e) {
+function zde(e) {
   return (t, n) => (w.fetch(e, { credentials: "same-origin" }).then((s) => {
     s.ok || t.env.a(`failed to load wasm binary file at '${e}'`), s.arrayBuffer().then((r) => {
       WebAssembly.instantiate(r, t).then((a) => {
@@ -37688,38 +37826,38 @@ function Ide(e) {
     });
   }), {});
 }
-function Bw(e, t, n) {
-  if (Bd != null)
-    return Bd;
+function Gw(e, t, n) {
+  if (Wd != null)
+    return Wd;
   let s = "tfjs-backend-wasm.wasm";
-  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Lu != null && Lu[s] != null ? Lu[s] : n + s;
+  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Bu != null && Bu[s] != null ? Bu[s] : n + s;
 }
-async function Sde() {
+async function Mde() {
   let [e, t] = await Promise.all([X().getAsync("WASM_HAS_SIMD_SUPPORT"), X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
   return new Promise((n, s) => {
     let r = {};
     r.locateFile = (o, u) => {
       if (o.endsWith(".worker.js")) {
-        let l = xde.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
+        let l = Fde.replace(/\n/g, "\\n"), c = new Blob([l], { type: "application/javascript" });
         return URL.createObjectURL(c);
       }
-      return o.endsWith(".wasm") ? Bw(e, t, Pu != null ? Pu : u) : u + o;
-    }, Dv && (r.instantiateWasm = Ide(Bw(e, t, Pu != null ? Pu : "")));
+      return o.endsWith(".wasm") ? Gw(e, t, zu != null ? zu : u) : u + o;
+    }, Ov && (r.instantiateWasm = zde(Gw(e, t, zu != null ? zu : "")));
     let a = false;
     r.onAbort = () => {
-      if (a || Bu)
+      if (a || Vu)
         return;
-      Bu = true, s({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
+      Vu = true, s({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
     };
     let i;
-    t && e && Bd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Lw.default.toString()], { type: "text/javascript" }), i = (0, Lw.default)(r)) : i = (0, wde.default)(r), i.then((o) => {
-      a = true, Bu = false;
+    t && e && Wd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Uw.default.toString()], { type: "text/javascript" }), i = (0, Uw.default)(r)) : i = (0, Ode.default)(r), i.then((o) => {
+      a = true, Vu = false;
       let u = null;
       o.tfjs = { init: o.cwrap("init", null, []), initWithThreadsCount: o.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: o.cwrap("get_threads_count", "number", []), registerTensor: o.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: o.cwrap("dispose_data", u, ["number"]), dispose: o.cwrap("dispose", u, []) }, n({ wasm: o });
     });
   });
 }
-function Cde(e, t) {
+function Lde(e, t) {
   switch (t) {
     case "float32":
       return new Float32Array(e);
@@ -37731,48 +37869,48 @@ function Cde(e, t) {
       throw new Error(`Unknown dtype ${t}`);
   }
 }
-var Nde = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
-var Bd = null;
-var Pu = null;
-var Lu = {};
-var Bu = false;
-var Dv = false;
-function Lpe(e, t = false) {
-  if (Gk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Bu)
+var Bde = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
+var Wd = null;
+var zu = null;
+var Bu = {};
+var Vu = false;
+var Ov = false;
+function Jpe(e, t = false) {
+  if (Kk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Vu)
     throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
-  Bd = e, Dv = t;
+  Wd = e, Ov = t;
 }
-function Bpe(e, t = false) {
-  if (Bu)
+function ehe(e, t = false) {
+  if (Vu)
     throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
   if (typeof e == "string")
-    Pu = e;
+    zu = e;
   else {
-    Lu = e;
-    let n = Nde.filter((s) => Lu[s] == null);
+    Bu = e;
+    let n = Bde.filter((s) => Bu[s] == null);
     if (n.length > 0)
       throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
   }
-  Dv = t;
+  Ov = t;
 }
-var HN = -1;
-var eg = -1;
-function Vpe(e) {
-  HN = e;
+var QN = -1;
+var ng = -1;
+function the(e) {
+  QN = e;
 }
-function Wpe() {
-  if (eg === -1)
+function nhe() {
+  if (ng === -1)
     throw new Error("WASM backend not initialized.");
-  return eg;
+  return ng;
 }
-var Upe = "0.0.0";
-var Tde = 2;
-pp("wasm", async () => {
-  let { wasm: e } = await Sde();
-  return new kde(e);
-}, Tde);
-var sr = "3.15.0-20220405";
-var Gpe = { tfjs: sr, "tfjs-core": sr, "tfjs-data": sr, "tfjs-layers": sr, "tfjs-converter": sr, "tfjs-backend-cpu": sr, "tfjs-backend-webgl": sr, "tfjs-backend-wasm": sr };
+var she = "0.0.0";
+var Vde = 2;
+fp("wasm", async () => {
+  let { wasm: e } = await Mde();
+  return new Pde(e);
+}, Vde);
+var rr = "3.15.0-20220414";
+var rhe = { tfjs: rr, "tfjs-core": rr, "tfjs-data": rr, "tfjs-layers": rr, "tfjs-converter": rr, "tfjs-backend-cpu": rr, "tfjs-backend-webgl": rr, "tfjs-backend-wasm": rr };
 
 // src/image/imagefxshaders.ts
 var vertexIdentity = `
@@ -38481,9 +38619,9 @@ function GLImageFilter() {
 
 // src/image/enhance.ts
 async function histogramEqualization(inputImage) {
-  const squeeze = inputImage.shape.length === 4 ? mr(inputImage) : inputImage;
+  const squeeze = inputImage.shape.length === 4 ? gr(inputImage) : inputImage;
   const channels = Bn(squeeze, 3, 2);
-  const min = [fm(channels[0]), fm(channels[1]), fm(channels[2])];
+  const min = [gm(channels[0]), gm(channels[1]), gm(channels[2])];
   const max = [As(channels[0]), As(channels[1]), As(channels[2])];
   const absMax = await Promise.all(max.map((channel) => channel.data()));
   const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
@@ -38491,7 +38629,7 @@ async function histogramEqualization(inputImage) {
   const range = [ge(max[0], min[0]), ge(max[1], min[1]), ge(max[2], min[2])];
   const fact = [xe(maxValue, range[0]), xe(maxValue, range[1]), xe(maxValue, range[2])];
   const enh = [V(sub[0], fact[0]), V(sub[1], fact[1]), V(sub[2], fact[2])];
-  const rgb2 = Jn([enh[0], enh[1], enh[2]], 2);
+  const rgb2 = es([enh[0], enh[1], enh[2]], 2);
   const reshape = G(rgb2, [1, squeeze.shape[0], squeeze.shape[1], 3]);
   Re([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb2, squeeze]);
   return reshape;
@@ -38556,13 +38694,13 @@ async function process2(input, config3, getTensor = true) {
       if (input.shape[2] === 3) {
         tensor = Pn(input, 0);
       } else if (input.shape[2] === 4) {
-        const rgb2 = ob(input, [0, 0, 0], [-1, -1, 3]);
+        const rgb2 = lb(input, [0, 0, 0], [-1, -1, 3]);
         tensor = Pn(rgb2, 0);
         Re(rgb2);
       }
     } else if (input.shape.length === 4) {
       if (input.shape[3] === 3) {
-        tensor = lr(input);
+        tensor = cr(input);
       } else if (input.shape[3] === 4) {
         tensor = kd(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
       }
@@ -38683,8 +38821,8 @@ async function process2(input, config3, getTensor = true) {
     let pixels;
     let depth = 3;
     if (typeof ImageData !== "undefined" && input instanceof ImageData || input["data"] && input["width"] && input["height"]) {
-      if (env.browser && Nk) {
-        pixels = Nk ? Nk.fromPixels(input) : null;
+      if (env.browser && Ak) {
+        pixels = Ak ? Ak.fromPixels(input) : null;
       } else {
         depth = input["data"].length / input["height"] / input["width"];
         const arr = new Uint8Array(input["data"]["buffer"]);
@@ -38693,12 +38831,12 @@ async function process2(input, config3, getTensor = true) {
     } else {
       if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
         tmpCanvas = canvas(outCanvas.width, outCanvas.height);
-      if (Nk && env.browser) {
+      if (Ak && env.browser) {
         if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
-          pixels = Nk.fromPixels(outCanvas);
+          pixels = Ak.fromPixels(outCanvas);
         } else {
           tmpCanvas = copy(outCanvas);
-          pixels = Nk.fromPixels(tmpCanvas);
+          pixels = Ak.fromPixels(tmpCanvas);
         }
       } else {
         const tempCanvas = copy(outCanvas);
@@ -38710,7 +38848,7 @@ async function process2(input, config3, getTensor = true) {
       }
     }
     if (depth === 4) {
-      const rgb2 = ob(pixels, [0, 0, 0], [-1, -1, 3]);
+      const rgb2 = lb(pixels, [0, 0, 0], [-1, -1, 3]);
       Re(pixels);
       pixels = rgb2;
     }
@@ -38727,10 +38865,10 @@ async function skip(config3, input) {
   if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048)
     return skipFrame;
   if (!last.inputTensor) {
-    last.inputTensor = lr(input);
+    last.inputTensor = cr(input);
   } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
     Re(last.inputTensor);
-    last.inputTensor = lr(input);
+    last.inputTensor = cr(input);
   } else {
     const t = {};
     t.diff = ge(input, last.inputTensor);
@@ -38739,7 +38877,7 @@ async function skip(config3, input) {
     const diffSum = await t.sum.data();
     const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
     Re([last.inputTensor, t.diff, t.squared, t.sum]);
-    last.inputTensor = lr(input);
+    last.inputTensor = cr(input);
     skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
   }
   return skipFrame;
@@ -38756,8 +38894,8 @@ async function compare(config3, input1, input2) {
       log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
     return 0;
   }
-  t.input1 = lr(input1);
-  t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? qn.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : lr(input2);
+  t.input1 = cr(input1);
+  t.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? jn.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : cr(input2);
   t.diff = ge(t.input1, t.input2);
   t.squared = V(t.diff, t.diff);
   t.sum = ve(t.squared);
@@ -38808,7 +38946,7 @@ var Env = class {
     __publicField(this, "ImageData");
     this.browser = typeof navigator !== "undefined";
     this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
-    this.tfjs = { version: Gpe["tfjs-core"] };
+    this.tfjs = { version: rhe["tfjs-core"] };
     this.offscreen = typeof OffscreenCanvas !== "undefined";
     this.initial = true;
     this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
@@ -38831,7 +38969,7 @@ var Env = class {
     this.backends = Object.keys(Ss().registryFactory);
     this.wasm.supported = typeof WebAssembly !== "undefined";
     this.wasm.backend = this.backends.includes("wasm");
-    if (this.wasm.supported && this.wasm.backend && Vde() === "wasm") {
+    if (this.wasm.supported && this.wasm.backend && tpe() === "wasm") {
       this.wasm.simd = await X().getAsync("WASM_HAS_SIMD_SUPPORT");
       this.wasm.multithread = await X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
     }
@@ -38839,8 +38977,8 @@ var Env = class {
     const ctx = c ? c.getContext("webgl2") : void 0;
     this.webgl.supported = typeof ctx !== "undefined";
     this.webgl.backend = this.backends.includes("webgl");
-    if (this.webgl.supported && this.webgl.backend && (Vde() === "webgl" || Vde() === "humangl")) {
-      const gl2 = zA().gpgpu !== "undefined" ? await zA().getGPGPUContext().gl : null;
+    if (this.webgl.supported && this.webgl.backend && (tpe() === "webgl" || tpe() === "humangl")) {
+      const gl2 = UA().gpgpu !== "undefined" ? await UA().getGPGPUContext().gl : null;
       if (gl2) {
         this.webgl.version = gl2.getParameter(gl2.VERSION);
         this.webgl.renderer = gl2.getParameter(gl2.RENDERER);
@@ -38855,7 +38993,7 @@ var Env = class {
       this.webgpu.supported = false;
     }
     try {
-      this.kernels = Jf(Vde()).map((kernel) => kernel.kernelName.toLowerCase());
+      this.kernels = em(tpe()).map((kernel) => kernel.kernelName.toLowerCase());
     } catch (e) {
     }
   }
@@ -38895,7 +39033,7 @@ async function loadModel(modelPath) {
   const cachedModels = await En.listModels();
   const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
   const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init2) => httpHandler(url, init2) };
-  const model18 = new E4(modelCached ? cachedModelName : modelUrl, tfLoadOptions);
+  const model18 = new V4(modelCached ? cachedModelName : modelUrl, tfLoadOptions);
   let loaded = false;
   try {
     model18.findIOHandler();
@@ -38966,7 +39104,7 @@ async function predict(image, config3, idx, count2) {
       return;
     const t = {};
     const box = [[0, 0.1, 0.9, 0.9]];
-    t.resize = qn.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
+    t.resize = jn.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
     const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
     if ((_a3 = config3.face["gear"]) == null ? void 0 : _a3.enabled)
       [t.age, t.gender, t.race] = model.execute(t.resize, ["age_output", "gender_output", "race_output"]);
@@ -39041,7 +39179,7 @@ async function predict2(image, config3, idx, count2) {
     if (!(model2 == null ? void 0 : model2.inputs) || !model2.inputs[0] || !model2.inputs[0].shape)
       return;
     const t = {};
-    t.resize = qn.resizeBilinear(image, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false);
+    t.resize = jn.resizeBilinear(image, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false);
     t.enhance = V(t.resize, constants.tf255);
     const obj = { age: 0 };
     if (config3.face["ssrnet"].enabled)
@@ -39089,13 +39227,13 @@ async function predict3(image, config3, idx, count2) {
     if (!(model3 == null ? void 0 : model3.inputs[0].shape))
       return;
     const t = {};
-    t.resize = qn.resizeBilinear(image, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
+    t.resize = jn.resizeBilinear(image, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
     t.enhance = j(() => {
       const [red, green, blue] = Bn(t.resize, 3, 3);
       const redNorm = V(red, rgb[0]);
       const greenNorm = V(green, rgb[1]);
       const blueNorm = V(blue, rgb[2]);
-      const grayscale = KA([redNorm, greenNorm, blueNorm]);
+      const grayscale = eE([redNorm, greenNorm, blueNorm]);
       const normalize = V(ge(grayscale, constants.tf05), 2);
       return normalize;
     });
@@ -39141,7 +39279,7 @@ async function predict4(image, config3, idx, count2) {
   }
   skipped4 = 0;
   return new Promise(async (resolve) => {
-    const resize = qn.resizeBilinear(image, [(model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[2] : 0, (model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[1] : 0], false);
+    const resize = jn.resizeBilinear(image, [(model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[2] : 0, (model4 == null ? void 0 : model4.inputs[0].shape) ? model4.inputs[0].shape[1] : 0], false);
     const res = model4 == null ? void 0 : model4.execute(resize);
     const num = (await res.data())[0];
     cached[idx] = Math.round(100 * num) / 100;
@@ -42461,7 +42599,7 @@ var cutAndResize = (box, image, cropSize) => {
   const h = image.shape[1];
   const w10 = image.shape[2];
   const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w10, box.endPoint[1] / h, box.endPoint[0] / w10];
-  const crop = qn.cropAndResize(image, [cutBox], [0], cropSize);
+  const crop = jn.cropAndResize(image, [cutBox], [0], cropSize);
   const norm = xe(crop, constants.tf255);
   Re(crop);
   return norm;
@@ -42574,7 +42712,7 @@ function correctFaceRotation(rotate, box, input, inputSize10) {
     if (largeAngle) {
       const center = getBoxCenter(box);
       const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
-      const rotated = qn.rotateWithOffset(input, angle, 0, centerRaw);
+      const rotated = jn.rotateWithOffset(input, angle, 0, centerRaw);
       rotationMatrix = buildRotationMatrix(-angle, center);
       face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
       Re(rotated);
@@ -42619,14 +42757,14 @@ async function load5(config3) {
     log("cached model:", model5["modelUrl"]);
   inputSize = model5.inputs[0].shape ? model5.inputs[0].shape[2] : 0;
   inputSizeT = we(inputSize, "int32");
-  anchors = Ki(generateAnchors(inputSize));
+  anchors = Yi(generateAnchors(inputSize));
   return model5;
 }
 function decodeBounds(boxOutputs) {
   const t = {};
-  t.boxStarts = He(boxOutputs, [0, 1], [-1, 2]);
+  t.boxStarts = qe(boxOutputs, [0, 1], [-1, 2]);
   t.centers = ie(t.boxStarts, anchors);
-  t.boxSizes = He(boxOutputs, [0, 3], [-1, 2]);
+  t.boxSizes = qe(boxOutputs, [0, 3], [-1, 2]);
   t.boxSizesNormalized = xe(t.boxSizes, inputSizeT);
   t.centersNormalized = xe(t.centers, inputSizeT);
   t.halfBoxSize = xe(t.boxSizesNormalized, constants.tf2);
@@ -42634,7 +42772,7 @@ function decodeBounds(boxOutputs) {
   t.ends = ie(t.centersNormalized, t.halfBoxSize);
   t.startNormalized = V(t.starts, inputSizeT);
   t.endNormalized = V(t.ends, inputSizeT);
-  const boxes = UE([t.startNormalized, t.endNormalized], 1);
+  const boxes = XE([t.startNormalized, t.endNormalized], 1);
   Object.keys(t).forEach((tensor) => Re(t[tensor]));
   return boxes;
 }
@@ -42643,7 +42781,7 @@ async function getBoxes(inputImage, config3) {
   if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
     return [];
   const t = {};
-  t.resized = qn.resizeBilinear(inputImage, [inputSize, inputSize]);
+  t.resized = jn.resizeBilinear(inputImage, [inputSize, inputSize]);
   t.div = xe(t.resized, constants.tf127);
   t.normalized = ge(t.div, constants.tf05);
   const res = model5 == null ? void 0 : model5.execute(t.normalized);
@@ -42652,16 +42790,16 @@ async function getBoxes(inputImage, config3) {
     t.concat384 = Ft([sorted[0], sorted[2]], 2);
     t.concat512 = Ft([sorted[1], sorted[3]], 2);
     t.concat = Ft([t.concat512, t.concat384], 1);
-    t.batch = mr(t.concat, 0);
+    t.batch = gr(t.concat, 0);
   } else {
-    t.batch = mr(res);
+    t.batch = gr(res);
   }
   Re(res);
   t.boxes = decodeBounds(t.batch);
-  t.logits = He(t.batch, [0, 0], [-1, 1]);
+  t.logits = qe(t.batch, [0, 0], [-1, 1]);
   t.sigmoid = qs(t.logits);
-  t.scores = mr(t.sigmoid);
-  t.nms = await qn.nonMaxSuppressionAsync(t.boxes, t.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
+  t.scores = gr(t.sigmoid);
+  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.scores, ((_a2 = config3.face.detector) == null ? void 0 : _a2.maxDetected) || 0, ((_b2 = config3.face.detector) == null ? void 0 : _b2.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
   const nms = await t.nms.array();
   const boxes = [];
   const scores = await t.scores.data();
@@ -42669,9 +42807,9 @@ async function getBoxes(inputImage, config3) {
     const confidence = scores[nms[i]];
     if (confidence > (((_d2 = config3.face.detector) == null ? void 0 : _d2.minConfidence) || 0)) {
       const b = {};
-      b.bbox = He(t.boxes, [nms[i], 0], [1, -1]);
-      b.slice = He(t.batch, [nms[i], keypointsCount - 1], [1, -1]);
-      b.squeeze = mr(b.slice);
+      b.bbox = qe(t.boxes, [nms[i], 0], [1, -1]);
+      b.slice = qe(t.batch, [nms[i], keypointsCount - 1], [1, -1]);
+      b.squeeze = gr(b.slice);
       b.landmarks = G(b.squeeze, [keypointsCount, -1]);
       const points = await b.bbox.data();
       const rawBox = {
@@ -42871,7 +43009,7 @@ async function prepareImage(input, size2) {
     return input;
   let final;
   if (cropBox) {
-    t.cropped = qn.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
+    t.cropped = jn.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
   }
   if (input.shape[1] !== input.shape[2]) {
     const height = [
@@ -42888,11 +43026,11 @@ async function prepareImage(input, size2) {
       width,
       [0, 0]
     ];
-    t.pad = fi(t.cropped || input, padding);
-    t.resize = qn.resizeBilinear(t.pad, [size2, size2]);
+    t.pad = bi(t.cropped || input, padding);
+    t.resize = jn.resizeBilinear(t.pad, [size2, size2]);
     final = xe(t.resize, constants.tf255);
   } else if (input.shape[1] !== size2) {
-    t.resize = qn.resizeBilinear(t.cropped || input, [size2, size2]);
+    t.resize = jn.resizeBilinear(t.cropped || input, [size2, size2]);
     final = xe(t.resize, constants.tf255);
   } else {
     final = xe(t.cropped || input, constants.tf255);
@@ -43098,14 +43236,14 @@ async function process3(res, outputShape, config3) {
   const t = {};
   const results = [];
   const detections = await res.array();
-  t.squeeze = mr(res);
+  t.squeeze = gr(res);
   const arr = Bn(t.squeeze, 6, 1);
-  t.stack = Jn([arr[1], arr[0], arr[3], arr[2]], 1);
-  t.boxes = mr(t.stack);
-  t.scores = mr(arr[4]);
-  t.classes = mr(arr[5]);
+  t.stack = es([arr[1], arr[0], arr[3], arr[2]], 1);
+  t.boxes = gr(t.stack);
+  t.scores = gr(arr[4]);
+  t.classes = gr(arr[5]);
   Re([res, ...arr]);
-  t.nms = await qn.nonMaxSuppressionAsync(t.boxes, t.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
+  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
   const nms = await t.nms.data();
   let i = 0;
   for (const id2 of Array.from(nms)) {
@@ -43143,7 +43281,7 @@ async function predict6(input, config3) {
   skipped6 = 0;
   return new Promise(async (resolve) => {
     const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
-    const resize = qn.resizeBilinear(input, [inputSize4, inputSize4]);
+    const resize = jn.resizeBilinear(input, [inputSize4, inputSize4]);
     const objectT = config3.object.enabled ? model6 == null ? void 0 : model6.execute(resize, ["tower_0/detections"]) : null;
     lastTime6 = now();
     Re(resize);
@@ -43207,8 +43345,8 @@ async function max2d(inputs, minScore) {
   const newScore = (await max.data())[0];
   Re([reshaped, max]);
   if (newScore > minScore) {
-    const coordinates = Hu(reshaped, 0);
-    const mod = kD(coordinates, width);
+    const coordinates = qu(reshaped, 0);
+    const mod = TD(coordinates, width);
     const x = (await mod.data())[0];
     const div = xe(coordinates, we(width, "int32"));
     const y = (await div.data())[0];
@@ -43230,7 +43368,7 @@ async function predict7(image, config3) {
     const tensor = j(() => {
       if (!(model7 == null ? void 0 : model7.inputs[0].shape))
         return null;
-      const resize = qn.resizeBilinear(image, [model7.inputs[0].shape[2], model7.inputs[0].shape[1]], false);
+      const resize = jn.resizeBilinear(image, [model7.inputs[0].shape[2], model7.inputs[0].shape[1]], false);
       const enhance2 = V(resize, constants.tf2);
       const norm = ge(enhance2, constants.tf1);
       return norm;
@@ -43330,7 +43468,7 @@ async function predict8(image, config3, idx, count2) {
     if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) {
       const t = {};
       const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
-      t.resize = qn.resizeBilinear(image, [inputSize10, inputSize10], false);
+      t.resize = jn.resizeBilinear(image, [inputSize10, inputSize10], false);
       t.channels = V(t.resize, constants.rgb);
       t.grayscale = ve(t.channels, 3, true);
       t.grayscaleSub = ge(t.grayscale, constants.tf05);
@@ -43381,7 +43519,7 @@ async function predict9(input, config3, idx, count2) {
     let data = [];
     if (((_a3 = config3.face["embedding"]) == null ? void 0 : _a3.enabled) && (model9 == null ? void 0 : model9.inputs[0].shape)) {
       const t = {};
-      t.crop = qn.resizeBilinear(input, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
+      t.crop = jn.resizeBilinear(input, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
       t.data = model9 == null ? void 0 : model9.execute(t.crop);
       const output = await t.data.data();
       data = Array.from(output);
@@ -43446,14 +43584,14 @@ var getLeftToRightEyeDepthDifference = (rawCoords) => {
 var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
   const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
   const boxSize = getBoxSize(box);
-  let crop = qn.cropAndResize(face4, [[
+  let crop = jn.cropAndResize(face4, [[
     box.startPoint[1] / meshSize,
     box.startPoint[0] / meshSize,
     box.endPoint[1] / meshSize,
     box.endPoint[0] / meshSize
   ]], [0], [inputSize5, inputSize5]);
   if (flip && env.kernels.includes("flipleftright")) {
-    const flipped = qn.flipLeftRight(crop);
+    const flipped = jn.flipLeftRight(crop);
     Re(crop);
     crop = flipped;
   }
@@ -43559,7 +43697,7 @@ var cache3 = {
 var model11 = null;
 var inputSize6 = 0;
 async function predict10(input, config3) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2;
+  var _a2, _b2, _c, _d2, _e, _f, _g2, _h, _i, _j2;
   const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp;
   const skipFrame = cache3.skipped < (((_b2 = config3.face.detector) == null ? void 0 : _b2.skipFrames) || 0);
   if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
@@ -43588,7 +43726,7 @@ async function predict10(input, config3) {
       annotations: {}
     };
     [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d2 = config3.face.mesh) == null ? void 0 : _d2.enabled) ? inputSize6 : size());
-    if ((_e2 = config3 == null ? void 0 : config3.filter) == null ? void 0 : _e2.equalization) {
+    if ((_e = config3 == null ? void 0 : config3.filter) == null ? void 0 : _e.equalization) {
       const equilized = await histogramEqualization(face4.tensor);
       Re(face4.tensor);
       face4.tensor = equilized;
@@ -43683,7 +43821,7 @@ function enhance(input) {
   const tensor = input.image || input.tensor || input;
   if (!(model12 == null ? void 0 : model12.inputs[0].shape))
     return tensor;
-  const crop = qn.resizeBilinear(tensor, [model12.inputs[0].shape[2], model12.inputs[0].shape[1]], false);
+  const crop = jn.resizeBilinear(tensor, [model12.inputs[0].shape[2], model12.inputs[0].shape[1]], false);
   const norm = V(crop, constants.tf255);
   Re(crop);
   return norm;
@@ -43719,7 +43857,7 @@ async function predict11(image, config3, idx, count2) {
         obj.gender = gender[0] <= 0.5 ? "female" : "male";
         obj.genderScore = Math.min(0.99, confidence);
       }
-      const argmax = Hu(resT.find((t) => t.shape[1] === 100), 1);
+      const argmax = qu(resT.find((t) => t.shape[1] === 100), 1);
       const age = (await argmax.data())[0];
       Re(argmax);
       const ageT = resT.find((t) => t.shape[1] === 100);
@@ -43758,7 +43896,7 @@ function cutBoxFromImageAndResize(box, image, cropSize) {
     box.endPoint[1] / h,
     box.endPoint[0] / w10
   ]];
-  return qn.cropAndResize(image, boxes, [0], cropSize);
+  return jn.cropAndResize(image, boxes, [0], cropSize);
 }
 function scaleBoxCoordinates2(box, factor) {
   const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
@@ -46807,15 +46945,15 @@ var HandDetector = class {
     __publicField(this, "doubleInputSizeTensor");
     this.model = model18;
     this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
-    this.anchorsTensor = Ki(this.anchors);
+    this.anchorsTensor = Yi(this.anchors);
     this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0;
     this.inputSizeTensor = Zt([this.inputSize, this.inputSize]);
     this.doubleInputSizeTensor = Zt([this.inputSize * 2, this.inputSize * 2]);
   }
   normalizeBoxes(boxes) {
     const t = {};
-    t.boxOffsets = He(boxes, [0, 0], [-1, 2]);
-    t.boxSizes = He(boxes, [0, 2], [-1, 2]);
+    t.boxOffsets = qe(boxes, [0, 0], [-1, 2]);
+    t.boxSizes = qe(boxes, [0, 2], [-1, 2]);
     t.div = xe(t.boxOffsets, this.inputSizeTensor);
     t.boxCenterPoints = ie(t.div, this.anchorsTensor);
     t.halfBoxSizes = xe(t.boxSizes, this.doubleInputSizeTensor);
@@ -46823,7 +46961,7 @@ var HandDetector = class {
     t.startPoints = V(t.sub, this.inputSizeTensor);
     t.add = ie(t.boxCenterPoints, t.halfBoxSizes);
     t.endPoints = V(t.add, this.inputSizeTensor);
-    const res = UE([t.startPoints, t.endPoints], 1);
+    const res = XE([t.startPoints, t.endPoints], 1);
     Object.keys(t).forEach((tensor) => Re(t[tensor]));
     return res;
   }
@@ -46838,24 +46976,24 @@ var HandDetector = class {
   }
   async predict(input, config3) {
     const t = {};
-    t.resize = qn.resizeBilinear(input, [this.inputSize, this.inputSize]);
+    t.resize = jn.resizeBilinear(input, [this.inputSize, this.inputSize]);
     t.div = xe(t.resize, constants.tf127);
     t.image = ge(t.div, constants.tf1);
     t.batched = this.model.execute(t.image);
-    t.predictions = mr(t.batched);
-    t.slice = He(t.predictions, [0, 0], [-1, 1]);
+    t.predictions = gr(t.batched);
+    t.slice = qe(t.predictions, [0, 0], [-1, 1]);
     t.sigmoid = qs(t.slice);
-    t.scores = mr(t.sigmoid);
+    t.scores = gr(t.sigmoid);
     const scores = await t.scores.data();
-    t.boxes = He(t.predictions, [0, 1], [-1, 4]);
+    t.boxes = qe(t.predictions, [0, 1], [-1, 4]);
     t.norm = this.normalizeBoxes(t.boxes);
-    t.nms = await qn.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
+    t.nms = await jn.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
     const nms = await t.nms.array();
     const hands = [];
     for (const index2 of nms) {
       const p = {};
-      p.box = He(t.norm, [index2, 0], [1, -1]);
-      p.slice = He(t.predictions, [index2, 5], [1, 14]);
+      p.box = qe(t.norm, [index2, 0], [1, -1]);
+      p.slice = qe(t.predictions, [index2, 5], [1, 14]);
       p.norm = this.normalizeLandmarks(p.slice, index2);
       p.palmLandmarks = G(p.norm, [-1, 2]);
       const box = await p.box.data();
@@ -46966,7 +47104,7 @@ var HandPipeline = class {
         const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
         const palmCenter = getBoxCenter2(currentBox);
         const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
-        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? qn.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();
+        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? jn.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();
         const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
         const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
         const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
@@ -47574,25 +47712,25 @@ async function detectHands(input, config3) {
   const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);
   const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
   const width = Math.round(height * ratio / 8) * 8;
-  t.resize = qn.resizeBilinear(input, [height, width]);
+  t.resize = jn.resizeBilinear(input, [height, width]);
   t.cast = ce(t.resize, "int32");
   [t.rawScores, t.rawBoxes] = await models2[0].executeAsync(t.cast, modelOutputNodes);
-  t.boxes = mr(t.rawBoxes, [0, 2]);
-  t.scores = mr(t.rawScores, [0]);
+  t.boxes = gr(t.rawBoxes, [0, 2]);
+  t.scores = gr(t.rawScores, [0]);
   const classScores = Fs(t.scores, 1);
   Re(classScores[faceIndex]);
   classScores.splice(faceIndex, 1);
-  t.filtered = Jn(classScores, 1);
+  t.filtered = es(classScores, 1);
   Re(classScores);
   t.max = As(t.filtered, 1);
-  t.argmax = Hu(t.filtered, 1);
+  t.argmax = qu(t.filtered, 1);
   let id2 = 0;
-  t.nms = await qn.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  t.nms = await jn.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
   const nms = await t.nms.data();
   const scores = await t.max.data();
   const classNum = await t.argmax.data();
   for (const nmsIndex of Array.from(nms)) {
-    const boxSlice = He(t.boxes, nmsIndex, 1);
+    const boxSlice = qe(t.boxes, nmsIndex, 1);
     const boxYX = await boxSlice.data();
     Re(boxSlice);
     const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
@@ -47625,7 +47763,7 @@ async function detectFingers(input, h, config3) {
   if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
     const t = {};
     const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
-    t.crop = qn.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t.crop = jn.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
     t.div = xe(t.crop, constants.tf255);
     [t.score, t.keypoints] = models2[1].execute(t.div, ["Identity_1", "Identity"]);
     const rawScore = (await t.score.data())[0];
@@ -47719,7 +47857,7 @@ async function predict14(image, config3, idx, count2) {
   }
   skipped12 = 0;
   return new Promise(async (resolve) => {
-    const resize = qn.resizeBilinear(image, [(model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[2] : 0, (model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[1] : 0], false);
+    const resize = jn.resizeBilinear(image, [(model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[2] : 0, (model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[1] : 0], false);
     const res = model13 == null ? void 0 : model13.execute(resize);
     const num = (await res.data())[0];
     cached2[idx] = Math.round(100 * num) / 100;
@@ -47861,8 +47999,8 @@ function padInput(input, inputSize10) {
     [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
     [0, 0]
   ];
-  t.pad = fi(input, cache5.padding);
-  t.resize = qn.resizeBilinear(t.pad, [inputSize10, inputSize10]);
+  t.pad = bi(input, cache5.padding);
+  t.resize = jn.resizeBilinear(t.pad, [inputSize10, inputSize10]);
   const final = ce(t.resize, "int32");
   Object.keys(t).forEach((tensor) => Re(t[tensor]));
   return final;
@@ -48036,8 +48174,8 @@ async function process4(res, outputShape, config3) {
   for (const strideSize of [1, 2, 4]) {
     j(async () => {
       const baseSize = strideSize * 13;
-      const scoresT = mr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
-      const featuresT = mr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
+      const scoresT = gr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
+      const featuresT = gr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
       const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
       const boxIdx = await boxesMax.argMax(2).array();
       const scores = await scoresT.array();
@@ -48083,7 +48221,7 @@ async function process4(res, outputShape, config3) {
   const nmsScores = results.map((a) => a.score);
   let nmsIdx = [];
   if (nmsBoxes && nmsBoxes.length > 0) {
-    const nms = await qn.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    const nms = await jn.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
     nmsIdx = await nms.data();
     Re(nms);
   }
@@ -48102,7 +48240,7 @@ async function predict16(image, config3) {
     return last9;
   return new Promise(async (resolve) => {
     const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0];
-    const resize = qn.resizeBilinear(image, [inputSize9, inputSize9], false);
+    const resize = jn.resizeBilinear(image, [inputSize9, inputSize9], false);
     const norm = xe(resize, constants.tf255);
     const transpose = norm.transpose([0, 3, 1, 2]);
     Re(norm);
@@ -48431,10 +48569,10 @@ async function predict17(input, config3) {
   const res = j(() => {
     if (!model16.inputs[0].shape)
       return [];
-    const resized = qn.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+    const resized = jn.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
     const normalized = ge(xe(ce(resized, "float32"), 127.5), 1);
     const results = model16.execute(normalized, poseNetOutputs);
-    const results3d = results.map((y) => mr(y, [0]));
+    const results3d = results.map((y) => gr(y, [0]));
     results3d[1] = qs(results3d[1]);
     return results3d;
   });
@@ -48478,20 +48616,20 @@ async function process5(input, background, config3) {
   if (!inputImage.tensor)
     return { data: [], canvas: null, alpha: null };
   const t = {};
-  t.resize = qn.resizeBilinear(inputImage.tensor, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
+  t.resize = jn.resizeBilinear(inputImage.tensor, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false);
   Re(inputImage.tensor);
   t.norm = xe(t.resize, constants.tf255);
   t.res = model17.execute(t.norm);
-  t.squeeze = mr(t.res, 0);
+  t.squeeze = gr(t.res, 0);
   if (t.squeeze.shape[2] === 2) {
-    t.softmax = ub(t.squeeze);
+    t.softmax = cb(t.squeeze);
     [t.bg, t.fg] = Fs(t.softmax, 2);
     t.expand = Pn(t.fg, 2);
     t.pad = Pn(t.expand, 0);
-    t.crop = qn.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
-    t.data = mr(t.crop, 0);
+    t.crop = jn.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
+    t.data = gr(t.crop, 0);
   } else {
-    t.data = qn.resizeBilinear(t.squeeze, [height, width]);
+    t.data = jn.resizeBilinear(t.squeeze, [height, width]);
   }
   const data = Array.from(await t.data.data());
   if (env.node && !env.Canvas && typeof ImageData === "undefined") {
@@ -48501,8 +48639,8 @@ async function process5(input, background, config3) {
     return { data, canvas: null, alpha: null };
   }
   const alphaCanvas = canvas(width, height);
-  if (Nk)
-    await Nk.toPixels(t.data, alphaCanvas);
+  if (Ak)
+    await Ak.toPixels(t.data, alphaCanvas);
   const alphaCtx = alphaCanvas.getContext("2d");
   if (config3.segmentation.blur && config3.segmentation.blur > 0)
     alphaCtx.filter = `blur(${config3.segmentation.blur}px)`;
@@ -48567,7 +48705,7 @@ function reset(instance) {
     instance.models[model18] = null;
 }
 async function load19(instance) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2, _B2, _C2, _D2, _E2;
+  var _a2, _b2, _c, _d2, _e, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2, _B2, _C2, _D2, _E2;
   if (env.initial)
     reset(instance);
   if (instance.config.hand.enabled) {
@@ -48576,7 +48714,7 @@ async function load19(instance) {
     if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d2 = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d2.includes("handdetect")))
       [instance.models.handpose, instance.models.handskeleton] = await load13(instance.config);
   }
-  if (instance.config.body.enabled && !instance.models.blazepose && ((_f = (_e2 = instance.config.body) == null ? void 0 : _e2.modelPath) == null ? void 0 : _f.includes("blazepose")))
+  if (instance.config.body.enabled && !instance.models.blazepose && ((_f = (_e = instance.config.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("blazepose")))
     instance.models.blazepose = loadPose(instance.config);
   if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"]["modelPath"])
     instance.models.blazeposedetect = loadDetect(instance.config);
@@ -48684,7 +48822,7 @@ async function register(instance) {
     log("error: humangl backend invalid context");
     reset(instance);
   }
-  if (!Ude(config2.name)) {
+  if (!spe(config2.name)) {
     try {
       config2.canvas = await canvas(100, 100);
     } catch (err) {
@@ -48718,20 +48856,20 @@ async function register(instance) {
       return;
     }
     try {
-      S5(2, config2.gl);
+      F5(2, config2.gl);
     } catch (err) {
       log("error: cannot set WebGL context:", err);
       return;
     }
     try {
-      const ctx = new Kf(config2.gl);
-      pp(config2.name, () => new M1(ctx), config2.priority);
+      const ctx = new Xf(config2.gl);
+      fp(config2.name, () => new U1(ctx), config2.priority);
     } catch (err) {
       log("error: cannot register WebGL backend:", err);
       return;
     }
     try {
-      const kernels = Jf("webgl");
+      const kernels = em("webgl");
       kernels.forEach((kernelConfig) => {
         const newKernelConfig = { ...kernelConfig, backendName: config2.name };
         El(newKernelConfig);
@@ -48740,7 +48878,7 @@ async function register(instance) {
       log("error: cannot update WebGL backend registration:", err);
       return;
     }
-    const current = zA().getGPGPUContext ? zA().getGPGPUContext().gl : null;
+    const current = UA().getGPGPUContext ? UA().getGPGPUContext().gl : null;
     if (current) {
       log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);
     } else {
@@ -48748,7 +48886,7 @@ async function register(instance) {
       return;
     }
     try {
-      Jw.set("WEBGL_VERSION", 2);
+      sk.set("WEBGL_VERSION", 2);
     } catch (err) {
       log("error: cannot set WebGL backend flags:", err);
       return;
@@ -48763,7 +48901,7 @@ function registerCustomOps() {
   if (!env.kernels.includes("mod")) {
     const kernelMod = {
       kernelName: "Mod",
-      backendName: Vde(),
+      backendName: tpe(),
       kernelFunc: (op2) => j(() => ge(op2.inputs.a, V(xe(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
     };
     El(kernelMod);
@@ -48772,8 +48910,8 @@ function registerCustomOps() {
   if (!env.kernels.includes("floormod")) {
     const kernelMod = {
       kernelName: "FloorMod",
-      backendName: Vde(),
-      kernelFunc: (op2) => j(() => Hk(op2.inputs.a / op2.inputs.b) * op2.inputs.b + kD(op2.inputs.a, op2.inputs.b))
+      backendName: tpe(),
+      kernelFunc: (op2) => j(() => Xk(op2.inputs.a / op2.inputs.b) * op2.inputs.b + TD(op2.inputs.a, op2.inputs.b))
     };
     El(kernelMod);
     env.kernels.push("floormod");
@@ -48781,7 +48919,7 @@ function registerCustomOps() {
 }
 async function check(instance, force = false) {
   instance.state = "backend";
-  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && Vde() !== instance.config.backend) {
+  if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tpe() !== instance.config.backend) {
     const timeStamp = now();
     if (instance.config.backend && instance.config.backend.length > 0) {
       if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
@@ -48825,7 +48963,7 @@ async function check(instance, force = false) {
         if (instance.config.debug)
           log("wasm path:", instance.config.wasmPath);
         if (typeof (tfjs_esm_exports == null ? void 0 : tfjs_esm_exports.setWasmPaths) !== "undefined")
-          await Bpe(instance.config.wasmPath, instance.config.wasmPlatformFetch);
+          await ehe(instance.config.wasmPath, instance.config.wasmPlatformFetch);
         else
           throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
         const simd = await X().getAsync("WASM_HAS_SIMD_SUPPORT");
@@ -48836,35 +48974,35 @@ async function check(instance, force = false) {
           log("warning: wasm simd support is not enabled");
       }
       try {
-        await Lde(instance.config.backend);
-        await Bde();
+        await Jde(instance.config.backend);
+        await epe();
         init();
       } catch (err) {
         log("error: cannot set backend:", instance.config.backend, err);
         return false;
       }
     }
-    if (Vde() === "humangl") {
-      Jw.set("CHECK_COMPUTATION_FOR_ERRORS", false);
-      Jw.set("WEBGL_CPU_FORWARD", true);
-      Jw.set("WEBGL_USE_SHAPES_UNIFORMS", true);
-      Jw.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
+    if (tpe() === "humangl") {
+      sk.set("CHECK_COMPUTATION_FOR_ERRORS", false);
+      sk.set("WEBGL_CPU_FORWARD", true);
+      sk.set("WEBGL_USE_SHAPES_UNIFORMS", true);
+      sk.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
       if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) {
         log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
-        Jw.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
+        sk.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
       }
-      if (zA().getGPGPUContext) {
-        const gl2 = await zA().getGPGPUContext().gl;
+      if (UA().getGPGPUContext) {
+        const gl2 = await UA().getGPGPUContext().gl;
         if (instance.config.debug)
           log(`gl version:${gl2.getParameter(gl2.VERSION)} renderer:${gl2.getParameter(gl2.RENDERER)}`);
       }
     }
-    if (Vde() === "webgpu") {
+    if (tpe() === "webgpu") {
     }
-    Dde();
-    await Bde();
+    jde();
+    await epe();
     instance.performance.initBackend = Math.trunc(now() - timeStamp);
-    instance.config.backend = Vde();
+    instance.config.backend = tpe();
     await env.updateBackend();
     registerCustomOps();
   }
@@ -48882,7 +49020,7 @@ function fakeOps(kernelNames, config3) {
     };
     El(kernelConfig);
   }
-  env.kernels = Jf(Vde()).map((kernel) => kernel.kernelName.toLowerCase());
+  env.kernels = em(tpe()).map((kernel) => kernel.kernelName.toLowerCase());
 }
 
 // src/draw/draw.ts
@@ -49036,7 +49174,7 @@ function arrow(ctx, from, to2, radius = 5) {
 
 // src/draw/face.ts
 async function face(inCanvas2, result, drawOptions) {
-  var _a2, _b2, _c, _d2, _e2;
+  var _a2, _b2, _c, _d2, _e;
   const localOptions = mergeDeep(options3, drawOptions);
   if (!result || !inCanvas2)
     return;
@@ -49155,7 +49293,7 @@ async function face(inCanvas2, result, drawOptions) {
           ctx.stroke(pathH);
           ctx.stroke(pathV);
         }
-        if (localOptions.drawGaze && ((_c = (_b2 = f.rotation) == null ? void 0 : _b2.gaze) == null ? void 0 : _c.strength) && ((_e2 = (_d2 = f.rotation) == null ? void 0 : _d2.gaze) == null ? void 0 : _e2.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
+        if (localOptions.drawGaze && ((_c = (_b2 = f.rotation) == null ? void 0 : _b2.gaze) == null ? void 0 : _c.strength) && ((_e = (_d2 = f.rotation) == null ? void 0 : _d2.gaze) == null ? void 0 : _e.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
           ctx.strokeStyle = "pink";
           ctx.fillStyle = "pink";
           const leftGaze = [
@@ -49547,7 +49685,7 @@ var calculateFaceAngle = (face4, imageSize) => {
 
 // src/face/face.ts
 var detectFace = async (instance, input) => {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
+  var _a2, _b2, _c, _d2, _e, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
   let timeStamp = now();
   let ageRes;
   let gearRes;
@@ -49593,7 +49731,7 @@ var detectFace = async (instance, input) => {
     } else {
       instance.state = "run:antispoof";
       timeStamp = now();
-      antispoofRes = ((_e2 = instance.config.face.antispoof) == null ? void 0 : _e2.enabled) ? await predict4(faces[i].tensor || fs([]), instance.config, i, faces.length) : 0;
+      antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict4(faces[i].tensor || fs([]), instance.config, i, faces.length) : 0;
       instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
     }
     instance.analyze("End AntiSpoof:");
@@ -49676,7 +49814,7 @@ var detectFace = async (instance, input) => {
     if (!((_u2 = instance.config.face.iris) == null ? void 0 : _u2.enabled)) {
     }
     const irisSize = faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0] && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
-    const tensor = ((_v2 = instance.config.face.detector) == null ? void 0 : _v2.return) ? mr(faces[i].tensor) : null;
+    const tensor = ((_v2 = instance.config.face.detector) == null ? void 0 : _v2.return) ? gr(faces[i].tensor) : null;
     Re(faces[i].tensor);
     if (faces[i].tensor)
       delete faces[i].tensor;
@@ -49848,7 +49986,7 @@ var hand2 = (res) => {
 var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
 var interpolateTime = 0;
 function calc2(newResult, config3) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2;
+  var _a2, _b2, _c, _d2, _e, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2;
   const t02 = now();
   if (!newResult)
     return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
@@ -49865,7 +50003,7 @@ function calc2(newResult, config3) {
       const box = newResult.body[i].box.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j10] + newBoxCoord) / bufferedFactor);
       const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j10] + newBoxCoord) / bufferedFactor);
       const keypoints = newResult.body[i].keypoints.map((newKpt, j10) => {
-        var _a3, _b3, _c2, _d3, _e3, _f2, _g3, _h2, _i2;
+        var _a3, _b3, _c2, _d3, _e2, _f2, _g3, _h2, _i2;
         return {
           score: newKpt.score,
           part: newKpt.part,
@@ -49881,7 +50019,7 @@ function calc2(newResult, config3) {
           ],
           distance: [
             bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _a3[0]) || 0) + (((_b3 = newKpt.distance) == null ? void 0 : _b3[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
-            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _d3[1]) || 0) + (((_e3 = newKpt.distance) == null ? void 0 : _e3[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
+            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _d3[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
             bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _g3[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
           ]
         };
@@ -49892,7 +50030,7 @@ function calc2(newResult, config3) {
         coords = efficientposecoords_exports;
       else if ((_d2 = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d2.includes("blazepose"))
         coords = blazeposecoords_exports;
-      else if ((_f = (_e2 = config3.body) == null ? void 0 : _e2.modelPath) == null ? void 0 : _f.includes("movenet"))
+      else if ((_f = (_e = config3.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("movenet"))
         coords = movenetcoords_exports;
       for (const [name, indexes] of Object.entries(coords.connected)) {
         const pt2 = [];
@@ -50027,7 +50165,7 @@ function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25,
 
 // src/util/persons.ts
 function join2(faces, bodies, hands, gestures, shape) {
-  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2;
+  var _a2, _b2, _c, _d2, _e, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2;
   let id2 = 0;
   const persons = [];
   for (const face4 of faces) {
@@ -50056,7 +50194,7 @@ function join2(faces, bodies, hands, gestures, shape) {
         (_b2 = person2.gestures) == null ? void 0 : _b2.push(gesture2);
       else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_c = person2.body) == null ? void 0 : _c.id))
         (_d2 = person2.gestures) == null ? void 0 : _d2.push(gesture2);
-      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_f = (_e2 = person2.hands) == null ? void 0 : _e2.left) == null ? void 0 : _f.id))
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_f = (_e = person2.hands) == null ? void 0 : _e.left) == null ? void 0 : _f.id))
         (_g2 = person2.gestures) == null ? void 0 : _g2.push(gesture2);
       else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_i = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i.id))
         (_j2 = person2.gestures) == null ? void 0 : _j2.push(gesture2);
@@ -50972,7 +51110,7 @@ var Human = class {
         (_a2 = this.events) == null ? void 0 : _a2.dispatchEvent(new Event(event));
     });
     this.env = env;
-    config.wasmPath = Gpe["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Rde}/dist/`;
+    config.wasmPath = rhe["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${qde}/dist/`;
     config.modelBasePath = env.browser ? "../models/" : "file://models/";
     config.backend = env.browser ? "humangl" : "tensorflow";
     this.version = version;
@@ -51049,7 +51187,7 @@ var Human = class {
         log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
       if (!await check(this))
         log("error: backend check failed");
-      await Bde();
+      await epe();
       if (this.env.browser) {
         if (this.config.debug)
           log("configuration:", this.config);
@@ -51103,7 +51241,7 @@ var Human = class {
   async detect(input, userConfig) {
     this.state = "detect";
     return new Promise(async (resolve) => {
-      var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
+      var _a2, _b2, _c, _d2, _e, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
       this.state = "config";
       let timeStamp;
       this.config = mergeDeep(this.config, userConfig);
@@ -51174,7 +51312,7 @@ var Human = class {
           delete this.performance.body;
       } else {
         timeStamp = now();
-        if ((_e2 = this.config.body.modelPath) == null ? void 0 : _e2.includes("posenet"))
+        if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
           bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
         else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
           bodyRes = this.config.body.enabled ? await predict5(img.tensor, bodyConfig) : [];
@@ -51492,6 +51630,31 @@ export {
  * limitations under the License.
  * =============================================================================
  */
+/**
+ * @license
+ * Copyright 2022 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
 /**
  * @license
  * Copyright 2022 Google LLC. All Rights Reserved.
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index 21a53d5b..15562e8f 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/options.ts", "../src/draw/primitives.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar LT=Object.create,Vd=Object.defineProperty,BT=Object.getOwnPropertyDescriptor,Vw=Object.getOwnPropertyNames,VT=Object.getPrototypeOf,WT=Object.prototype.hasOwnProperty,UT=e=>Vd(e,\"__esModule\",{value:!0}),zt=(e,t)=>function(){return t||(0,e[Vw(e)[0]])((t={exports:{}}).exports,t),t.exports},Ae=(e,t)=>{for(var n in t)Vd(e,n,{get:t[n],enumerable:!0})},GT=(e,t,n,s)=>{if(t&&typeof t==\"object\"||typeof t==\"function\")for(let r of Vw(t))!WT.call(e,r)&&(n||r!==\"default\")&&Vd(e,r,{get:()=>t[r],enumerable:!(s=BT(t,r))||s.enumerable});return 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(ar(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new W$(this.backendInstance),!0}setupRegisteredKernels(){Jf(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Jf(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof nl)&&typeof n.then==\"function\"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof t!=\"function\")throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return tm.nextTensorId++}nextVariableId(){return tm.nextVariableId++}clone(e){let t=M.runKernel(Ba,{x:e}),n={x:e},s=a=>({x:()=>{let i=\"float32\",o={x:a},u={dtype:i};return M.runKernel(Na,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Zf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Of(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Of(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Zf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Of(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=nx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&ir(e[0])&&(r=e.map(o=>Dl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n===\"string\"){let o=this.state.tensorInfo.get(a),u=Xw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new gd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*Xf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*Xf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=nx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Hd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Rg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};gb.className=\"Adam\";Tr(gb);var bb=class extends _r{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=$r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};bb.className=\"Adamax\";Tr(bb);var Ip=class extends _r{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};yb.className=\"Momentum\";Tr(yb);var vb=class extends _r{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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adagrad(e,t=.1){return new mb(e,t)}},Pi={sgd:Wr.sgd,momentum:Wr.momentum,adadelta:Wr.adadelta,adagrad:Wr.adagrad,rmsprop:Wr.rmsprop,adamax:Wr.adamax,adam:Wr.adam},WO=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function OI(){return new Promise(e=>WO(()=>e()))}var 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},DL=0,Ge=class extends ae.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=DL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+\"_\"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s=\"float32\"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+\"_ib-\"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new hs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return bn(this.getNodeAtIndex(e,\"input\").inputTensors)}getOutputAt(e){return bn(this.getNodeAtIndex(e,\"output\").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");return ea(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return a}else{let a=FL(e),i=this.computeOutputShape(a),o,u=OL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of \"output shape\" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new hs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Cd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return vm(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer \"${this.name}\" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function mB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,\"input layer has >1 nodes\"),Cs(x===0,\"input layer has >1 tensors\"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Qo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let N=x.inboundNodes[k];if(v.indexOf(N)!==-1)throw new hs(`The tensor ${b.name} at layer \"${x.name}\" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let E=N.inboundLayers.length;for(let A=0;A<E;A++){let P=N.inputTensors[A],R=N.inboundLayers[A],F=N.nodeIndices[A],$=N.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);i.push(N)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],N=k.inboundNodes[T],E=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,E),n[N.id]=N}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new hs(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${v.name}\". 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Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split(\"/\");i=o.slice(0,-2).concat([o[o.length-1]]).join(\"/\")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Pb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${uS}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=wm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Qr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Au(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=fa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=fa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!BM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function lS(e,t){return gB(e,t,\"classWeight\")}async function cS(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Hu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,\"float32\")}else return null}function bB(e,t){return V(e,t)}var yB=32;function dS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Px(\"input\",e.inputNames,n),i=Px(\"output\",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  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Use LayersModel.compile(modelCompileArgs).\");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Lx(e,this.feedInputNames,this.feedInputShapes,!1,\"input\"),t=Lx(t,this.feedOutputNames,r,!1,\"target\"),TB(e,t,null),$B(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error(\"sample weight is not supported yet.\");let u=null;if(s!=null){let l=lS(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await cS(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,\"steps\"),i=[];if(s>0)throw new Fe(\"Verbose mode is not implemented yet.\");if(r!=null)throw new Fe(\"steps mode in testLoop() is not implemented yet\");{let o=Im(a,n),u=Zt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=ta(u,c,p-c),h=Wb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(we(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Sx(e,s)>1&&(r+=`_${Sx(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let p=new Qr(c),d=Au(this.outputs,p,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],d[f]);r[f]!=null&&(g=bB(g,r[f]));let b=St(g);t.push(b),f===0?h=g:h=ie(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=St(g(s[b],d[b]))}Ht(m),a.push(m)}return h=St(h),this.calculateLosses().forEach(f=>{h=ie(h,f)}),h},o=this.collectedTrainableWeights.map(c=>c.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Qr(a),o=Au(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=St(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=St(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return CB(this,e,t,n)}async fitDataset(e,t){return xB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ds(n[0],e),ds(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=dm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-dm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss==\"string\")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!=\"string\")throw new Error(\"Serialization of non-string loss is not supported.\");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]==\"string\")e[s]=Ws(n[s]);else throw new Error(\"Serialization of non-string loss is not supported.\")}return e}getMetricIdentifiers(){if(typeof this.metrics==\"string\"||typeof this.metrics==\"function\")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error(\"Loading weight_metrics is not supported yet.\");if(e.loss_weights!=null)throw new Error(\"Loading loss_weights is not supported yet.\");if(e.sample_weight_mode!=null)throw new Error(\"Loading sample_weight_mode is not supported yet.\");let t=Zu(e.optimizer_config),n=ms(t),s;if(typeof e.loss==\"string\")s=Kr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Kr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Kr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Kr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Kr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e==\"string\"){let u=En.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");let n=await En.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:AB,generatedBy:`TensorFlow.js tfjs-layers v${uS}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u=\"optimizer\",{data:l,specs:c}=await En.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=En.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Dx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Dx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className=\"Model\";ae.registerClass(pr);var hS=class extends pr{};hS.className=\"Functional\";ae.registerClass(hS);async function EB(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Zu(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function RB(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return DB(e,void 0,t)}async function DB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Zu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");let{modelWeights:l,optimizerWeights:c}=FB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function FB(e,t){let n=En.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group===\"optimizer\"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Cm=class extends pr{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp(\"sequential_\"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Cm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");if(n.inputs.length!==1)throw new U(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");let s=ZI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+\"_input\"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=QI(this.outputs[0])}this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:fa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError(\"There are no layers in the model.\");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className===\"Merge\")throw new U(\"Legacy serialization format not supported yet.\");r=t}else w.assert(t.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Cm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Ub=Cm;Ub.className=\"Sequential\";ae.registerClass(Ub);function Npe(e){return new pr(e)}function Tpe(e){return new Ub(e)}function $pe(e,t){return t==null&&(t={}),RB(e,t)}function OB(e){return ZI(e)}function _pe(e,t){zb.registerCallbackConstructor(e,t)}var In=class extends ae.Serializable{getConfig(){return{}}},fS=class extends In{apply(e,t=1){return aL(e,t)}};fS.className=\"elu\";ae.registerClass(fS);var mS=class extends In{apply(e){return gI(e)}};mS.className=\"selu\";ae.registerClass(mS);var gS=class extends In{apply(e){return Xs(e)}};gS.className=\"relu\";ae.registerClass(gS);var bS=class extends In{apply(e){return j(()=>bp(6,Xs(e)))}};bS.className=\"relu6\";ae.registerClass(bS);var yS=class extends In{apply(e){return e}};yS.className=\"linear\";ae.registerClass(yS);var vS=class extends In{apply(e){return qs(e)}};vS.className=\"sigmoid\";ae.registerClass(vS);var xS=class extends In{apply(e){return oL(e)}};xS.className=\"hardSigmoid\";ae.registerClass(xS);var wS=class extends In{apply(e){return zl(e)}};wS.className=\"softplus\";ae.registerClass(wS);var kS=class extends In{apply(e){return iL(e)}};kS.className=\"softsign\";ae.registerClass(kS);var IS=class extends In{apply(e){return qu(e)}};IS.className=\"tanh\";ae.registerClass(IS);var Gb=class extends In{apply(e,t=-1){return ub(e,t)}};Gb.className=\"softmax\";ae.registerClass(Gb);var SS=class extends In{apply(e,t=-1){return iI(e,t)}};SS.className=\"logSoftmax\";ae.registerClass(SS);var CS=class extends In{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};CS.className=\"swish\";ae.registerClass(CS);var NS=class extends In{apply(e){return j(()=>V(e,qu(zl(e))))}};NS.className=\"mish\";ae.registerClass(NS);function br(e){return e.getClassName()}function Gf(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,\"activation\")}function yr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},Gf(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},Gf(t)}else return e instanceof In?e:Gf(e)}function Hb(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var TS=class extends ae.Serializable{},Gl=class extends TS{constructor(e){super(),Hb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Wl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Gl.className=\"L1L2\";ae.registerClass(Gl);function PB(e){return Hb(e),new Gl({l1:e!=null?e.l1:null,l2:0})}function zB(e){return Hb(e),new Gl({l2:e!=null?e.l2:null,l1:0})}var Vx={l1l2:\"L1L2\"};function it(e){return wb(e)}function Wx(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ft(e){if(e==null)return null;if(typeof e==\"string\"){let n={className:e in Vx?Vx[e]:e,config:{}};return Wx(n)}else return e instanceof TS?e:Wx(e)}var qb=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};qb.className=\"ReLU\";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Xg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className=\"LeakyReLU\";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes==\"number\")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight(\"alpha\",t,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),sb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:it(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Kb.className=\"PReLU\";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Xb.className=\"ELU\";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Yb.className=\"ThresholdedReLU\";ae.registerClass(Yb);var Qb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Qb.className=\"Softmax\";ae.registerClass(Qb);function Xi(e,t,n){if(typeof e==\"number\")return fa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!tL(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function gs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n===\"same\"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+gr([n-t,0]);else if(s===\"same\")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function Zb(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?qe(e,[0,2,3,1]):e))}function $S(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?qe(e,[0,2,3,4,1]):e))}function MB(e,t,n,s=1,r=\"valid\",a,i=1){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=qe(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let o=Zk(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",i);return n!=null&&(o=ws(o,n)),o})}function Ux(e,t,n,s=[1,1],r=\"valid\",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zb(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=ha.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:n,activation:o}),a===\"channelsFirst\"&&(u=qe(u,[0,3,1,2])),u})}function LB(e,t,n,s=[1,1,1],r=\"valid\",a,i){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=$S(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return o=eI(o,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",i),n!=null&&(o=ws(o,n)),a===\"channelsFirst\"&&(o=qe(o,[0,4,1,2,3])),o})}var Jb=class extends Ge{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Jb.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,\"kernelSize\"),this.strides=Xi(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Ct(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hl=class extends Jb{constructor(e,t){super(e,t),this.kernel=null,Hl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=UI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=MB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_S=class extends Hl{constructor(e){super(2,e),_S.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=_S;zp.className=\"Conv2D\";ae.registerClass(zp);var AS=class extends Hl{constructor(e){super(3,e),AS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=AS;Mp.className=\"Conv3D\";ae.registerClass(Mp);var ey=class extends zp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat===\"channelsFirst\"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,1]));let g=Jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className=\"Conv2DTranspose\";ae.registerClass(ey);var ty=class extends Mp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat===\"channelsFirst\"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,4,1]));let k=tR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ty.className=\"Conv3DTranspose\";ae.registerClass(ty);var ES=class extends Hl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=qe(e,[0,2,3,1])),n=c3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};ES.className=\"SeparableConv\";var ny=class extends ES{constructor(e){super(2,e)}};ny.className=\"SeparableConv2D\";ae.registerClass(ny);var RS=class extends Hl{constructor(e){super(1,e),RS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!kb(e.kernelSize,\"number\",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},sy=RS;sy.className=\"Conv1D\";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e),typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className=\"Cropping2D\";ae.registerClass(ry);var ay=class extends Ge{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,ZM(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation===\"nearest\"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ay.className=\"UpSampling2D\";ae.registerClass(ay);function BB(e,t,n=[1,1],s=\"valid\",r,a){return j(()=>{r==null&&(r=bs()),Ct(r);let i=Zb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}var iy=class extends Jb{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=BB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};iy.className=\"DepthwiseConv2D\";ae.registerClass(iy);function DS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function FS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),r!=null&&(r=ce(ce(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=Pn(r,-1)),r=qe(r,l)),s&&(t=Zn(t,0),r!=null&&(r=Zn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Qn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Jn(c,1)),[p,g,d]})}var OS=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ym(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");ym(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let i={training:s},u=FS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Vl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?gm(t,[1,n]):t):this.cell.stateSize>1?[gm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===OS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Ar=OS;Ar.className=\"RNN\";ae.registerClass(Ar);var ql=class extends Ge{},Lp=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className=\"SimpleRNNCell\";ae.registerClass(Lp);var oy=class extends Ar{constructor(e){e.cell=new Lp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};oy.className=\"SimpleRNN\";ae.registerClass(oy);var Bp=class extends ql{constructor(e){if(super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new U(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className=\"GRUCell\";ae.registerClass(Bp);var uy=class extends Ar{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Bp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className=\"GRU\";ae.registerClass(uy);var jl=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};jl.className=\"LSTMCell\";ae.registerClass(jl);var ly=class extends Ar{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new jl(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ly.className=\"LSTM\";ae.registerClass(ly);var Vp=class extends ql{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){ym(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ea(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Pb(t)}};Vp.className=\"StackedRNNCells\";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):XI(t(),n),o=()=>Ul(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var PS=class extends Ar{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t===\"channelsFirst\",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};PS.className=\"ConvRNN2D\";var Wp=class extends jl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Xi(n,2,\"kernelSize\"),this.kernelSize.forEach(o=>Bt(o,\"kernelSize\")),this.strides=Xi(s||1,2,\"strides\"),this.strides.forEach(o=>Bt(o,\"strides\")),this.padding=r||\"valid\",Un(this.padding),this.dataFormat=a||\"channelsLast\",Ct(this.dataFormat),this.dilationRate=Xi(i||1,2,\"dilationRate\"),this.dilationRate.forEach(o=>Bt(o,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return $b([d,h,f])}},t.className=\"CustomInit\",t)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ca(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ca(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Wp.className=\"ConvLSTM2DCell\";ae.registerClass(Wp);var cy=class extends PS{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};cy.className=\"ConvLSTM2D\";ae.registerClass(cy);var Up=class extends Ge{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Ul(()=>XI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className=\"Dropout\";ae.registerClass(Up);var dy=class extends Up{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};dy.className=\"SpatialDropout1D\";ae.registerClass(dy);var py=class extends Ge{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=UI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};py.className=\"Dense\";ae.registerClass(py);var hy=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return rL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};hy.className=\"Flatten\";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};fy.className=\"Activation\";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),nL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};my.className=\"RepeatVector\";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U(\"Can only specifiy one unknown dimension.\");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};gy.className=\"Reshape\";ae.registerClass(gy);var by=class extends Ge{constructor(e){if(super(e),e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};by.className=\"Permute\";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return pm(Xu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=pm(Xu(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};yy.className=\"Masking\";ae.registerClass(yy);var vy=class extends Ge{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,\"inputDim\"),this.outputDim=e.outputDim,Bt(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Xu(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!==\"int32\"&&(n=Sp(n,\"int32\"));let s=KI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vy.className=\"Embedding\";ae.registerClass(vy);var bi=class extends Ge{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Vl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an Array\");if(!Array.isArray(e))throw new U(\"`inputs` should be an Array\");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},xy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};xy.className=\"Add\";ae.registerClass(xy);var wy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};wy.className=\"Multiply\";ae.registerClass(wy);var ky=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};ky.className=\"Average\";ae.registerClass(ky);var Iy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$r(t,e[n]);return t})}};Iy.className=\"Maximum\";ae.registerClass(Iy);var Sy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Sy.className=\"Minimum\";ae.registerClass(Sy);var Cy=class extends bi{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(e))}mergeFunction(e){return j(()=>$b(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U(\"A `Concatenate` layer should be called on a list of inputs.\");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an array for Concatenate\");if(!Array.isArray(e))throw new U(\"`inputs` should be an array for Concatenate\");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Qn(e[a]),\"bool\")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return qk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cy.className=\"Concatenate\";ae.registerClass(Cy);function Cu(e,t){for(;e<0;)e+=t;return e}function VB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Ny=class extends bi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Cu(r,e[a].shape.length)):s=[Cu(this.axes,t.shape.length),Cu(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),VB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cu(this.axes,e.length),Cu(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ny.className=\"Dot\";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Ul(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ty.className=\"GaussianNoise\";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Ul(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};$y.className=\"GaussianDropout\";ae.registerClass($y);var _y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ul(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Ko(Ml(n),this.rate);u=Sp(u,\"float32\");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};_y.className=\"AlphaDropout\";ae.registerClass(_y);function Ju(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=$E(e,t,n,s,r,a);else if(e.rank===3)i=AE(e,t,n,s,r,a);else if(e.rank===4)i=RE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function WB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance;return[Ju(e,i,o,n,t,r),i,o]})}function UB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Ju(e,l,c,d,p,r),i,o]})}function GB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?WB(e,t,n,s,r):UB(e,t,n,s,r)}var Ay=class extends Ge{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.movingMeanInitializer=ht(e.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||\"ones\"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=fa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Ju(s,b,y,v,x,this.epsilon)}else return Ju(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=GB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className=\"BatchNormalization\";ae.registerClass(Ay);var Ey=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=tb(n,this.axis,!0),u=fa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),Ju(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ey.className=\"LayerNormalization\";ae.registerClass(Ey);function HB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=bs()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n===\"channelsFirst\"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],fi(e,s)})}var Ry=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding==\"number\")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]==\"number\")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>HB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className=\"ZeroPadding2D\";ae.registerClass(Ry);function Gp(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=Zb(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=eb(e,t,n,o):i=Gg(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}function zS(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=$S(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=dI(e,t,n,o):i=Yk(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,4,1,2,3])),i})}var MS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Vl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Dy.className=\"MaxPooling1D\";ae.registerClass(Dy);var Fy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Fy.className=\"AveragePooling1D\";ae.registerClass(Fy);var LS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Oy.className=\"MaxPooling2D\";ae.registerClass(Oy);var Py=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Py.className=\"AveragePooling2D\";ae.registerClass(Py);var BS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,\"max\")}};zy.className=\"MaxPooling3D\";ae.registerClass(zy);var My=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,\"avg\")}};My.className=\"AveragePooling3D\";ae.registerClass(My);var VS=class extends Ge{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ly=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Ly.className=\"GlobalAveragePooling1D\";ae.registerClass(Ly);var By=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};By.className=\"GlobalMaxPooling1D\";ae.registerClass(By);var WS=class extends Ge{constructor(e){super(e),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?St(n,[1,2]):St(n,[2,3])})}};Vy.className=\"GlobalAveragePooling2D\";ae.registerClass(Vy);var Wy=class extends 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s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);Hn(r,t,\"TensorList shape mismatch: \");let a=Fs(e);return new eo(a,t,s)}function t4(e,t,n){return new eo([],e,t,n)}function n4(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r}  vs. ${s})`);let a=new eo([],n,e.dtype,s),i=Fs(e,0);return t.forEach((o,u)=>{a.setItem(o,i[u])}),a}function s4(e,t,n){let s=0,r=t.map(c=>(s+=c,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),i=Pm(a,n),o=s===0?0:e.size/s,u=j(()=>{let c=[];e=G(e,[1,s,o]);for(let p=0;p<t.length;++p){let d=p===0?0:r[p-1],h=[0,d,0],f=[1,t[p],o];c[p]=G(He(e,h,f),i)}return e.dispose(),c}),l=new eo([],n,e.dtype,t.length);for(let c=0;c<u.length;c++)l.setItem(c,u[c]);return l}var r4=async(e,t,n)=>{switch(e.op){case\"If\":case\"StatelessIf\":{let s=I(\"thenBranch\",e,t,n),r=I(\"elseBranch\",e,t,n),a=I(\"cond\",e,t,n),i=I(\"args\",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case\"While\":case\"StatelessWhile\":{let s=I(\"body\",e,t,n),r=I(\"cond\",e,t,n),a=I(\"args\",e,t,n),i=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),o=a.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&c.dispose()});let l=a;for(;u[0];){let c=l;l=await n.functionMap[s].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return l}case\"LoopCond\":{let s=I(\"pred\",e,t,n);return[Us(s)]}case\"Switch\":{let s=I(\"pred\",e,t,n),r=I(\"data\",e,t,n);return r.kept||(r=Us(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case\"Merge\":{let s=e.inputNames.find(r=>un(r,t,n)!==void 0);if(s){let r=un(s,t,n);return[Us(r)]}return}case\"Enter\":{let s=I(\"frameName\",e,t,n),r=I(\"tensor\",e,t,n);return n.enterFrame(s),[Us(r)]}case\"Exit\":{let s=I(\"tensor\",e,t,n);return n.exitFrame(),[Us(s)]}case\"NextIteration\":{let s=I(\"tensor\",e,t,n);return n.nextIteration(),[Us(s)]}case\"TensorArrayV3\":{let s=I(\"size\",e,t,n),r=I(\"dtype\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"dynamicSize\",e,t,n),o=I(\"clearAfterRead\",e,t,n),u=I(\"identicalElementShapes\",e,t,n),l=I(\"name\",e,t,n),c=new JW(l,r,s,a,u,i,o);return n.addTensorArray(c),[c.idTensor,we(1)]}case\"TensorArrayWriteV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case\"TensorArrayReadV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n);return[n.getTensorArray(s.id).read(r)]}case\"TensorArrayGatherV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"dtype\",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case\"TensorArrayScatterV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case\"TensorArrayConcatV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id),a=I(\"dtype\",e,t,n);return[r.concat(a)]}case\"TensorArraySplitV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"lengths\",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case\"TensorArraySizeV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return[we(r.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case\"TensorListSetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case\"TensorListGetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let s=I(\"indices\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"numElements\",e,t,n),o=n4(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let s=I(\"elementShape\",e,t,n),r=I(\"elementDType\",e,t,n),a;e.op===\"TensorListReserve\"?a=\"numElements\":a=\"maxNumElements\";let i=I(a,e,t,n),o=t4(s,r,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListGather\":{let s=I(\"tensorListId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case\"TensorListStack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=I(\"numElements\",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case\"TensorListFromTensor\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=e4(s,r,a);return n.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id),a=I(\"dtype\",e,t,n),i=I(\"elementShape\",e,t,n);return[r.concat(a,i)]}case\"TensorListPushBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"tensor\",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case\"TensorListPopBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"lengths\",e,t,n),i=s4(s,a,r);return n.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),\"int32\")]}case\"TensorListResize\":{let s=I(\"tensorListId\",e,t,n),r=I(\"size\",e,t,n),i=n.getTensorList(s.id).resize(r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function 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r=I(\"keyDType\",e,t,n),a=I(\"valueDType\",e,t,n),i=new c4(r,a);return s.addHashTable(e.name,i),[i.handle]}case\"LookupTableImport\":case\"LookupTableImportV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"values\",e,t,n);return[await s.getHashTableById(r.id).import(a,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"defaultValue\",e,t,n);return[await s.getHashTableById(r.id).find(a,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let r=I(\"tableHandle\",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},p4=(e,t,n)=>{switch(e.op){case\"ResizeBilinear\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[qn.resizeBilinear(s,[r[0],r[1]],a,i)]}case\"ResizeNearestNeighbor\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[qn.resizeNearestNeighbor(s,[r[0],r[1]],a,i)]}case\"CropAndResize\":{let s=I(\"image\",e,t,n),r=I(\"boxes\",e,t,n),a=I(\"boxInd\",e,t,n),i=I(\"cropSize\",e,t,n),o=I(\"method\",e,t,n),u=I(\"extrapolationValue\",e,t,n);return[qn.cropAndResize(s,r,a,i,o,u)]}case\"ImageProjectiveTransformV3\":{let s=I(\"images\",e,t,n),r=I(\"transforms\",e,t,n),a=I(\"outputShape\",e,t,n),i=I(\"fillValue\",e,t,n),o=I(\"interpolation\",e,t,n),u=I(\"fillMode\",e,t,n);return[qn.transform(s,r,o.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${e.op} is not 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implemented`)}},m4=(e,t,n)=>{switch(e.op){case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[ju(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"FusedBatchNormV3\":return[ju(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"LRN\":return[qR(I(\"x\",e,t,n),I(\"radius\",e,t,n),I(\"bias\",e,t,n),I(\"alpha\",e,t,n),I(\"beta\",e,t,n))];case\"Softmax\":return[ub(I(\"x\",e,t,n))];case\"LogSoftmax\":return[iI(I(\"x\",e,t,n))];case\"SparseToDense\":return[NI(I(\"sparseIndices\",e,t,n),I(\"outputShape\",e,t,n),I(\"sparseValues\",e,t,n),I(\"defaultValue\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},g4=(e,t,n)=>{switch(e.op){case\"Max\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[As(I(\"x\",e,t,n),i,o)]}case\"Mean\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[St(I(\"x\",e,t,n),i,o)]}case\"Min\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[fm(I(\"x\",e,t,n),i,o)]}case\"Sum\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[ve(I(\"x\",e,t,n),i,o)]}case\"All\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[qk(I(\"x\",e,t,n),i,o)]}case\"Any\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[pm(I(\"x\",e,t,n),i,o)]}case\"ArgMax\":{let i=I(\"axis\",e,t,n);return[Hu(I(\"x\",e,t,n),i)]}case\"ArgMin\":{let i=I(\"axis\",e,t,n);return[JA(I(\"x\",e,t,n),i)]}case\"Prod\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[pI(I(\"x\",e,t,n),i,o)]}case\"Cumprod\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[aR(I(\"x\",e,t,n),i,o,u)]}case\"Cumsum\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[sI(I(\"x\",e,t,n),i,o,u)]}case\"Bincount\":let s=I(\"x\",e,t,n),r=I(\"weights\",e,t,n),a=I(\"size\",e,t,n);return[Qk(s,r,a)];case\"DenseBincount\":{let i=I(\"x\",e,t,n),o=I(\"weights\",e,t,n),u=I(\"size\",e,t,n),l=I(\"binaryOutput\",e,t,n);return[uR(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},b4=(e,t,n)=>{switch(e.op){case\"ConcatV2\":case\"Concat\":{let s=I(\"n\",e,t,n),r=I(\"axis\",e,t,n),a=I(\"tensors\",e,t,n);return a=a.slice(0,s),[Ft(a,r)]}case\"Gather\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[Ku(s,ce(r,\"int32\"),0)]}case\"GatherV2\":{let s=I(\"axis\",e,t,n),r=I(\"batchDims\",e,t,n),a=I(\"x\",e,t,n),i=I(\"indices\",e,t,n);return[Ku(a,ce(i,\"int32\"),s,r)]}case\"Reverse\":{let s=I(\"dims\",e,t,n),r=[];for(let i=0;i<s.length;i++)s[i]&&r.push(i);let a=I(\"x\",e,t,n);return[Zn(a,r)]}case\"ReverseV2\":{let s=I(\"axis\",e,t,n),r=I(\"x\",e,t,n);return[Zn(r,s)]}case\"Slice\":{let s=I(\"begin\",e,t,n),r=I(\"size\",e,t,n);return[He(I(\"x\",e,t,n),s,r)]}case\"StridedSlice\":{let s=I(\"begin\",e,t,n),r=I(\"end\",e,t,n),a=I(\"strides\",e,t,n),i=I(\"beginMask\",e,t,n),o=I(\"endMask\",e,t,n),u=I(\"ellipsisMask\",e,t,n),l=I(\"newAxisMask\",e,t,n),c=I(\"shrinkAxisMask\",e,t,n),p=I(\"x\",e,t,n);return[D3(p,s,r,a,i,o,u,l,c)]}case\"Pack\":return j(()=>{let s=I(\"axis\",e,t,n),r=I(\"tensors\",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error(\"the input tensors shape does not match\");return l?u:G(u,a)});return[Jn(o,s)]});case\"Unpack\":{let s=I(\"axis\",e,t,n),r=I(\"tensor\",e,t,n);return Fs(r,s)}case\"Tile\":{let s=I(\"reps\",e,t,n);return[ps(I(\"x\",e,t,n),s)]}case\"Split\":case\"SplitV\":{let s=I(\"axis\",e,t,n),r=I(\"numOrSizeSplits\",e,t,n),a=I(\"x\",e,t,n);return Bn(a,r,s)}case\"ScatterNd\":{let s=I(\"indices\",e,t,n),r=I(\"values\",e,t,n),a=I(\"shape\",e,t,n);return[X3(s,r,a)]}case\"GatherNd\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[J3(s,r)]}case\"SparseToDense\":{let s=I(\"sparseIndices\",e,t,n),r=I(\"outputShape\",e,t,n),a=I(\"sparseValues\",e,t,n),i=I(\"defaultValue\",e,t,n);return[NI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},y4=(e,t,n)=>{switch(e.op){case\"SparseFillEmptyRows\":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Wc.sparseFillEmptyRows(I(\"indices\",e,t,n),I(\"values\",e,t,n),I(\"denseShape\",e,t,n),I(\"defaultValue\",e,t,n));return[s,r,a,i]}case\"SparseReshape\":{let{outputIndices:s,outputShape:r}=Wc.sparseReshape(I(\"inputIndices\",e,t,n),I(\"inputShape\",e,t,n),I(\"newShape\",e,t,n));return[s,r]}case\"SparseSegmentMean\":return[Wc.sparseSegmentMean(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];case\"SparseSegmentSum\":return[Wc.sparseSegmentSum(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},v4=(e,t,n)=>{switch(e.op){case\"FFT\":return[lb(I(\"x\",e,t,n))];case\"IFFT\":return[Id(I(\"x\",e,t,n))];case\"RFFT\":return[cb(I(\"x\",e,t,n))];case\"IRFFT\":return[xI(I(\"x\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},x4=(e,t,n)=>{switch(e.op){case\"StringNGrams\":{let{nGrams:s,nGramsSplits:r}=Mf.stringNGrams(I(\"data\",e,t,n),I(\"dataSplits\",e,t,n),I(\"separator\",e,t,n),I(\"nGramWidths\",e,t,n),I(\"leftPad\",e,t,n),I(\"rightPad\",e,t,n),I(\"padWidth\",e,t,n),I(\"preserveShortSequences\",e,t,n));return[s,r]}case\"StringSplit\":{let{indices:s,values:r,shape:a}=Mf.stringSplit(I(\"input\",e,t,n),I(\"delimiter\",e,t,n),I(\"skipEmpty\",e,t,n));return[s,r,a]}case\"StringToHashBucketFast\":return[Mf.stringToHashBucketFast(I(\"input\",e,t,n),I(\"numBuckets\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},w4=(e,t,n)=>{switch(e.op){case\"Cast\":return[ce(I(\"x\",e,t,n),I(\"dtype\",e,t,n))];case\"ExpandDims\":{let s=I(\"axis\",e,t,n);return[Pn(I(\"x\",e,t,n),s)]}case\"Squeeze\":{let s=I(\"axis\",e,t,n);return[mr(I(\"x\",e,t,n),s)]}case\"Reshape\":return[G(I(\"x\",e,t,n),I(\"shape\",e,t,n))];case\"MirrorPad\":return[xD(I(\"x\",e,t,n),I(\"padding\",e,t,n),I(\"mode\",e,t,n))];case\"PadV2\":case\"Pad\":return[fi(I(\"x\",e,t,n),I(\"padding\",e,t,n),I(\"constantValue\",e,t,n))];case\"SpaceToBatchND\":{let s=I(\"blockShape\",e,t,n),r=I(\"paddings\",e,t,n);return[nb(I(\"x\",e,t,n),s,r)]}case\"BatchToSpaceND\":{let s=I(\"blockShape\",e,t,n),r=I(\"crops\",e,t,n);return[Hg(I(\"x\",e,t,n),s,r)]}case\"DepthToSpace\":{let s=I(\"blockSize\",e,t,n),r=I(\"dataFormat\",e,t,n).toUpperCase();return[cR(I(\"x\",e,t,n),s,r)]}case\"BroadcastTo\":return[nd(I(\"x\",e,t,n),I(\"shape\",e,t,n))];case\"BroadcastArgs\":return[OE(I(\"s0\",e,t,n),I(\"s1\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Yx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case\"arithmetic\":return j(()=>QW(a,i,o));case\"basic_math\":return j(()=>ZW(a,i,o));case\"control\":return r4(a,i,o);case\"convolution\":return j(()=>a4(a,i,o));case\"creation\":return j(()=>i4(a,i,o));case\"dynamic\":return o4(a,i,o);case\"evaluation\":return j(()=>u4(a,i,o));case\"image\":return j(()=>p4(a,i,o));case\"graph\":return j(()=>l4(a,i,o));case\"logical\":return j(()=>h4(a,i,o));case\"matrices\":return j(()=>f4(a,i,o));case\"normalization\":return j(()=>m4(a,i,o));case\"reduction\":return j(()=>g4(a,i,o));case\"slice_join\":return j(()=>b4(a,i,o));case\"sparse\":return j(()=>y4(a,i,o));case\"spectral\":return j(()=>v4(a,i,o));case\"string\":return j(()=>x4(a,i,o));case\"transformation\":return j(()=>w4(a,i,o));case\"hash_table\":return d4(a,i,o,s);case\"custom\":let u=XS(a.op);if(u&&u.customExecutor)return u.customExecutor(new YW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Qx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(\"\"),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Zx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((b0(d)||N4(d)||T4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function k4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var I4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],S4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],C4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function b0(e){return I4.indexOf(e.op)>=0}function N4(e){return S4.indexOf(e.op)>=0}function T4(e){return C4.indexOf(e.op)>=0}var zm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new zm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=Zx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return k4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[An(c)[0]]),r=t.map(c=>An(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Qx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=An(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=Yx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>un(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!==\"control\"){let u=_W(o.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=i[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete i[l.id]}else c!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new Qx(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(l=>un(l,this.tensorsMap,a)),o=i.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[An(y)[0]]),i=n.map(y=>An(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=Zx(e,o,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=An(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,i,f,u);await Promise.all(y)}c==null&&!s&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=o.filter(y=>!b0(y)&&!un(y.name,h,t)).map(y=>y.name);if(b.length>0){let y=\"\";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,s,r,a,i,o,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p=\"\";if(c.node.op===\"Enter\"&&I(\"isConstant\",c.node,s,n)&&([p]=Ts(c.node.name,n)),s[c.node.name]==null){let d=Yx(c.node,s,n,this._resourceManager);p||([p]=Ts(c.node.name,n));let h=n.currentContext;w.isPromise(d)?l.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u))}else this.processChildNodes(c.node,t,n,s,r,u)}return l}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Ts(i.name,n);r[o]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=An(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let 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nU(e){if(!to(e))throw new Error(\"The argument to zip() must be an object or array.\");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return _n(async()=>{let n=await x0(e,s=>{if(s instanceof Jo)return{value:s.iterator(),recurse:!1};if(to(s))return{value:null,recurse:!0};throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\")});return W4(n,1)},t)}function sU(e){if(e===null)return null;let t=e[0];return z4(t)?{value:rU(e),recurse:!1}:{value:null,recurse:!0}}function rU(e){if(e.length===0)throw new Error(\"Can't make a batch of zero elements.\");return e[0]instanceof et?Jn(e):fs(e)}var $0=class extends Jo{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`\n`).map(s=>(s.endsWith(\"\\r\")&&(s=s.slice(0,-1)),s))}},Kc='\"',Tu=Symbol(\"out\"),ew=Symbol(\"field\"),Xc=Symbol(\"quote\"),qf=Symbol(\"quoteafterquote\"),tw=Symbol(\"quoteinquote\"),_0=class extends Jo{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=\",\",this.delimWhitespace=!1,this.base=new $0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>\"Delimiter should not be provided when delimWhitespace is true.\"),this.delimWhitespace=!0,this.delimiter=\" \"):this.delimiter=t.delimiter?t.delimiter:\",\"}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error(\"Column names must be provided if there is no header line.\");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>\"The length of provided columnNames (\"+this.fullColumnNames.length.toString()+\") does not match the length of the header line read from file (\"+e.length.toString()+\").\"),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>\"Duplicate column names found: \"+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key \"'+s+'\" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+\").\")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error(\"No data was found for CSV parsing.\");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o===\"\")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype===\"bool\"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else 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Kc:a=Xc;break;default:}break;default:}if(a===qf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new A0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},E0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new E0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Nk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: 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D0{constructor(e,t){super(),this.upstream=e,this.impl=new iU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},iU=class extends Qy{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oU=class extends Ut{decodeUTF8(){return new uU(this)}},uU=class extends D0{constructor(e){super(),this.upstream=e,this.impl=new lU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lU=class extends Qy{constructor(e){if(super(),this.upstream=e,X().get(\"IS_BROWSER\"))this.decoder=new TextDecoder(\"utf-8\");else{let{StringDecoder:t}=Ww();this.decoder=new t(\"utf8\")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get(\"IS_BROWSER\")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},F0=class extends oU{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get(\"IS_BROWSER\")?e instanceof File||e instanceof Blob:!1),()=>\"FileChunkIterator only supports File, Blob and Uint8Array right now.\"),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new 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iterator(){if(O0(this.input)&&X().get(\"IS_NODE\")){let e=tg();this.input=e.readFileSync(this.input.slice(7))}return new F0(this.input,this.options)}},z0=class extends R0{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return O0(this.url)?new P0(this.url,this.fileOptions).iterator():cU(this.url,this.fileOptions)}};function pU(e,t={}){return new _0(new z0(e),t)}function hU(e){let t=Yy(e);return _n(async()=>t)}function fU(e){return _n(async()=>{let t=await e();return Yy(()=>t.next())})}async function mU(e,t){return E0.create(e,t)}async function gU(e){return A0.create(e)}var bU=\"0.0.0\";function be(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!==\"complex64\",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var yU=xs.whereImpl,M0=class extends nl{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ud(this,Ss())}nextDataId(){return M0.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get(\"IS_NODE\")&&S.warn(`\n============================\nHi there \\u{1F44B}. 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aH={kernelName:og,backendName:\"cpu\",kernelFunc:rH};function iH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;be([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=De(i.shape,\"float32\"),T=1/(h*f),N=n.data.get(r.dataId).values,E=De(r.shape,\"float32\",N);for(let A=0;A<c.batchSize;++A)for(let P=0;P<c.inChannels;++P)for(let R=0;R<c.inHeight;++R)for(let F=0;F<c.inWidth;++F){let $=R-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/p;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Y=0;Y<y;Y+=g){let Z=(z+Y)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(A,K,Z,P)}}k.set(W*T,A,R,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var oH={kernelName:ig,backendName:\"cpu\",kernelFunc:iH};function 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p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,b=d.padInfo.left,y=d.padInfo.top,v=d.dataFormat===\"channelsLast\",x=new Vt(d.outShape,r.dtype),k=w.computeStrides(r.shape),T=w.computeStrides(a.shape),N=k[0],E=v?k[1]:k[2],A=v?k[2]:1,P=v?1:k[1],R=x.strides[0],F=v?x.strides[1]:x.strides[2],$=v?x.strides[2]:1,z=v?1:x.strides[1],W=n.data.get(r.dataId).values,q=n.data.get(a.dataId).values,K=x.values;for(let Y=0;Y<d.batchSize;++Y){let Z=Y*N,te=Y*R;for(let ee=0;ee<d.outHeight;++ee){let se=te+ee*F,ne=ee*d.strideHeight-y;for(let oe=0;oe<h;++oe){let re=ne+oe*m;if(re<0||re>=d.inHeight)continue;let le=oe*T[0],me=Z+re*E;for(let ke=0;ke<d.outWidth;++ke){let Se=se+ke*$,Ee=ke*d.strideWidth-b;for(let Pe=0;Pe<f;++Pe){let Xe=Ee+Pe*g;if(Xe<0||Xe>=d.inWidth)continue;let Je=le+Pe*T[1],Ye=me+Xe*A,tt=Je;for(let Ce=0;Ce<d.inChannels;++Ce){let ut=W[Ye+Ce*P];for(let 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Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=N.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=N.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var SH={kernelName:cg,backendName:\"cpu\",kernelFunc:IH};function CH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],\"conv2dBackpropInput\");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=S.convertConv2DDataFormat(l),f=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,\"float32\"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:T,filterHeight:N,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=N-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h===\"channelsLast\",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<T;++ke)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let Pe=Ee-K,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min($,(N+Pe)/W);for(let Ye=0;Ye<R;++Ye){let tt=Ye-Y,Ce=Math.max(0,Math.ceil(tt/q)),ut=Math.min(z,(E+tt)/q),at=0;for(let Nt=Xe;Nt<Je;++Nt){let Cn=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let en=Et*q-tt,Nn=oe*ke+re*Nt+le*Et,Tn=v*(N-1-Cn)+x*(E-1-en)+k*Se;for(let Yt=0;Yt<F;++Yt){let Dn=b[Nn+me*Yt],tn=y[Tn+Yt];at+=Dn*tn}}}let Jt=te*ke+ee*Ee+se*Ye+ne*Se;g[Jt]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var NH={kernelName:_a,backendName:\"cpu\",kernelFunc:CH};function TH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;be([r,a],\"conv3d\");let l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,N=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let R=P*E[0],F=P*x.strides[0];for(let $=0;$<l.outDepth;++$){let z=F+$*x.strides[1],W=$*l.strideDepth-b;for(let q=0;q<c;++q){let K=W+q*h;if(K<0||K>=l.inDepth)continue;let Y=q*A[0],Z=R+K*E[1];for(let te=0;te<l.outHeight;++te){let ee=z+te*x.strides[2],se=te*l.strideHeight-v;for(let ne=0;ne<p;++ne){let oe=se+ne*f;if(oe<0||oe>=l.inHeight)continue;let re=Y+ne*A[1],le=Z+oe*E[2];for(let me=0;me<l.outWidth;++me){let ke=ee+me*l.outChannels,Se=me*l.strideWidth-y;for(let Ee=0;Ee<d;++Ee){let Pe=Se+Ee*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=re+Ee*A[2],Je=le+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)N[ke+ut]+=Ce*T[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var $H={kernelName:Xd,backendName:\"cpu\",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;be([r,a],\"conv3dBackpropFilterV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=S.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,\"float32\"),v=y.values,[x,k,T,N]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),ke=re*k+oe;for(let Se=0;Se<b;++Se){let 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MH={kernelName:co,backendName:\"cpu\",kernelFunc:zH};function LH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumprod\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var BH={kernelName:hl,backendName:\"cpu\",kernelFunc:LH};function VH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumsum\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var WH={kernelName:lo,backendName:\"cpu\",kernelFunc:VH};function UH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=ev(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=W0(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var GH={kernelName:hg,backendName:\"cpu\",kernelFunc:UH};function HH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:p1}=S;function V5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=mv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 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n=e.shapeInfo.logicalShape;switch(n.length){case 0:return cK(e,t);case 1:return pK(e,t);case 2:return fK(e,t);case 3:return gK(e,t);case 4:return yK(e,t);case 5:return vK(e);case 6:return xK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function h1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return lK(e);case 1:return dK(e,t);case 2:return hK(e,t);case 3:return mK(e,t);default:return bK(e,t)}}function W5(e,t,n=!1,s){let r=\"\";n?r+=h1(e,s):r+=su(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=wK(e,t):r+=kK(e,t)),r}function U5(e,t,n){switch(e.length){case 0:return f1();case 1:return J5(e,t,n);case 2:return oK(e,t,n);case 3:return tK(e,t,n);default:return sK(e,t,n)}}function G5(e,t,n){switch(e.length){case 0:return f1();case 1:return eK(e,t,n);case 2:return uK(e,t,n);case 3:return nK(e,t,n);case 4:return rK(e,t,n);case 5:return aK(e,t);case 6:return iK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function H5(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function q5(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function j5(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function K5(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${X5}\n    ${Y5}\n    ${Q5}\n  `}var X5=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, 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modCoord == 0. ? frag.r : frag.g;\n  }\n`;function f1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function J5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function eK(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function tK(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function nK(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${qp([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=yi([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function sK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`\n      int b${l} = index / ${i};\n      index -= b${l} * ${i};\n    `+o,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function rK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${qp([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=yi([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function aK(e,t){let n=yi([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function iK(e,t){let n=yi([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function oK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function uK(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function vi(e){return`offset${e}`}function lK(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function cK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=vi(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${u}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function dK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function pK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${ru(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=vi(n);return i===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function hK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function fK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=au(e,u),h=[\"row\",\"col\"];return`\n      ${su(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${iu(h,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${ru(e)}\n      }\n    `;let l=a[0],c=a[1],p=vi(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function mK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=au(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${h1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${iu(m,h)});\n        }\n      `}let o=fn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}function gK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=au(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${su(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${iu(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===i&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=vi(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${i} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function bK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function yK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=au(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${su(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${iu(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=vi(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function vK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=au(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${su(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${iu(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=vi(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function xK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=au(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${su(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${iu(b,a)});\n      }\n    `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${ru(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=vi(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function ru(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function wK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=p1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(i),l=i-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":i<2&&o.length>=1?c=\"coords = 0;\":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";i<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)i===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":o.indexOf(y)>-1?h=\"return vec4(outputValue.x, 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c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=V5(r,i,t),u=GC(e.gl,o),l=e.createProgram(u);return X().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...m1(e,t,l)}}function m1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,\"NAN\",!1),X().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(n,\"INFINITY\",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,\"outShape\",d),l=e.getUniformLocation(n,\"outShapeStrides\",d),u=e.getUniformLocation(n,\"outTexShape\",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function rw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function SK(e,t,n,s,r){t.program.enableShapeUniforms||(rw(t.inShapeInfos,n),rw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=mv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new 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4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function CK(e,t,n){let s=\"\";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=mv(e.packedInputs,i.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${X().getNumber(\"WEBGL_VERSION\")}`,a}function Sn(e){return X().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var NK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):yi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}},TK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):yi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}},$K=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${d1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},_K=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${d1}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}},AK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?fv():hv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},EK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?\"outShape[2]\":`${e[2]}`}) {\n          localCoords[2] += ${i};\n          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?\"outShape[1]\":`${e[1]}`}) {\n            localCoords[1] += ${a};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${o}] = values[0];\n            } else if (offset == 1) {\n              result[${o}] = values[1];\n            } else if (offset == 2) {\n              result[${o}] = values[2];\n            } else {\n              result[${o}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?fv():hv(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${s}\n\n          ${n.output} = ${r};\n        }\n    `}},RK={};Ae(RK,{bindVertexProgramAttributeStreams:()=>S1,createBufferFromOutputTexture:()=>T1,createFloat16MatrixTexture:()=>x1,createFloat16PackedMatrixTexture:()=>I1,createFloat32MatrixTexture:()=>v1,createIndexBuffer:()=>y1,createPackedMatrixTexture:()=>k1,createUnsignedBytesMatrixTexture:()=>w1,createVertexBuffer:()=>b1,createVertexShader:()=>g1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>_1,downloadFloat32MatrixFromBuffer:()=>$1,downloadMatrixFromPackedOutputTexture:()=>E1,downloadPackedMatrixFromBuffer:()=>A1,getInternalFormatForFloat16MatrixTexture:()=>bv,getInternalFormatForFloat16PackedMatrixTexture:()=>xv,getInternalFormatForFloat32MatrixTexture:()=>gv,getInternalFormatForPackedMatrixTexture:()=>vv,getInternalFormatForUnsignedBytesMatrixTexture:()=>yv,uploadDenseMatrixToTexture:()=>C1,uploadPixelDataToTexture:()=>N1});function g1(e){let t=fn(),n=`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    ${t.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;return UC(e,n)}function b1(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return jC(e,t)}function y1(e){let t=new Uint16Array([0,1,2,2,1,3]);return KC(e,t)}function Ql(e,t,n,s,r,a){YC(t,n);let i=XC(e),o=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(o,i)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber(\"WEBGL_VERSION\")===1?fe(e,()=>e.texImage2D(o,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(o,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function gv(e){return e.internalFormatFloat}function v1(e,t,n,s){let[r,a]=Yl(t,n);return Ql(e,r,a,gv(s),s.textureFormatFloat,e.FLOAT)}function bv(e){return 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fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function $1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function _1(e,t,n,s){let[r,a]=Yl(t,n),i=4,o=new Uint8Array(T5(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function A1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array($5(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function E1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Kf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber(\"WEBGL_VERSION\");e!=null?(this.gl=e,S5(t,e)):this.gl=vs(t);let n=\"WEBGL_color_buffer_float\",s=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),X().getNumber(\"WEBGL_VERSION\")===1){let r=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Fu(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=Fu(this.gl,a);else if(X().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ln(this.gl,s))this.colorBufferHalfFloatExtension=Fu(this.gl,s);else if(X().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=b1(this.gl),this.indexBuffer=y1(this.gl),this.framebuffer=QC(this.gl),this.textureConfig=dv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),v1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),x1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),w1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),N1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),C1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return 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fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),qC(t,n),this.debug&&rd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=S1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&rd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?JC(this.gl,e,t):e1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),t1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=tu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&rd(this.gl,this.program),Ou(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Fu(this.gl,X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=DK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ad(this.gl,e,this.framebuffer),this.debug&&Ou(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ad(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ou(this.gl)):Vm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;ad(s,e,this.framebuffer),this.debug&&Ou(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function DK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:FK,bincountImpl:R1,bincountReduceImpl:OK,ceilImpl:PK,concatImpl:zK,equalImpl:MK,expImpl:LK,expm1Impl:BK,floorImpl:VK,gatherNdImpl:WK,gatherV2Impl:UK,greaterImpl:GK,greaterEqualImpl:HK,lessImpl:qK,lessEqualImpl:jK,linSpaceImpl:KK,logImpl:XK,maxImpl:YK,maximumImpl:QK,minimumImpl:ZK,multiplyImpl:JK,negImpl:eX,notEqualImpl:tX,prodImpl:nX,rangeImpl:sX,rsqrtImpl:rX,sigmoidImpl:aX,simpleAbsImpl:D1,sliceImpl:iX,sparseFillEmptyRowsImpl:oX,sparseReshapeImpl:uX,sparseSegmentReductionImpl:F1,sqrtImpl:lX,stridedSliceImpl:cX,stringNGramsImpl:dX,stringSplitImpl:pX,stringToHashBucketFastImpl:hX,subImpl:fX,tileImpl:mX,topKImpl:gX,transposeImpl:wv,uniqueImpl:bX}=Zy;function O1(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:O1(e,t)}function yX(e,t){if(e===1)return\"rc\";let n=\"\";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=\",\");return n}var vX=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=ln(\"rc\",this.rank),n=rt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},P1=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${xX(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?fv():hv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function xX(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?B5([\"r\",\"c\",\"d\"],\"inputShape\"):yi([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var wX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=iw(t,n),r=ow(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=aw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=iw(n,s),a=ow(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=aw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function aw(e,t,n,s,r){let a=IX(t,s),i;if(r){let[u,l]=tu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=kX(n,a);return i*o}function IX(e,t){switch(e){case 3:return vv(t);case 4:return xv(t);case 1:return gv(t);case 0:return bv(t);case 2:return yv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SX(e){return X().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function iw(e,t){if(e===1)return 3;if(e===0||e==null)return SX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function ow(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n     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= min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,FX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Zr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},OX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln(\"rc\",t),s=rt(t),r=yX(t,n),a=n.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},PX=xs.whereImpl,zX=1e-7,MX=1e-4,Zc={};function LX(e){return e in Zc||(Zc[e]={}),Zc[e]}var BX=X().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),VX=600;function WX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*VX/1024/1024}var z1=class extends nl{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Kf)t=e;else{let n=vs(X().getNumber(\"WEBGL_VERSION\"),e);t=new Kf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber(\"WEBGL_VERSION\"));t=new Kf(n),this.binaryCache=LX(X().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new wX(this.gpgpu),this.numMBBeforeWarning=WX(),this.texData=new Ud(this,Ss())}nextDataId(){return z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||X().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Zr(i,Mi):p=new Hs(i,Mi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Zr(s,Mi):h=new Hs(s,Mi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool(\"DEBUG\")&&!X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&X().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&X().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;o?d=new Zr(r,Mi):d=new Hs(r,Mi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!VC(n))throw X().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool(\"WEBGL_PACK\")&&s===!0,i=a?id(t):t,o=a?new _K(i):new $K(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=BX){return X().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return PX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=D1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,uw,e.dtype);let t=new Hs(e.shape,uw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ba(e.shape),...ya(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ba(t),...ya(t)],a=new P1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=id(r),o;s?o=new TK(i):o=new NK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!el(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=CK(e,l,c),d=this.getAndSaveBinary(p,()=>IK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get(\"ENGINE_COMPILE_ONLY\")||SK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool(\"WEBGL_LAZILY_UNPACK\")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=X().getBool(\"DEBUG\");X().set(\"DEBUG\",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zX:MX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=r1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=tu(c[0],c[1])),o?d=new EK(p,m):d=new AK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=UX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await OI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(pv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=m1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},M1=z1;M1.nextDataId=0;function UX(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ppe=\"0.0.0\";function GX(){X().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}dp.isBrowser()&&pp(\"webgl\",()=>new M1,2);var zpe={forceHalfFloat:GX},L1=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,ro=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}},jp=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,Zl=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Sn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${rt(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let o=ln(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function kn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var HX={kernelName:Ba,backendName:\"webgl\",kernelFunc:kn};function Rr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.texData.get(a.dataId),o=kn({inputs:{x:s},backend:n}),u=kn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var qX={kernelName:jd,backendName:\"webgl\",kernelFunc:Rr},B1=\"return (a < 0.) ? b * a : a;\",V1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function jX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),o=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(V1,r.shape,i.shape):new ro(B1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],\"float32\");return n.disposeIntermediateTensorInfo(i),u}var KX={kernelName:Va,backendName:\"webgl\",kernelFunc:jX},W1=\"return (a < 0.) ? b * a : a;\",U1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function XX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(U1,s.shape,r.shape):new ro(W1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var YX={kernelName:Ja,backendName:\"webgl\",kernelFunc:XX},ou=\"if (isnan(x)) return x;\",QX=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,ZX=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Zr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new ro(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Rr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new ro(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e===\"linear\")return t?AX:CX;if(e===\"relu\")return t?RX:TX;if(e===\"elu\")return t?EX:NX;if(e===\"relu6\")return t?DX:$X;if(e===\"prelu\")return t?U1:W1;if(e===\"leakyrelu\")return t?V1:B1;if(e===\"sigmoid\")return t?FX:_X;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var G1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";i&&(o?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:m=`vec4 activation(vec4 x) {\n          ${i}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},lw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},cw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},dw=\"return a * b;\";function kv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new cw(lw.REAL,s.shape,r.shape),c=new cw(lw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Rr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=JK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?i=new Zl(dw,s.shape,r.shape):i=new ro(dw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var JX={kernelName:Ya,backendName:\"webgl\",kernelFunc:kv};function e8(e,t,n){let s=[ba(e.shape),...ya(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ba(t),...ya(t)],i=new P1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!el(r.shape,u)&&!(c.texture!==null&&el(c.shape,u))?e8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var t8={kernelName:Eo,backendName:\"webgl\",kernelFunc:he},pw=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l=\"\";r%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${o===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${o===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${o===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},n8=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=\"0.0\",o=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",o=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",o=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(i=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function s8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=S.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function xi(e,t,n,s){let r=s8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n===\"mean\"?c=i===0?new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new n8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var r8=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=a8(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function a8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var i8=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=O1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function Xp(e,t,n){let s=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new i8(e.shape,t):new r8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function o8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims(\"sum\",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=xi(b,y,\"sum\",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return o8(r,a,i,n)}var u8={kernelName:oi,backendName:\"webgl\",kernelFunc:Yp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=wv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var l8={kernelName:pi,backendName:\"webgl\",kernelFunc:qt},H1=1e3;function 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Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=kv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new G1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function c8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var d8={kernelName:ra,backendName:\"webgl\",kernelFunc:c8},hw=\"return abs(x);\";function p8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!==\"complex64\"){let a=n.texData.get(s.dataId),i=D1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?r=new Zr(s.shape,hw):r=new Hs(s.shape,hw),n.runWebGLProgram(r,[s],s.dtype)}var h8={kernelName:io,backendName:\"webgl\",kernelFunc:p8},f8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`,m8=Ke({opSnippet:f8}),g8={kernelName:sl,backendName:\"webgl\",kernelFunc:m8},b8=ss+`\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,backendName:\"webgl\",kernelFunc:y8},fw=\"return a + 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ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return kn({inputs:{x:s[0]},backend:n});if(s.length>X().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),o=X().getBool(\"WEBGL_PACK\")?new I8(s[0].shape,a):new k8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var S8={kernelName:ka,backendName:\"webgl\",kernelFunc:ld};function C8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims(\"all\",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,\"all\",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var N8={kernelName:al,backendName:\"webgl\",kernelFunc:C8};function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims(\"any\",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,\"any\",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var $8={kernelName:il,backendName:\"webgl\",kernelFunc:T8},_8=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let i=t===\"max\"?\">\":\"<\",o=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        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coords;\n        --${l[o-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map(N=>\"int \"+N),m=ln(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=ln(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=ln(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=ln(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${T}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new _8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function j1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new A8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,\"int32\");if(l.shape.length===t.shape.length){let c=j1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function K1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return j1(e,t,s)}function E8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=K1(n,u,i[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var R8={kernelName:Ia,backendName:\"webgl\",kernelFunc:E8};function D8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=K1(n,u,i[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var F8={kernelName:ol,backendName:\"webgl\",kernelFunc:D8},O8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,P8=Ke({opSnippet:O8}),z8={kernelName:ul,backendName:\"webgl\",kernelFunc:P8},M8=ss+\"return log(x + sqrt(x * x + 1.0));\",L8=Ke({opSnippet:M8}),B8={kernelName:ll,backendName:\"webgl\",kernelFunc:L8},V8=ss+`\n  return atan(x);\n`,W8=Ke({opSnippet:V8}),U8={kernelName:cl,backendName:\"webgl\",kernelFunc:W8},G8=QX+`\n  return atan(a, b);\n`,H8=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+ZX+`\n  return result;\n`,q8=jt({opSnippet:G8,packedOpSnippet:H8}),j8={kernelName:pl,backendName:\"webgl\",kernelFunc:q8},K8=ss+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelName:dl,backendName:\"webgl\",kernelFunc:X8},tl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let N=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${N} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,T=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${T}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${T}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},Iv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let A=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${A} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let T=Math.floor(a/4)*4,N=a%4,E=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${T}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${E}\n            }\n\n            int xC = xCCorner + ${T};\n            if (${N===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${E}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function Q8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var Z8={kernelName:Sa,backendName:\"webgl\",kernelFunc:Q8};function J8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Iv(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var eY={kernelName:qd,backendName:\"webgl\",kernelFunc:J8},tY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},nY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new nY(d);return n.runWebGLProgram(h,[r],i.dtype)}var rY={kernelName:og,backendName:\"webgl\",kernelFunc:sY};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;nu([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new tY(c);return n.runWebGLProgram(p,[r],i.dtype)}var iY={kernelName:ig,backendName:\"webgl\",kernelFunc:aY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return 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u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function uu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.texData.get(r.dataId),d=iX(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new mY(u):new hY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),gY(r,o,u,n)}var bY={kernelName:Po,backendName:\"webgl\",kernelFunc:uu},yY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=uu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},vY={kernelName:oo,backendName:\"webgl\",kernelFunc:yY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=R1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var wY={kernelName:ug,backendName:\"webgl\",kernelFunc:xY};function kY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var IY={kernelName:lg,backendName:\"webgl\",kernelFunc:kY},SY=\"return float(a != b);\",X1=jt({opSnippet:SY,cpuKernelImpl:tX,dtype:\"bool\"}),CY={kernelName:So,backendName:\"webgl\",kernelFunc:X1};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.real},backend:n})}var NY={kernelName:np,backendName:\"webgl\",kernelFunc:Jl},TY=\"return float(int(x));\";function $Y(e,t){let n=new Hs(e.shape,TY),s=t.runWebGLProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Hm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return kn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Hm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=Rr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype===\"complex64\"){let i=Jl({inputs:{input:r},backend:n}),o=Hm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=kn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return $Y(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=X1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var _Y={kernelName:Na,backendName:\"webgl\",kernelFunc:Hm},mw=\"return ceil(x);\",AY=Ke({opSnippet:mw,packedOpSnippet:mw,cpuKernelImpl:PK}),EY={kernelName:Ta,backendName:\"webgl\",kernelFunc:AY},RY=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `}},DY=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `}};function FY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool(\"WEBGL_PACK_CLIP\")?o=new DY(r.shape):o=new RY(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var OY={kernelName:Sr,backendName:\"webgl\",kernelFunc:FY},PY=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function gw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function zY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new PY(s.shape),i=[gw(s,r.complexTensorInfos.real),gw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var MY={kernelName:Kd,backendName:\"webgl\",kernelFunc:zY},LY=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},BY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln(\"coords\",s),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`\n        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {\n          return getChannel(\n            getT${f}(${Jc(i,u,m)}),\n            vec2(${Jc(l,u,m)}));\n        }`}let d=o.length,h=o[o.length-1];p+=`\n        return getChannel(\n          getT${d}(${Jc(i,u,h)}),\n          vec2(${Jc(l,u,h)}));`,this.userCode=`\n      float getValue(${i.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 1;\n        if (${a[s-2]} < ${n[s-2]} &&\n            ${a[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function Jc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var VY={kernelName:Zd,backendName:\"webgl\",kernelFunc:Qp};function Gi(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let c=e.map(m=>Jl({inputs:{input:m},backend:n})),p=e.map(m=>Qp({inputs:{input:m},backend:n})),d=Gi(c,t,n),h=Gi(p,t,n),f=Rr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=S.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=zK(p,d,s,h),m=S.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let c=Math.floor(e.length/2),p=Gi(e.slice(0,c),t,n),d=Gi(e.slice(c),t,n),h=Gi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&e[0].shape.length>1){let c=new BY(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=WY(e,t,n),o=new LY(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function WY(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return kn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Gi(o,a,n)}var UY={kernelName:uo,backendName:\"webgl\",kernelFunc:Y1},Q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat===\"channelsLast\",g=m?1:2,b=m?2:3,y=m?3:1,v=\"\",x=\"\";n&&(s?v=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?v=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:v=`\n          float activation(float x) {\n            ${n}\n          }\n        `,x=\"result = activation(result);\");let k=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${v}\n\n      const ivec2 strides = ivec2(${o}, ${u});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${y}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${b}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},GY=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},HY=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"},{name:\"pad\",type:\"ivec2\"},{name:\"stride\",type:\"ivec2\"},{name:\"dilation\",type:\"ivec2\"},{name:\"inChannels\",type:\"int\"},{name:\"itemsPerBlockRow\",type:\"int\"},{name:\"outWidth\",type:\"int\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n===\"channelsLast\",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?\"if(blockIndex < outShape[1] && pos < outShape[0]) {\":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.y + ${c};\n          pos = rc.x + ${l};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${s.output} = result;\n      }\n    `}};function Z1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>H1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(el(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=zd({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=zd({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function J1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new HY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],\"float32\",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o===\"leakyrelu\",z=o?Kp(o,!0):null,W=new G1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,\"float32\"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function qY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=Z1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)h=J1({x:r,filter:a,convInfo:d,backend:n});else{let m=new Q1(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jY={kernelName:$a,backendName:\"webgl\",kernelFunc:qY},KY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},XY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},YY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},QY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function ZY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new KY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var JY={kernelName:cg,backendName:\"webgl\",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var t9={kernelName:_a,backendName:\"webgl\",kernelFunc:e9};function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new GY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var s9={kernelName:Xd,backendName:\"webgl\",kernelFunc:n9};function r9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new YY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var a9={kernelName:dg,backendName:\"webgl\",kernelFunc:r9};function i9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new QY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var o9={kernelName:pg,backendName:\"webgl\",kernelFunc:i9},u9=ou+`\n  return cos(x);\n`,l9=Ke({opSnippet:u9}),c9={kernelName:Aa,backendName:\"webgl\",kernelFunc:l9},d9=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,p9=Ke({opSnippet:d9}),h9={kernelName:Ea,backendName:\"webgl\",kernelFunc:p9},f9=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},m9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new f9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],\"float32\")},g9={kernelName:co,backendName:\"webgl\",kernelFunc:m9},bw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"1.0\":`getX(${yw(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${vw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${vw(s,\"coords\")} = idx;\n          val *= getX(${yw(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function yw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new bw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new bw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var y9={kernelName:hl,backendName:\"webgl\",kernelFunc:b9},xw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"0.0\":`getX(${ww(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${kw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${kw(s,\"coords\")} = idx;\n          val += getX(${ww(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function ww(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function v9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new xw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new xw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var x9={kernelName:lo,backendName:\"webgl\",kernelFunc:v9};function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=R1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var k9={kernelName:hg,backendName:\"webgl\",kernelFunc:w9},I9=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function S9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=new I9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var C9={kernelName:po,backendName:\"webgl\",kernelFunc:S9},e2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},t2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,o===1){if(b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function N9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new t2(p):d=new e2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var T9={kernelName:Ra,backendName:\"webgl\",kernelFunc:N9},$9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},_9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function A9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new $9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var E9={kernelName:fg,backendName:\"webgl\",kernelFunc:A9};function R9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new _9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var D9={kernelName:mg,backendName:\"webgl\",kernelFunc:R9},F9=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function O9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new F9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var P9={kernelName:gg,backendName:\"webgl\",kernelFunc:O9},z9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function M9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeDilation2DInfo(r.shape,a.shape,i,o,\"NHWC\",u),c,p=new z9(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var L9={kernelName:Yd,backendName:\"webgl\",kernelFunc:M9};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=qt({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=kv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Yp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of 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cQ={kernelName:yg,backendName:\"webgl\",kernelFunc:lQ},dQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new dQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var pQ={kernelName:ml,backendName:\"webgl\",kernelFunc:ec},hQ=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},fQ={kernelName:go,backendName:\"webgl\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new hQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Cw=\"return floor(x);\",mQ=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:VK}),gQ={kernelName:Pa,backendName:\"webgl\",kernelFunc:mQ},bQ=`\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`,yQ=`\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division 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n.disposeData(d.dataId),f}function CQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))b=Z1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)b=J1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,T=h===\"leakyrelu\",N=h?Kp(h,!1):null,E=new Q1(g,x,N,k,T),A=[r,a];if(i&&A.push(i),o&&A.push(o),T){let 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    ${r} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${a};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `}};function AQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=WK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new _Q(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var EQ={kernelName:yo,backendName:\"webgl\",kernelFunc:AQ},RQ=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=DQ(e,2);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${s}));\n      }\n    `}};function DQ(e,t){let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(\"index\"):s.push(`${n[r]}`);return s.join()}function r2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get(\"DEBUG\")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype===\"string\"){let y=n.bufferSync(h),v=n.bufferSync(d),x=UK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new RQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var FQ={kernelName:bo,backendName:\"webgl\",kernelFunc:r2},OQ=\"return float(a > b);\",PQ=`\n  return vec4(greaterThan(a, b));\n`,zQ=jt({opSnippet:OQ,packedOpSnippet:PQ,cpuKernelImpl:GK,dtype:\"bool\"}),MQ={kernelName:vo,backendName:\"webgl\",kernelFunc:zQ},LQ=\"return 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b));\n`,iZ=jt({opSnippet:rZ,packedOpSnippet:aZ,cpuKernelImpl:jK,dtype:\"bool\"}),oZ={kernelName:wo,backendName:\"webgl\",kernelFunc:iZ};function uZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=KK(s,r,a);return t.makeTensorInfo([i.length],\"float32\",i)}var lZ={kernelName:xg,backendName:\"webgl\",kernelFunc:uZ},cZ=ou+`\n  return x < 0.0 ? 0./0. : log(x);\n`,dZ=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,pZ=Ke({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:XK}),hZ={kernelName:Wa,backendName:\"webgl\",kernelFunc:pZ},fZ=ou+`\n  return log(1.0 + x);\n`,mZ=Ke({opSnippet:fZ}),gZ={kernelName:vl,backendName:\"webgl\",kernelFunc:mZ},bZ=\"return float(a >= 1.0 && b >= 1.0);\",yZ=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,vZ=jt({opSnippet:bZ,packedOpSnippet:yZ,dtype:\"bool\"}),xZ={kernelName:ko,backendName:\"webgl\",kernelFunc:vZ},wZ=\"return float(!(x >= 1.0));\",kZ=Ke({opSnippet:wZ}),IZ={kernelName:xl,backendName:\"webgl\",kernelFunc:kZ},SZ=\"return float(a >= 1.0 || b >= 1.0);\",CZ=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,NZ=jt({opSnippet:SZ,packedOpSnippet:CZ,dtype:\"bool\"}),TZ={kernelName:Jd,backendName:\"webgl\",kernelFunc:NZ},$Z=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${a}; j <= ${a}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${o};\n        setOutput(val);\n      }\n    `}},_Z=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${a};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${a}; j <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${o};\n        setOutput(result);\n      }\n    `}},AZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _Z(r.shape,a,i,o,u):new $Z(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},EZ={kernelName:ep,backendName:\"webgl\",kernelFunc:AZ},RZ=class{constructor(e,t,n,s,r){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${s}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${s})\n                * float(${r})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${r});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}},DZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new RZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},FZ={kernelName:wg,backendName:\"webgl\",kernelFunc:DZ};function OZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=xi(o,e.dtype,\"max\",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function a2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[c[N]];let k=wv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Xp(r,c,n);l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims(\"max\",l,o);let[f,m]=S.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=S.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=YK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=OZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var PZ={kernelName:Ua,backendName:\"webgl\",kernelFunc:a2},zZ=L1+`\n  return max(a, b);\n`,MZ=`\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+jp+`\n  return result;\n`,LZ=jt({opSnippet:zZ,packedOpSnippet:MZ,cpuKernelImpl:QK}),BZ={kernelName:Ga,backendName:\"webgl\",kernelFunc:LZ};function VZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,\"maxPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,\"max\",!1);return n.runWebGLProgram(p,[r],r.dtype)}var WZ={kernelName:Ha,backendName:\"webgl\",kernelFunc:VZ};function UZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Iv(p,\"max\",!1);return n.runWebGLProgram(d,[r],r.dtype)}var GZ={kernelName:tp,backendName:\"webgl\",kernelFunc:UZ},HZ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},qZ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? 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1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Iv(d,\"max\",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new qZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var KZ={kernelName:Ig,backendName:\"webgl\",kernelFunc:jZ};function XZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;nu([a,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new tl(d,\"max\",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new HZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var YZ={kernelName:kg,backendName:\"webgl\",kernelFunc:XZ};function QZ(e,t,n,s){let r=new tl(n,\"max\",!1),a=s.runWebGLProgram(r,[e],\"float32\");r=new tl(n,\"max\",!0,!0,t);let i=s.runWebGLProgram(r,[e],\"float32\");return[a,i]}var ZZ={kernelName:Sg,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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setOutput(value);\n        } else {\n          ${r} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `}},G7=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(\",\"),i=t.map((f,m)=>f[0]+e[m]).join(\",\"),o=ln(\"rc\",s),u=ln(\"source\",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;\n       if(${l}) {\n      `,s===1?\"\":`}\n       rc = outputLoc;\n       ${o[s-2]} += 1;\n       if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?\"\":`  ${o[s-1]} += 1;\n         if(${l}) {`],d=s===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",h=\"\";for(let f=0,m=s===1?2:4;f<m;f++)h+=`\n        ${p[f]}\n        if (${d}) {\n          result[${f}] = 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sign(a) * pow(abs(a), b);\n`,j7=`\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  `+jp+`\n  return result;\n`,K7=jt({opSnippet:q7,packedOpSnippet:j7}),X7={kernelName:Za,backendName:\"webgl\",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims(\"prod\",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=nX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=cp(r.dtype),v=xi(b,y,\"prod\",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Q7={kernelName:Ao,backendName:\"webgl\",kernelFunc:Y7},d2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Z7={kernelName:Il,backendName:\"webgl\",kernelFunc:d2},J7=\"return 1.0 / x;\",eJ=Ke({opSnippet:J7}),tJ={kernelName:Sl,backendName:\"webgl\",kernelFunc:eJ},nJ=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,sJ=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,rJ=Ke({opSnippet:nJ,packedOpSnippet:sJ}),aJ={kernelName:ei,backendName:\"webgl\",kernelFunc:rJ},iJ=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,oJ=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,uJ=Ke({opSnippet:iJ,packedOpSnippet:oJ}),lJ={kernelName:ni,backendName:\"webgl\",kernelFunc:uJ},cJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}},dJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":p=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function pJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new dJ(r.shape,u,l,a,i):new cJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],\"float32\")}var hJ={kernelName:ti,backendName:\"webgl\",kernelFunc:pJ},fJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function mJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new fJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var gJ={kernelName:Tg,backendName:\"webgl\",kernelFunc:mJ},bJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},yJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function vJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new yJ(r.shape,u,l,a,i):new bJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var xJ={kernelName:Cl,backendName:\"webgl\",kernelFunc:vJ},wJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function kJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new wJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var IJ={kernelName:Ng,backendName:\"webgl\",kernelFunc:kJ},SJ=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet 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u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=mX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Gee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var qee={kernelName:Cr,backendName:\"webgl\",kernelFunc:h2},jee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `}},Kee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `}};function Ur(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _w(e){let t=1;for(;t<e;)t*=2;return t}function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=X().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=gX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Ur(n,h);let b=_w(a),y=_w(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new jee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,\"int32\",q),Ur(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Kee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,\"int32\",z),Ur(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=uu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Ur(n,T);let N=r2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Ur(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Ur(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Ur(n,A),[N,v]}var Yee={kernelName:Wo,backendName:\"webgl\",kernelFunc:Xee},Qee=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=n===\"nearest\"?1:2,o;switch(s){case\"constant\":o=1;break;case\"reflect\":o=2;break;case\"wrap\":o=3;break;case\"nearest\":o=4;break;default:o=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function Zee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Qee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var Jee={kernelName:Uo,backendName:\"webgl\",kernelFunc:Zee};function ete(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;nu(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=bX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],\"int32\",l)]}var tte={kernelName:Ag,backendName:\"webgl\",kernelFunc:ete};function nte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=uu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ste={kernelName:Go,backendName:\"webgl\",kernelFunc:nte},rte=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new rte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=d2({backend:n,attrs:{start:0,stop:E,step:1,dtype:\"float32\"}}),q=h2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,\"unsortedSegmentSum\",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ite={kernelName:up,backendName:\"webgl\",kernelFunc:ate},ote=[d8,h8,g8,v8,w8,S8,N8,$8,R8,F8,z8,B8,U8,j8,Y8,Z8,eY,rY,iY,uY,pY,vY,wY,IY,_Y,EY,OY,qX,MY,UY,jY,JY,t9,s9,a9,o9,c9,h9,g9,y9,x9,k9,C9,T9,E9,D9,P9,L9,V9,H9,X9,J9,nQ,aQ,iQ,uQ,cQ,pQ,fQ,gQ,xQ,IQ,NQ,$Q,EQ,FQ,MQ,WQ,HX,GQ,VY,jQ,YQ,JQ,KX,sZ,oZ,lZ,hZ,gZ,xZ,IZ,TZ,EZ,FZ,PZ,BZ,WZ,GZ,KZ,YZ,ZZ,e7,n7,i7,c7,f7,k7,JX,N7,_7,R7,O7,CY,M7,B7,W7,H7,X7,YX,Q7,Z7,NY,y7,tJ,aJ,lJ,t8,hJ,gJ,xJ,IJ,TJ,_J,RJ,OJ,zJ,BJ,UJ,jJ,YJ,JJ,nee,bY,x7,aee,oee,lee,dee,hee,mee,bee,vee,wee,See,Nee,$ee,Eee,Dee,Oee,zee,v7,u8,Bee,Uee,qee,Yee,Jee,l8,tte,ste,ite,L7];for(let e of ote)El(e);var zs=X();zs.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);zs.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);zs.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D\",()=>!1);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);zs.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);zs.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);zs.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);zs.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);zs.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);var ute=\"return a + b;\",lte=\"return areal * breal - aimag * bimag;\",cte=\"return areal * bimag + aimag * breal;\",dte=\"return a / b;\",pte=\"return a * b;\",hte=\"return (a - b) * (a - b);\",fte=\"return a - b;\",mte=\"return f32(a == b);\",gte=\"return vec4<f32>(a == b);\",bte=\"return f32(a > b);\",yte=\"return vec4<f32>(a > b);\",vte=\"return f32(a >= b);\",xte=\"return vec4<f32>(a >= b);\",wte=\"return f32(a < b);\",kte=\"return vec4<f32>(a < b);\",Ite=\"return f32(a <= b);\",Ste=\"return vec4<f32>(a <= b);\",Cte=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,Tte=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,f2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,$te=`\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `,_te=`\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `,Ate=\"return f32(a != b);\",Ete=\"return vec4<f32>(a != b);\",Rte=`\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `,Dte=`\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  ${f2}\n  return resultTemp;\n  `,Fte=\"if (a < 0.0) { return b * a; }  return a;\",Ote=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function Aw(e,t){let n=t?f2:Tte;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function tc(e,t){switch(e){case 0:return pte;case 1:return ute;case 2:return fte;case 3:return dte;case 4:return t?gte:mte;case 5:return t?yte:bte;case 6:return t?xte:vte;case 7:return t?kte:wte;case 8:return t?Ste:Ite;case 9:return t?Nte:Cte;case 10:return t?Ete:Ate;case 11:return hte;case 12:return t?_te:$te;case 14:return t?Ote:Fte;case 15:return Aw(\"max\",t);case 16:return Aw(\"min\",t);case 13:return t?Dte:Rte;case 17:return lte;case 18:return cte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Pte=\"return abs(a);\",zte=\"return ceil(a);\",Mte=\"return cos(a);\",Lte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Bte=\"return exp(a) - 1.0;\",Vte=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",Wte=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Ute=\"return exp(a);\",Gte=\"return floor(a);\",Hte=\"return a;\",qte=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,jte=\"return f32(!(a >= 1.0));\",Kte=\"return -a;\",Xte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Yte=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,Qte=\"if(a < 0.0) { return 0.0; } return a;\",Zte=\"return clamp(a, 0.0, 6.0);\",Jte=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",ene=`\n  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));\n  let isNaN = isnanVec4(a);\n\n  if (isNaN.r) {\n    resFloat.r = a.r;\n  }\n  if (isNaN.g) {\n    resFloat.g = a.g;\n  }\n  if (isNaN.b) {\n    resFloat.b = a.b;\n  }\n  if (isNaN.a) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,tne=\"return 1.0/sqrt(a);\",nne=\"return 1.0 / (1.0 + exp(-1.0 * a));\",sne=\"return sin(a);\",rne=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,ane=\"return sqrt(a);\",ine=\"return a * a;\",one=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,une=\"return f32(i32((a)));\";function Hr(e,t){switch(e){case 0:return Pte;case 2:return Mte;case 3:return Lte;case 1:return zte;case 4:return t?Wte:Vte;case 5:return Ute;case 6:return Bte;case 7:return Gte;case 8:return Hte;case 9:return qte;case 10:return jte;case 11:return Kte;case 14:return t?Yte:Xte;case 12:return t?ene:Qte;case 13:return t?Jte:Zte;case 15:return tne;case 18:return nne;case 16:return sne;case 17:return rne;case 19:return ane;case 20:return ine;case 21:return one;case 22:return une;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e===\"linear\")return Hr(8);if(e===\"relu\")return Hr(12,t);if(e===\"elu\")return Hr(4,t);if(e===\"relu6\")return Hr(13,t);if(e===\"prelu\")return tc(14,t);if(e===\"sigmoid\")return Hr(18,t);if(e===\"leakyrelu\")return Hr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function lne(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function Sv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function Dr(){return`\n  ${Sv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Ue(){return`\n    ${Dr()}\n      let index = getGlobalIndex();\n`}function cne(e,t,n,s=!1){let r=[];if(r.push(`\n    let workGroupSizeX = ${n.workGroupSize[0]}u;\n    let workGroupSizeY = ${n.workGroupSize[1]}u;\n    let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {\n        return i32(globalId.x);\n      }\n\n      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n          localId.y * workGroupSizeX + localId.x;\n      let workGroupID = (globalId - localId)/vec3<u32>(\n          workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n        workGroupID.y * numWorkgroups.x + workGroupID.x) *\n        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n        localInvocationIndex);\n    }\n  `),s===!0)return r.push(`\n      struct Uniform {\n        size            : i32,\n        numChannels     : i32,\n        outShapeStrides : vec2<i32>,\n        dispatchSize    : vec3<u32>,\n      };\n\n      @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `),[Ew,r.join(`\n`),Rw(t.shape),n.getUserCode()].join(`\n`);let a=\"struct Uniforms { NAN : f32, \";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}, `}),a+=`outShape : ${Wt(t.shape.length)}, `;let i=t.shape.length-1;a+=`\n       outShapeStrides: ${Wt(i)}, `,n.size&&(a+=\"size : i32, \"),n.uniforms&&(a+=n.uniforms),a+=\"};\",r.push(a),n.atomic?r.push(`\n    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n  `):r.push(`\n    @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n  `),n.variableNames.forEach((p,d)=>{r.push(`\n    @group(0) @binding(${1+d}) var<storage, read> ${p}: array<${cd(e[d].dtype,n.isVec4)}>;\n    `)}),a!==\"\"&&r.push(`\n    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;\n    `);let[o,u]=gne(t.shape,n.dispatchLayout),l=[Ew,r.join(`\n`),Rw(t.shape),o,dne(t.shape.length)];if(n.atomic||l.push(pne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>hne(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);l.push(p)}return l.push(n.getUserCode()),l.join(`\n`)}var Ew=`\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;function dne(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function pne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${r}(value);\n    }`,s>=2){let i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=Wt(s);n?a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}function hne(e,t,n,s){let r=fne(e,n);return e.shape.length<=t.length&&(r+=mne(e,t,n,s)),r}function fne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=i.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${o}) -> vec4<f32> {\n        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${o}) -> f32 {\n      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n        ${u})]);\n    }\n   `}function mne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i=\"get\"+a+\"ByOutput\",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n        fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${r}[globalIndex]);\n        }\n\n        fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n          return vec4<f32>(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n        }\n        `:`\n      fn ${i}Index(globalIndex : i32) -> f32 {\n        return f32(${r}[globalIndex]);\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32 {\n        return f32(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n      }\n      `;let c=S.getBroadcastDims(e.shape,t),p=u-o,d=\"\";if(o===0)return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n        return get${a}();\n      }\n    `:`\n      fn ${i}Index(globalIndex : i32) -> f32{\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32{\n        return get${a}();\n      }\n    `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords[${g+p}] = 0;`).join(`\n`);let h=\"\";if(u<2&&o>0)h=\"coords\";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+p}]`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n\n      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n    `:`\n    fn ${i}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n\n    fn ${i}Coords(coordsIn : ${l}) -> f32 {\n      var coords = coordsIn;\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n  `}function gne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `,a];let i=\"\",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=lne(h,\"uniforms.outShape\");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n    ${i}\n  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function Rw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a=\"var index2 = index;\"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join(\"\");return`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}var m2={};Ae(m2,{ArrayBufferToTypedArray:()=>b2,GPUBytesPerElement:()=>jm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Cv,computeWorkGroupSizeForMatMul:()=>g2,computeWorkPerThreadForConv2d:()=>Nv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Tv,tilesFitEvenlyIntoShape:()=>Ks});var na=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(na(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(na(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(na(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Cv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function g2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Nv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function jm(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function b2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Tv(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function y2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>\"tileInner must be divisible by 4. And ColPerThread must be 4\"),`\n  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let TileInner = ${s};\n\n  ${Dr()}\n\n    let tileRow = ${t===1?\"0\":\"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var ACached : vec4<f32>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / ColPerThread;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {\n            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];\n            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];\n            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];\n            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                acc[i] = BCached[3] * ACached.w + acc[i];\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var bne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${i}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${i}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}\n    `}};function $v(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`\n    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;\n    ${Dr()}\n      let tileRow = i32(localId.y) * ${e[1]};\n      let tileCol = i32(localId.x) * ${e[0]};\n\n      let globalRow = i32(globalId.y) * ${e[1]};\n      let globalCol = i32(globalId.x) * ${e[0]};\n\n      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;\n\n      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;\n      var ACached : f32;\n      var BCached : array<f32, ${e[0]}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let ColPerThreadA = ${r} / ${t[0]};\n      let tileColA = i32(localId.x) * ColPerThreadA;\n      let RowPerThreadB = ${r} / ${t[1]};\n      let tileRowB = i32(localId.y) * RowPerThreadB;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileColA + innerCol;\n\n            mm_Asub[inputRow][inputCol] = mm_readA(\n                globalRow + innerRow,\n                t * ${r} + inputCol, globalId);\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${r} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${r}; k = k + 1) {\n          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n            ACached = mm_Asub[tileRow + innerRow][k];\n            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `}function yne(e){return`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${Dr()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),\n                                mm_readA(globalRow, colA + 1, globalId),\n                                mm_readA(globalRow, colA + 2, globalId),\n                                mm_readA(globalRow, colA + 3, globalId));\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `}var v2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=g2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch * batchASize + row * uniforms.dimInner + col];\n           }\n           return 0.0;`:e=this.fitA?\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch* batchASize + col * uniforms.dimAOuter + row];\n           }\n           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n           }\n           return 0.0;`:t=this.fitB?\"return B[batch * batchBSize + col * uniforms.dimInner + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + col * uniforms.dimInner + row];\n           }\n           return 0.0;`;let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1]>1?$v([this.workPerThread,this.workPerThread,1],this.workGroupSize):yne(this.workGroupSize)}\n    `}};function vne(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${Dr()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var xne=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e=\"return A[batch * batchASize + row * uniforms.dimInner + col];\":e=\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\";let t;this.transposeB===!1?t=\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        ${e}\n      }\n\n      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${vne()}\n    `}};function wne(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${Dr()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var kne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push(\"bias\");let o=a!=null;o&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${i}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${i}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${wne(this.workGroupSize)}\n    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Ine={kernelName:Eo,backendName:\"webgpu\",kernelFunc:Me};function _v({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new xne([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new kne(k,T,[P,h,f],a,u,i):R?F=new bne(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),a,u,i):F=new v2(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}];u===\"leakyrelu\"&&(z.push({type:\"float32\",data:[o]}),F.uniforms+=\" alpha : f32,\");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Sne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return _v({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var Cne={kernelName:ra,backendName:\"webgpu\",kernelFunc:Sne},Dw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},Nne=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${tc(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},Tne=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${tc(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},x2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function Fw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new Tne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Nne(e,t,n,a):new x2(e,t,n)}function es(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $ne={kernelName:Ba,backendName:\"webgpu\",kernelFunc:es};function lu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.tensorMap.get(a.dataId),o=es({inputs:{x:s},backend:n}),u=es({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var _ne={kernelName:jd,backendName:\"webgpu\",kernelFunc:lu},nc=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${Hr(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype===\"complex64\"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Dw(17,i.shape,o.shape),b=new Dw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=lu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype===\"string\"||o.dtype===\"string\"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype===\"string\"?S.fromUint8ToStringArray(p):p,f=i.dtype===\"string\"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:Ane,ceilImpl:Ene,concatImpl:Rne,equalImpl:Dne,expImpl:Fne,expm1Impl:One,floorImpl:Pne,gatherNdImpl:zne,gatherV2Impl:Mne,greaterEqualImpl:Lne,greaterImpl:Bne,lessEqualImpl:Vne,lessImpl:Wne,logImpl:Une,maxImpl:Gne,maximumImpl:Hne,minimumImpl:qne,multiplyImpl:jne,negImpl:Kne,notEqualImpl:Xne,prodImpl:Yne,rangeImpl:Qne,rsqrtImpl:Zne,simpleAbsImpl:Jne,sliceImpl:ese,stridedSliceImpl:tse,stringNGramsImpl:nse,subImpl:sse,tileImpl:rse,topKImpl:ase,transposeImpl:ise,uniqueImpl:Mpe}=Zy,ose=Kt({opType:0,cpuKernelImpl:Jne}),use={kernelName:io,backendName:\"webgpu\",kernelFunc:ose},lse=mn({opSnippet:1,cpuKernelImpl:Ane,supportsComplex:!0}),cse={kernelName:Ir,backendName:\"webgpu\",kernelFunc:lse},dse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function pse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return es({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new dse(a);return n.runWebGPUProgram(i,s,r)}var hse={kernelName:ka,backendName:\"webgpu\",kernelFunc:pse},w2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"axis : i32, infinityValue : f32,\",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape[${r}]`;return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      // In order to get a flattened index into the input tensor, we need to\n      // add back the index along the reduced dimension to |outputCoords|.\n      // This function outputs the offset to the first value along\n      // |axis| and the stride to get the next value of the input along |axis|.\n      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        var i = ${this.outputShape.length-1};\n\n        var stride = 1;\n        var inputStride = 1;\n        var offset = 0;\n\n        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {\n          let length = ${n(`${this.inputShape.length} - r`)};\n          if (${this.inputShape.length} - r == uniforms.axis) {\n            inputStride = stride;\n          } else {\n            offset = offset + ${t(\"outputCoords\",\"i\")} * stride;\n            i = i - 1;\n          }\n          stride = stride * length;\n        }\n\n        return vec2<i32>(offset, inputStride);\n      }\n\n      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{\n        return coordInfo[0] + coordInfo[1] * index;\n      }\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let coordInfo = getInputCoordInfo(outputIndex);\n        let Length = ${n(\"uniforms.axis\")};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n\n        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = f32(x[getInputIndex(coordInfo, k)]);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(Length), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},fse=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${Sv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},mse=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=gse(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function gse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function wi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=ise(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new fse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new mse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var bse={kernelName:pi,backendName:\"webgpu\",kernelFunc:wi};function yse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],\"max\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var vse={kernelName:Ia,backendName:\"webgpu\",kernelFunc:yse};function xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],\"min\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var wse={kernelName:ol,backendName:\"webgpu\",kernelFunc:xse},k2=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,\",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},I2=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>,\",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function kse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new I2(c):(p=new k2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var Ise={kernelName:Sa,backendName:\"webgpu\",kernelFunc:kse};function Sse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return _v({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Cse={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Sse},Nse=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=Tse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},Km=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function Tse(e){if(e===1)return\"sourceLoc\";if(e<=6)return Km.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=ese(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Nse(o,u),c=[{type:\"int32\",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var $se={kernelName:Po,backendName:\"webgpu\",kernelFunc:cu},_se=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},Ase={kernelName:oo,backendName:\"webgpu\",kernelFunc:_se},S2=mn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:Xne}),Ese={kernelName:So,backendName:\"webgpu\",kernelFunc:S2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.real},backend:n})}var Rse={kernelName:np,backendName:\"webgpu\",kernelFunc:sc};function Dse(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Xm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return es({inputs:{x:r},backend:n});let i=$t(r.shape),o=Xm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=lu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype===\"complex64\"){let i=sc({inputs:{input:r},backend:n}),o=Xm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=es({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return Dse(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=S2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Fse={kernelName:Na,backendName:\"webgpu\",kernelFunc:Xm},Ose=Kt({opType:1,cpuKernelImpl:Ene}),Pse={kernelName:Ta,backendName:\"webgpu\",kernelFunc:Ose},zse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},Mse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function Lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new zse(r.shape):o=new Mse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Bse={kernelName:Sr,backendName:\"webgpu\",kernelFunc:Lse},Vse=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Wse={kernelName:Zd,backendName:\"webgpu\",kernelFunc:Zp};function Ym(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Ym(h,t,n),g=Ym(f,t,n),b=lu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Rne(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Use(e,t,n),o=a.map(h=>h.shape),u=new Vse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Use(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return es({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Ym(o,a,n)}var Gse={kernelName:uo,backendName:\"webgpu\",kernelFunc:C2},Hse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.hasLeakyreluAlpha&&this.variableNames.push(\"leakyreluAlpha\"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);\n    let divBy4Remainder${e} = flatIndex${e} % 4;\n    let divBy4Index${e} = flatIndex${e} / 4;\n    let curData${e} = x[divBy4Index${e}];\n    if (divBy4Remainder${e} == 0) {\n      temp = curData${e};\n    } else {\n      // TODO: This could end up being a redundant load with another one in\n      // the same shader invocation. Perhaps there's an opportunity for\n      // optimization\n      let nextData${e} = x[divBy4Index${e} + 1];\n      if (divBy4Remainder${e} == 1) {\n        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);\n      } else if (divBy4Remainder${e} == 2) {\n        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);\n      } else if (divBy4Remainder${e} == 3) {\n        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);\n      }\n    }\n    `}getUserCode(){let e=y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        var coord = vec4<i32>(\n            batch,\n            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n            inChCoord);\n        var resData = vec4<f32>(0.0);\n        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for\n          // the 'same' padding type.\n          if (coordsInBounds4D(coord, uniforms.xShape)) {\n            resData = x[getIndexFromCoords4D(coord, uniforms.xShape) / 4];\n          } else {\n            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);\n          ${this.getSampleAWithRemainder(1)}\n          resData = temp;\n          if (WCol == (uniforms.filterDims[1] - 1)) {\n            coord = vec4<i32>(\n              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);\n              ${this.getSampleAWithRemainder(2)}\n            if (inChCoord == 0) {\n              resData = vec4<f32>(resData.xyz, temp.x);\n            } else if (inChCoord == 1) {\n              resData = vec4<f32>(resData.xy, temp.xy);\n            } else {\n              resData = vec4<f32>(resData.x, temp.xyz);\n            }\n          }\n          `}\n        return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${s}\n         }\n         return vec4<f32>(0.0);\n        `,a=this.fitB?\"return W[row * uniforms.dimBOuter / 4 + col];\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `,i=\"\",o=\"\";if(this.activation){let c=Zs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${c}\n        }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {\n          let b = getLeakyreluAlphaByOutputCoords(outCoord);\n          ${c}\n        }`,new Error(\"Leakyrelu is not supported.\");i=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${c}\n        }`}o=\"value = activation(value, outCoord);\"}let u=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n        ${i}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          let r = row;\n          let c = col * 4;\n          var batch = i32(globalId.z);\n          ${r}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${a}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${u}\n            ${o}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${e}\n      `}},qse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=$v(this.elementsPerThread,this.workGroupSize),t=`\n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);\n    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];\n    let coord = vec4<i32>(\n        batch,\n        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n        col % uniforms.xShape[3]);\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${t}\n    }\n    return 0.0;\n    `,s=this.fitB?\"return W[row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter + col];\n\t }\n\t return 0.0;\n\t `,r=\"\",a=\"\";if(this.activation){let u=Zs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${u}\n                }`:r=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${u}\n                  }\n                `,a=\"value = activation(value, outCoord);\"}let i=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n    ${r}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${n}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      ${s}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      ${i}\n      ${a}\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${e}\n  `}},jse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${r}\n             }`:e=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n                    ${r}\n                  }\n                `,t=\"value = activation(value, outCoord);\"}let n=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${e}\n      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if(coordsInBounds4D(coord, uniforms.xShape)) {\n          return getX(batch, row, col, chan);\n        }\n        return 0.0;\n      }\n\n      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n        let coord = vec4<i32>(row, col, xChannel, outChannel);\n        if(coordsInBounds4D(coord, uniforms.wShape)) {\n          return getW(row, col, xChannel, outChannel);\n        }\n        return 0.0;\n      }\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          ${n}\n          ${t}\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Dr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let outChannel = coords[3];\n\n        var acc = 0.0;\n\n        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {\n              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n              let v = readInp(batch, coordRow, coordCol, xChannel);\n              let f = readFilt(row, col, xChannel, outChannel);\n              acc = acc + v * f;\n            }\n          }\n        }\n\n        writeResult(batch, coords[1], coords[2], outChannel, acc);\n      }\n    `}},Kse=class{constructor(e,t){this.variableNames=[\"A\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,\n      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`\n    ${Ue()}\n\n      for(var i = 0; i<${this.workPerThread}; i = i + 1) {\n        let flatIndex = index * ${this.workPerThread} + i;\n\n        let rc = getCoordsFromIndex(flatIndex);\n\n        if(flatIndex < uniforms.size) {\n          let blockIndex = rc[0];\n          let pos = rc[1];\n\n          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];\n          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;\n          var value = 0.0;\n          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {\n            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -\n              uniforms.pad[0];\n            let d1 = offsetX + uniforms.dilation[0] * ((pos %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n            let ch = pos % uniforms.inChannels;\n            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {\n              value = getA(d0, d1, ch);\n            }\n          }\n          setOutputAtIndex(flatIndex, value);\n        }\n      }\n    }\n  `}};function Xse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat===\"channelsLast\",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=_v({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Yse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y===\"channelsLast\",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Kse(T,v),$=[{type:\"int32\",data:[h.left,h.top]},{type:\"int32\",data:[p,d]},{type:\"int32\",data:[g,b]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*u]},{type:\"int32\",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new v2(q,[1,k,n.outChannels],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:\"int32\",data:[Y]},{type:\"int32\",data:[te]},{type:\"int32\",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o===\"leakyrelu\"&&($.push({type:\"float32\",data:[i]}),K.uniforms+=\" alpha : f32,\");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function N2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return Xse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")&&e.shape[0]===1)return Yse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool(\"WEBGPU_USE_NAIVE_CONV2D\"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type===\"VALID\")&&n.outChannels%4===0,f=[n.padInfo.top,n.padInfo.left],m=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...f]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new jse(n,u,o,l);else{h?c=new Hse(n,u,o,l):c=new qse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:\"int32\",data:[b]},{type:\"int32\",data:[y]},{type:\"int32\",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o===\"leakyrelu\"&&(m.push({type:\"float32\",data:[i]}),c.uniforms+=\" alpha : f32,\"),s.runWebGPUProgram(c,g,e.dtype,m)}function Qse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return N2({x:r,filter:a,convInfo:d,backend:s})}var Zse={kernelName:$a,backendName:\"webgpu\",kernelFunc:Qse},Jse=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${$v(this.elementsPerThread,this.workGroupSize)}\n  `}},ere=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function tre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new ere(d);else{f=new Jse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var nre={kernelName:_a,backendName:\"webgpu\",kernelFunc:tre},sre=Kt({opType:2}),rre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:sre},are=Kt({opType:3}),ire={kernelName:Ea,backendName:\"webgpu\",kernelFunc:are},ore=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${i};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${o};\n        if( in_x < 0.0 || in_x > ${t} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `}},ure=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new ore(r.shape[3],a.shape,o,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],\"float32\",p)},lre={kernelName:co,backendName:\"webgpu\",kernelFunc:ure},cre=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=[{type:\"int32\",data:[a]}],g=new cre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var pre={kernelName:po,backendName:\"webgpu\",kernelFunc:dre},T2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${Sv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},$2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,\n      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Dr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new T2(p):(h=new $2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var fre={kernelName:Ra,backendName:\"webgpu\",kernelFunc:hre},_2=mn({opSnippet:0,cpuKernelImpl:jne,supportsComplex:!0}),mre={kernelName:Ya,backendName:\"webgpu\",kernelFunc:_2},gre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32,\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=wi({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=Gne(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=Yne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":cp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new gre(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Av(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"sum\",n)}var bre={kernelName:oi,backendName:\"webgpu\",kernelFunc:Av};function yre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=wi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=_2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Av({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var vre={kernelName:Qd,backendName:\"webgpu\",kernelFunc:yre},xre=Kt({opType:4}),wre={kernelName:Fa,backendName:\"webgpu\",kernelFunc:xre},kre=mn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:Dne}),Ire={kernelName:ho,backendName:\"webgpu\",kernelFunc:kre},A2=Kt({opType:5,cpuKernelImpl:Fne,dtype:\"float32\"}),Sre={kernelName:Oa,backendName:\"webgpu\",kernelFunc:A2};function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Cre={kernelName:fo,backendName:\"webgpu\",kernelFunc:Qm},Nre=Kt({opType:6,cpuKernelImpl:One}),Tre={kernelName:mo,backendName:\"webgpu\",kernelFunc:Nre},$re=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function du(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new $re(s),o=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var _re={kernelName:ml,backendName:\"webgpu\",kernelFunc:du},Are=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},Ere={kernelName:go,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Are(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Rre=Kt({opType:7,cpuKernelImpl:Pne}),Dre={kernelName:Pa,backendName:\"webgpu\",kernelFunc:Rre},Fre=mn({opSnippet:12,dtype:\"int32\"}),Ore={kernelName:za,backendName:\"webgpu\",kernelFunc:Fre},Pre=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},E2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=cne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:\"main\"},label:t.constructor.name})};function R2(e,t,n,s=\"\",r=\"\"){return e.shaderKey+\"_\"+(e.workGroupSize?e.workGroupSize.join(\",\"):\"\")+t.map(i=>i.length).join(\",\")+n.join(\",\")+e.variableNames.join(\",\")+s+r}function Ow(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,\"int32\"),c=n.getFromPixelsProgram(a?\"import\":\"copyExternal\");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?\"import\":\"copyExternal\"],h=R2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>E2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var zre={kernelName:fd,backendName:\"webgpu\",kernelFunc:Mre},Bi;function Mre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let i=typeof HTMLVideoElement!=\"undefined\"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!=\"undefined\"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!=\"undefined\"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!=\"undefined\"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool(\"WEBGPU_USE_IMPORT\")&&i)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Bi==null&&(Bi=document.createElement(\"canvas\").getContext(\"2d\")),Bi.canvas.width=c,Bi.canvas.height=p,Bi.drawImage(r,0,0,c,p),r=Bi.canvas),l||u||i||o)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,\"int32\"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Lre=class{constructor(e,t,n,s,r){this.uniforms=\"varianceEpsilon : f32,\",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\")),this.offsetShape=s,this.scaleShape=r,this.shaderKey=\"batchNorm\"}getUserCode(){let e=\"0.0\";this.offsetShape!=null&&(e=\"getOffsetByOutputIndex(index)\");let t=\"1.0\";return this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},Bre={kernelName:Ma,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Lre(s.shape,i.shape,o.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Vre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return N2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Wre={kernelName:aa,backendName:\"webgpu\",kernelFunc:Vre};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new T2(m,b,d,y):(x=new $2(m,b,d,y),v.push({type:\"int32\",data:[m.filterHeight]},{type:\"int32\",data:[m.filterWidth]},{type:\"int32\",data:[m.outChannels/m.inChannels]})),d===\"leakyrelu\"&&(v.push({type:\"float32\",data:[h]}),x.uniforms+=\" alpha : f32,\"),n.runWebGPUProgram(x,g,\"float32\",v)}var Gre={kernelName:ia,backendName:\"webgpu\",kernelFunc:Ure},Hre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function qre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=zne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Hre(i,[l,c]),m=[{type:\"int32\",data:[i]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var jre={kernelName:yo,backendName:\"webgpu\",kernelFunc:qre},Kre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Xre(this.aShape);return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function Xre(e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let s=0;s<e.length;s++)s===2?n.push(\"indexZ\"):n.push(`${t[s]}`);return n.join()}function D2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=Mne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Kre(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Yre={kernelName:bo,backendName:\"webgpu\",kernelFunc:D2},Qre=mn({opSnippet:5,cpuKernelImpl:Bne,dtype:\"bool\"}),Zre={kernelName:vo,backendName:\"webgpu\",kernelFunc:Qre},Jre=mn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:Lne}),eae={kernelName:La,backendName:\"webgpu\",kernelFunc:Jre};function tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:\"float32\",data:[a]}],o=new nc(r.shape,14);return o.uniforms=\"alpha : f32,\",n.runWebGPUProgram(o,[r],\"float32\",i)}var nae={kernelName:Va,backendName:\"webgpu\",kernelFunc:tae},sae=mn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:Wne}),rae={kernelName:xo,backendName:\"webgpu\",kernelFunc:sae},aae=mn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:Vne}),iae={kernelName:wo,backendName:\"webgpu\",kernelFunc:aae},oae=Kt({opType:9,cpuKernelImpl:Une}),uae={kernelName:Wa,backendName:\"webgpu\",kernelFunc:oae},lae=mn({opSnippet:9,dtype:\"bool\"}),cae={kernelName:ko,backendName:\"webgpu\",kernelFunc:lae},dae=Kt({opType:10}),pae={kernelName:xl,backendName:\"webgpu\",kernelFunc:dae};function F2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,\"max\",n)}var hae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:F2},fae=mn({opSnippet:15,cpuKernelImpl:Hne}),mae={kernelName:Ga,backendName:\"webgpu\",kernelFunc:fae};function gae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});p=new I2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new k2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var bae={kernelName:Ha,backendName:\"webgpu\",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,\"mean\",n)}var vae={kernelName:qa,backendName:\"webgpu\",kernelFunc:yae};function xae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"min\",n)}var wae={kernelName:ja,backendName:\"webgpu\",kernelFunc:xae},kae=mn({opSnippet:16,cpuKernelImpl:qne}),Iae={kernelName:Ka,backendName:\"webgpu\",kernelFunc:kae},Sae=class{constructor(e,t,n){this.uniforms=\"\",this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(\",\"),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:\"\"}`).join(\",\"),s=e===1?\"start\":\"start[i]\",r=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Wt(e),o=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${i}(${t});\n          let end = ${i}(${n});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${s}) {\n              ${a} = ${s} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${r}) {\n              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${o}));\n        }\n      }\n    `}},Cae={kernelName:Xa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:\"int32\",data:[c[0],c[1]]})),u=new Sae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Nae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=Kne(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Tae={kernelName:Io,backendName:\"webgpu\",kernelFunc:Nae};function $ae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var _ae={kernelName:Co,backendName:\"webgpu\",kernelFunc:$ae};function Aae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var Eae={kernelName:No,backendName:\"webgpu\",kernelFunc:Aae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var Rae={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Ld};function O2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=O2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Dae={kernelName:To,backendName:\"webgpu\",kernelFunc:O2};function Fae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let o=[],u=t.map(c=>{let p=Qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=C2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Oae={kernelName:_o,backendName:\"webgpu\",kernelFunc:Fae},Pae=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?\"any(outC < start)\":\"outC < start\",o=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${r};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${o}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}},P2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return es({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return du({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:\"float32\",data:[i]}];a.map(l=>o.push({type:\"int32\",data:[l[0],l[1]]}));let u=new Pae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},zae={kernelName:Qa,backendName:\"webgpu\",kernelFunc:P2},Mae=mn({opSnippet:13}),Lae={kernelName:Za,backendName:\"webgpu\",kernelFunc:Mae};function Bae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new x2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var Vae={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Bae};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"prod\",n)}var Uae={kernelName:Ao,backendName:\"webgpu\",kernelFunc:Wae},Gae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Qne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Hae={kernelName:Il,backendName:\"webgpu\",kernelFunc:Gae},z2=mn({opSnippet:3}),qae={kernelName:Da,backendName:\"webgpu\",kernelFunc:z2},jae=Kt({opType:12}),Kae={kernelName:ei,backendName:\"webgpu\",kernelFunc:jae},Xae=Kt({opType:13}),Yae={kernelName:ni,backendName:\"webgpu\",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Zae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[o?.5:0]}],f=new Qae(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var Jae={kernelName:ti,backendName:\"webgpu\",kernelFunc:Zae},eie=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function tie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new eie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var nie={kernelName:Cl,backendName:\"webgpu\",kernelFunc:tie},sie=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},rie={kernelName:qo,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new sie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},aie=Kt({opType:15,cpuKernelImpl:Zne}),iie={kernelName:si,backendName:\"webgpu\",kernelFunc:aie},oie=class{constructor(e,t,n,s,r,a,i){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${s})`,o=this.type===\"int32\"?\"atomicAdd(&(result[flatIndex]), i32(updateValue));\":`\n     var assumed = atomicLoad(&(result[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${i};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${o}\n        }\n      }`}};function uie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=du({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[o]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new oie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var lie={kernelName:Fo,backendName:\"webgpu\",kernelFunc:uie},cie=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function die(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new cie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var pie={kernelName:Oo,backendName:\"webgpu\",kernelFunc:die},hie=Kt({opType:18}),fie={kernelName:ai,backendName:\"webgpu\",kernelFunc:hie},mie=Kt({opType:16}),gie={kernelName:ri,backendName:\"webgpu\",kernelFunc:mie},bie=Kt({opType:17}),yie={kernelName:zo,backendName:\"webgpu\",kernelFunc:bie},M2=mn({opSnippet:2,cpuKernelImpl:sse,supportsComplex:!0}),vie={kernelName:ci,backendName:\"webgpu\",kernelFunc:M2};function xie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=F2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=M2({inputs:{a:r,b:l},backend:n}),p=A2({inputs:{x:c},backend:n}),d=Av({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=z2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var wie={kernelName:ui,backendName:\"webgpu\",kernelFunc:xie},kie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=P2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},Iie={kernelName:Mo,backendName:\"webgpu\",kernelFunc:kie},Sie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function Cie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:c}],f=new Sie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Nie={kernelName:ip,backendName:\"webgpu\",kernelFunc:Cie};function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var $ie={kernelName:Lo,backendName:\"webgpu\",kernelFunc:Tie},_ie=Kt({opType:19}),Aie={kernelName:ii,backendName:\"webgpu\",kernelFunc:_ie},Eie={kernelName:Al,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Rie=mn({opSnippet:11}),Die={kernelName:li,backendName:\"webgpu\",kernelFunc:Rie},Fie=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=tse(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Fie(h),E=[{type:\"int32\",data:y},{type:\"int32\",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Pie={kernelName:Bo,backendName:\"webgpu\",kernelFunc:Oie};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=nse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var Mie={kernelName:op,backendName:\"webgpu\",kernelFunc:zie},Lie=Kt({opType:21}),Bie={kernelName:di,backendName:\"webgpu\",kernelFunc:Lie},Vie=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=Wie(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Wie(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Uie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=rse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Vie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Gie={kernelName:Cr,backendName:\"webgpu\",kernelFunc:Uie},Hie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},qie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Vi(e,t){t!==null&&e.disposeData(t.dataId)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=ase(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,\"int32\",[])];if(u===1)return[r,du({attrs:{shape:o,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Pw(a),h=Pw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new Hie(N),R=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,\"int32\",R),Vi(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new qie([c,k/2]),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,\"int32\",A),Vi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=cu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Vi(n,b);let y=D2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Vi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Vi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Vi(n,x),[y,f]}var Kie={kernelName:Wo,backendName:\"webgpu\",kernelFunc:jie},Xie=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function Yie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Xie(g),y=i===\"nearest\"?1:2,v;switch(o){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var Qie={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Yie};function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Jie={kernelName:Go,backendName:\"webgpu\",kernelFunc:Zie},eoe=[Cne,use,cse,hse,vse,wse,Ise,Cse,Ase,Fse,Pse,Bse,_ne,Gse,Zse,nre,rre,ire,lre,pre,fre,vre,wre,Ire,Sre,Cre,Tre,_re,Ere,zre,Dre,Ore,Bre,Wre,Gre,jre,Yre,Zre,eae,$ne,Wse,nae,rae,iae,uae,cae,pae,hae,mae,bae,vae,wae,Iae,Cae,mre,Tae,_ae,Eae,Ese,Dae,Oae,zae,Lae,Vae,Uae,Hae,Rse,qae,Kae,Yae,Ine,Jae,nie,rie,iie,lie,pie,fie,gie,yie,$se,Pie,Mie,wie,Iie,Nie,$ie,Aie,Eie,Die,vie,bre,Bie,Gie,Kie,Qie,bse,Jie,Rae];for(let e of eoe)El(e);var toe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=zw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=zw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function zw(e,t){return`${e}_${t}`}var L2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey=\"fromPixels\",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:\"rgba8unorm\",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},noe=class extends L2{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},soe=X().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Mw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[a,a,a]):[a,a,1]},B2=class extends nl{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Tv())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new toe(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*jm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*jm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case\"copyExternal\":return this.fromPixelProgram||(this.fromPixelProgram=new L2),this.fromPixelProgram;case\"import\":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new noe),this.fromPixelImportProgram;default:w.assert(!1,()=>\"Unsupported fromPixels shape\");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=b2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type===\"int32\"?new Int32Array(s,o,a.data.length).set(a.data):a.type===\"uint32\"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Mw(this.device,e);let a=[{type:\"float32\",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o=\"int32\";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join(\"_\"),f=d.map(N=>N.join(\"_\")).join(\";\"),m=R2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>E2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Pre(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Mw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=soe){return X().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Ev=B2;Ev.nextDataId=0;var roe={};Ae(roe,{WebGPUBackend:()=>Ev,webgpu_util:()=>m2});Tv()&&pp(\"webgpu\",async()=>{X().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:X().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has(\"timestamp-query\");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=[\"timestamp-query\"]:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.\");let a=await t.requestDevice(s);return new Ev(a,r)},3);var It=(e=>(e[e.float32=0]=\"float32\",e[e.int32=1]=\"int32\",e[e.bool=2]=\"bool\",e[e.string=3]=\"string\",e[e.complex64=4]=\"complex64\",e))(It||{}),Jp=(e=>(e[e.linear=0]=\"linear\",e[e.relu=1]=\"relu\",e[e.relu6=2]=\"relu6\",e[e.prelu=3]=\"prelu\",e[e.leakyrelu=4]=\"leakyrelu\",e[e.sigmoid=5]=\"sigmoid\",e[e.elu=6]=\"elu\",e))(Jp||{}),V2;function aoe(e){V2=e.wasm.cwrap(ra,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ioe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return V2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var ooe={kernelName:ra,backendName:\"wasm\",setupFunc:aoe,kernelFunc:ioe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var uoe=Xt(io);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var loe=!0,coe=gn(Ir,loe),W2;function doe(e){W2=e.wasm.cwrap(ka,null,[\"array\",\"number\",\"number\",\"number\"])}function poe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return W2(a,r.length,It[s.dtype],i),s}var hoe={kernelName:ka,backendName:\"wasm\",setupFunc:doe,kernelFunc:poe};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var foe={kernelName:Ba,backendName:\"wasm\",kernelFunc:eh},U2;function moe(e){U2=e.wasm.cwrap(pi,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function wr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=boe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=goe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return U2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function goe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function boe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var yoe={kernelName:pi,backendName:\"wasm\",kernelFunc:wr,setupFunc:moe};function Fr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=wr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var G2;function voe(e){G2=e.wasm.cwrap(al,null,[\"number, number, number\"])}function xoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;G2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var woe={kernelName:al,backendName:\"wasm\",setupFunc:voe,kernelFunc:xoe},H2;function koe(e){H2=e.wasm.cwrap(il,null,[\"number, number, number\"])}function Ioe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;H2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Soe={kernelName:il,backendName:\"wasm\",setupFunc:koe,kernelFunc:Ioe},q2;function Coe(e){q2=e.wasm.cwrap(Ia,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Noe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Fr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return q2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Toe={kernelName:Ia,backendName:\"wasm\",kernelFunc:Noe,setupFunc:Coe},j2;function $oe(e){j2=e.wasm.cwrap(Sa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _oe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return j2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var Aoe={kernelName:Sa,backendName:\"wasm\",setupFunc:$oe,kernelFunc:_oe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Eoe={kernelName:Eo,backendName:\"wasm\",kernelFunc:yn},K2;function Roe(e){K2=e.wasm.cwrap(Ca,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function Doe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),N=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,A=n.dataIdMap.get(N.dataId).id,P=i?T.shape[2]:T.shape[1],R=o?N.shape[1]:N.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return K2(E,W,T.shape.length,A,q,N.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(N.dataId),$.shape=v,$}var Foe={kernelName:Ca,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};function va(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype===\"string\"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Ooe(u,c[0],d,a,i);else if(h===3)Poe(u,c[0],c[1],d,a,i);else if(h===4)zoe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Ooe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Poe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function zoe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Moe={kernelName:Po,backendName:\"wasm\",kernelFunc:va};function Loe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=va({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Boe={kernelName:oo,backendName:\"wasm\",kernelFunc:Loe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Voe={kernelName:Na,backendName:\"wasm\",kernelFunc:ac},Woe=Xt(Ta),X2;function Uoe(e){X2=e.wasm.cwrap(Sr,null,[\"number\",\"number\",\"number\",\"number\"])}function Goe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return X2(o,a,i,l),u}var Hoe={kernelName:Sr,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};function Y2(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=S.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return eh({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(S.assertParamsConsistent(o,s),a[0].dtype===\"string\"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return yn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=S.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=tv(f,r,t[0].dtype,m),b=S.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=S.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var qoe={kernelName:uo,backendName:\"wasm\",kernelFunc:Y2},Q2;function joe(e){Q2=e.wasm.cwrap($a,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Koe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=S.convertConv2DDataFormat(d),f=S.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Qoe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=S.convertConv2DDataFormat(u),h=S.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:T,strideHeight:N,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat===\"channelsLast\",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,\"float32\"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return Z2(me,ke,f,m,g,y,v,b,k,T,x,N,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Zoe={kernelName:_a,backendName:\"wasm\",setupFunc:Yoe,kernelFunc:Qoe},Joe=Xt(Aa),eue=Xt(Ea),J2=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(J2||{}),eN;function tue(e){eN=e.wasm.cwrap(co,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function nue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!==\"float32\"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return eN(g,b,y,c,k,p,d,J2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var sue={kernelName:co,backendName:\"wasm\",setupFunc:tue,kernelFunc:nue},tN;function rue(e){tN=e.wasm.cwrap(hl,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function aue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;tN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var iue={kernelName:hl,backendName:\"wasm\",setupFunc:rue,kernelFunc:aue},nN;function oue(e){nN=e.wasm.cwrap(lo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function uue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;nN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var lue={kernelName:lo,backendName:\"wasm\",setupFunc:oue,kernelFunc:uue},sN;function cue(e){sN=e.wasm.cwrap(po,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function due(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return sN(b,a,i===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var pue={kernelName:po,backendName:\"wasm\",setupFunc:cue,kernelFunc:due},rN;function hue(e){rN=e.wasm.cwrap(Ra,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=S.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,\"float32\"),F=s.dataIdMap.get(R.dataId).id;return rN(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,T,N,E,A,F),R}var mue={kernelName:Ra,backendName:\"wasm\",setupFunc:hue,kernelFunc:fue},gue=Xt(Fa),bue=!1,yue=gn(ho,bue,\"bool\"),vue=Xt(Oa,\"float32\");function Zm(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:o}})}var xue={kernelName:fo,backendName:\"wasm\",kernelFunc:Zm};function aN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var wue={kernelName:ml,backendName:\"wasm\",kernelFunc:aN},iN;function kue(e){iN=e.wasm.cwrap(go,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Iue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return iN(a,o,u,l,c,i),r}var Sue={kernelName:go,backendName:\"wasm\",kernelFunc:Iue,setupFunc:kue},Cue=Xt(Pa),Nue=!1,Tue=gn(za,Nue),oN;function $ue(e){oN=e.wasm.cwrap(Ma,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function _ue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return oN(c,p,d,h,f,r,g),m}var Aue={kernelName:Ma,backendName:\"wasm\",setupFunc:$ue,kernelFunc:_ue},uN;function Eue(e){uN=e.wasm.cwrap(aa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Rue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return uN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Due={kernelName:aa,backendName:\"wasm\",setupFunc:Eue,kernelFunc:Rue},lN;function Fue(e){lN=e.wasm.cwrap(ia,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Oue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return lN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Pue={kernelName:ia,backendName:\"wasm\",setupFunc:Fue,kernelFunc:Oue},cN;function zue(e){cN=e.wasm.cwrap(yo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Mue(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=$k.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return cN(h,It[s.dtype],m,i,p,o,g,b),l}var Lue={kernelName:yo,backendName:\"wasm\",setupFunc:zue,kernelFunc:Mue},dN;function Bue(e){dN=e.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Vue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let A=0;A<l.length;++A){let P=l[A];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,T=t.dataIdMap.get(g.dataId).id,N=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return dN(v,It[r.dtype],N,b,k,p.batchSize,E,T),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Wue={kernelName:bo,backendName:\"wasm\",setupFunc:Bue,kernelFunc:Vue},Uue=!1,Gue=gn(vo,Uue,\"bool\"),Hue=!1,que=gn(La,Hue,\"bool\"),pN;function jue(e){pN=e.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\"])}function Kue(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,\"float32\");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;pN(r,It[t.dtype],n,i)}return a}var Xue={kernelName:Va,backendName:\"wasm\",setupFunc:jue,kernelFunc:Kue},Yue=!1,Que=gn(xo,Yue,\"bool\"),Zue=!1,Jue=gn(wo,Zue,\"bool\"),ele=Xt(Wa),tle=!1,nle=gn(ko,tle,\"bool\"),hN;function sle(e){hN=e.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\"])}function rle(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;hN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var ale={kernelName:Ua,backendName:\"wasm\",setupFunc:sle,kernelFunc:rle},ile=!1,ole=gn(Ga,ile),fN;function ule(e){fN=e.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function lle(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,T=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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x=t.dataIdMap.get(v.dataId).id;mN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!==\"float32\"&&t.disposeData(y.dataId),v}var hle={kernelName:qa,backendName:\"wasm\",setupFunc:dle,kernelFunc:ple},gN;function fle(e){gN=e.wasm.cwrap(ja,null,[\"number\",\"number\",\"number\",\"number\"])}function mle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"min\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;gN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var gle={kernelName:ja,backendName:\"wasm\",setupFunc:fle,kernelFunc:mle},ble=!1,yle=gn(Ka,ble),bN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(bN||{}),yN;function vle(e){yN=e.wasm.cwrap(Xa,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function xle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return yN(i,l,t.shape.length,It[t.dtype],d,h,bN[r],u),o}var wle={kernelName:Xa,backendName:\"wasm\",kernelFunc:xle,setupFunc:vle},kle=!0,Ile=gn(Ya,kle),Sle=Xt(Io);function Rv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var vN;function Cle(e){vN=e.wasm.cwrap(Co,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Nle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=vN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Rv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],\"int32\",d)}var Tle={kernelName:Co,backendName:\"wasm\",setupFunc:Cle,kernelFunc:Nle},xN;function $le(e){xN=e.wasm.cwrap(kl,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function _le(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=xN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var Ale={kernelName:kl,backendName:\"wasm\",setupFunc:$le,kernelFunc:_le},wN;function Ele(e){wN=e.wasm.cwrap(No,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Rle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=wN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var Dle={kernelName:No,backendName:\"wasm\",setupFunc:Ele,kernelFunc:Rle},Fle=!1,Ole=gn(So,Fle,\"bool\"),kN;function Ple(e){kN=e.wasm.cwrap($o,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function zle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return kN(p,a,i,o,l),u}var Mle={kernelName:$o,backendName:\"wasm\",setupFunc:Ple,kernelFunc:zle};function Lle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Ble={kernelName:To,backendName:\"wasm\",kernelFunc:Lle};function Vle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Zm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack 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SN={kernelName:Qa,backendName:\"wasm\",kernelFunc:Gle,setupFunc:Ule},Hle=!1,qle=gn(Za,Hle),CN;function jle(e){CN=e.wasm.cwrap(Ja,null,[\"number\",\"number\",\"number\"])}function Kle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!==\"float32\"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return CN(o,i,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var Xle={kernelName:Ja,backendName:\"wasm\",setupFunc:jle,kernelFunc:Kle},NN;function Yle(e){NN=e.wasm.cwrap(Ao,null,[\"number\",\"number\",\"number\",\"number\"])}function Qle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=S.getInnerMostAxes(f.length,l.shape.length))}S.assertAxesAreInnerMostDims(\"prod\",f,l.shape.length);let[m,g]=S.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;NN(u,b,It[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Zle={kernelName:Ao,backendName:\"wasm\",setupFunc:Yle,kernelFunc:Qle},Jle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=rv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},ece={kernelName:Il,backendName:\"wasm\",kernelFunc:Jle},tce=!0,nce=gn(Da,tce),sce=Xt(ei),rce=Xt(ni),TN;function ace(e){TN=e.wasm.cwrap(ti,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");Bd=e,Dv=t}function Bpe(e,t=!1){if(Bu)throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");if(typeof e==\"string\")Pu=e;else{Lu=e;let n=Nde.filter(s=>Lu[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Dv=t}var HN=-1,eg=-1;function Vpe(e){HN=e}function Wpe(){if(eg===-1)throw new Error(\"WASM backend not initialized.\");return eg}var Upe=\"0.0.0\",Tde=2;pp(\"wasm\",async()=>{let{wasm:e}=await Sde();return new kde(e)},Tde);var sr=\"3.15.0-20220405\",Gpe={tfjs:sr,\"tfjs-core\":sr,\"tfjs-data\":sr,\"tfjs-layers\":sr,\"tfjs-converter\":sr,\"tfjs-backend-cpu\":sr,\"tfjs-backend-webgl\":sr,\"tfjs-backend-wasm\":sr};export{io as Abs,sl as Acos,rl as Acosh,fb as AdadeltaOptimizer,mb as AdagradOptimizer,gb as AdamOptimizer,bb as AdamaxOptimizer,Ir as Add,ka as AddN,al as All,il as Any,Ia as ArgMax,ol as ArgMin,ul as Asin,ll as Asinh,cl as Atan,pl as Atan2,dl as Atanh,Sa as AvgPool,qd as AvgPool3D,og as AvgPool3DGrad,ig as AvgPoolGrad,kde as BackendWasm,Ca as BatchMatMul,oo as BatchToSpaceND,ug as Bincount,lg as BroadcastArgs,A$ as BroadcastTo,CW as Callback,zL as CallbackList,Na as Cast,Ta as Ceil,Sr as ClipByValue,jd as Complex,Kd as ComplexAbs,uo as Concat,$a as Conv2D,cg as Conv2DBackpropFilter,_a as Conv2DBackpropInput,Xd as Conv3D,dg as Conv3DBackpropFilterV2,pg as Conv3DBackpropInputV2,Aa as Cos,Ea as Cosh,co as CropAndResize,hl as Cumprod,lo as Cumsum,BL as CustomCallback,Ud as DataStorage,hg as DenseBincount,po as DepthToSpace,Ra as DepthwiseConv2dNative,fg as DepthwiseConv2dNativeBackpropFilter,mg as DepthwiseConv2dNativeBackpropInput,gg as Diag,Yd as Dilation2D,Qf as Dilation2DBackpropFilter,Yf as Dilation2DBackpropInput,Jw as ENV,NW as EarlyStopping,Qd as Einsum,Fa as Elu,bg as EluGrad,S$ as Environment,ho as Equal,fl as Erf,Oa as Exp,fo as ExpandDims,mo as Expm1,yg as FFT,ml as Fill,go as FlipLeftRight,Pa as Floor,za as FloorDiv,fd as FromPixels,Ma as FusedBatchNorm,aa as FusedConv2D,ia as FusedDepthwiseConv2D,Kf as GPGPUContext,yo as GatherNd,bo as GatherV2,E4 as GraphModel,vo as Greater,La as GreaterEqual,LL as History,vg as IFFT,Ba as Identity,Zd as Imag,Dt as InputSpec,gl as IsFinite,bl as IsInf,yl as IsNan,nl as KernelBackend,ep as LRN,wg as LRNGrad,AL as LayerVariable,pr as LayersModel,Va as LeakyRelu,xo as Less,wo as LessEqual,xg as LinSpace,Wa as Log,vl as Log1p,E$ as LogSoftmax,ko as LogicalAnd,xl as LogicalNot,Jd as LogicalOr,L0 as MathBackendCPU,M1 as MathBackendWebGL,Ua as Max,Ha as MaxPool,tp as MaxPool3D,Ig as MaxPool3DGrad,kg as MaxPoolGrad,Sg as MaxPoolWithArgmax,Ga as Maximum,qa as Mean,ja as Min,Ka as Minimum,Xa as MirrorPad,wl as Mod,yb as MomentumOptimizer,Cg as Multinomial,Ya as Multiply,Io as Neg,Co as NonMaxSuppressionV3,kl as NonMaxSuppressionV4,No as NonMaxSuppressionV5,So as NotEqual,l_ as OP_SCOPE_SUFFIX,$o as OneHot,To as OnesLike,_r as Optimizer,Wr as OptimizerConstructors,_o as Pack,Qa as PadV2,$de as Pool,Za as Pow,Ja as Prelu,Ao as Prod,vb as RMSPropOptimizer,Ar as RNN,Il as Range,e_ as Rank,np as Real,Da as RealDiv,Sl as Reciprocal,oO as Reduction,ei as Relu,ni as Relu6,Eo as Reshape,ti as ResizeBilinear,Tg as ResizeBilinearGrad,Cl as ResizeNearestNeighbor,Ng as ResizeNearestNeighborGrad,Ro as Reverse,qo as RotateWithOffset,Do as Round,si as Rsqrt,Ip as SGDOptimizer,Fo as ScatterNd,Oo as Select,Nl as Selu,Ub as Sequential,ai as Sigmoid,Tl as Sign,ri as Sin,zo as Sinh,Po as Slice,ui as Softmax,$l as Softplus,Mo as SpaceToBatchND,sp as SparseFillEmptyRows,_l as SparseReshape,rp as SparseSegmentMean,ap as SparseSegmentSum,ip as SparseToDense,Lo as SplitV,ii as Sqrt,Al as Square,li as SquaredDifference,hi as Step,Bo as StridedSlice,op as StringNGrams,$g as StringSplit,_g as StringToHashBucketFast,ci as Sub,oi as Sum,$s as SymbolicTensor,Vo as Tan,di as Tanh,et as Tensor,Vt as TensorBuffer,Cr as Tile,Wo as TopK,Uo as Transform,pi as Transpose,Ag as Unique,Go as Unpack,up as UnsortedSegmentSum,gd as Variable,Ho as ZerosLike,ra as _FusedMatMul,Mt as abs,GA as acos,qA as acosh,ie as add,KA as addN,qk as all,pm as any,Hu as argMax,JA as argMin,tE as asin,sE as asinh,aE as atan,oE as atan2,lE as atanh,Gg as avgPool,Yk as avgPool3d,zA as backend,S as backend_util,qde as basicLSTMCell,ju as batchNorm,$E as batchNorm2d,AE as batchNorm3d,RE as batchNorm4d,Hg as batchToSpaceND,Qk as bincount,xpe as booleanMaskAsync,OE as broadcastArgs,nd as broadcastTo,jo as broadcast_util,Nk as browser,De as buffer,Ape as callbacks,ce as cast,ME as ceil,Vn as clipByValue,lr as clone,oa as complex,Ft as concat,VE as concat1d,UE as concat2d,HE as concat3d,jE as concat4d,MM as constraints,Zk as conv1d,ca as conv2d,Jk as conv2dTranspose,eI as conv3d,tR as conv3dTranspose,Ede as copyRegisteredKernels,jg as cos,nI as cosh,TI as cosineWindow,aR as cumprod,sI as cumsum,js as customGrad,R4 as data,uR as denseBincount,Gk as deprecationWarn,cR as depthToSpace,hp as depthwiseConv2d,Rpe as deregisterOp,dp as device_util,jde as diag,fR as dilation2d,Ode as disableDeprecationWarnings,Re as dispose,Pde as disposeVariables,xe as div,vR as divNoNan,Kde as dot,nF as dropout,kR as einsum,fp as elu,Fde as enableDebugMode,Dde as enableProdMode,sF as enclosingPowerOfTwo,Ss as engine,X as env,Kn as equal,CR as erf,Xn as exp,Pn as expandDims,_R as expm1,rI as eye,lb as fft,Pl as fill,Ude as findBackend,Gde as findBackendFactory,mp as floor,Hk as floorDiv,GX as forceHalfFloat,ha as fused,Ku as gather,J3 as gatherND,$k as gather_util,Vde as getBackend,nx as getGradient,Zf as getKernel,Jf as getKernelsForBackend,Wpe as getThreadsCount,RK as gpgpu_util,Qde as grad,Zde as grads,Wn as greater,Ko as greaterEqual,Id as ifft,Kg as imag,qn as image,kpe as inTopKAsync,qM as initializers,OB as input,En as io,xI as irfft,Xde as isFinite,Yde as isInf,BR as isNaN,Ht as keep,xs as kernel_impls,$L as layers,Xg as leakyRelu,aI as less,Xo as lessEqual,VO as linalg,GR as linspace,Dpe as loadGraphModel,$pe as loadLayersModel,qR as localResponseNormalization,Yn as log,Yg as log1p,tpe as logSigmoid,iI as logSoftmax,iD as logSumExp,Ds as logicalAnd,Jg as logicalNot,cI as logicalOr,npe as logicalXor,Cpe as losses,We as matMul,iA as math,As as max,eb as maxPool,dI as maxPool3d,fD as maxPoolWithArgmax,$r as maximum,St as mean,dm as memory,spe as meshgrid,rW as metrics,fm as min,bp as minimum,xD as mirrorPad,kD as mod,Npe as model,xW as models,tb as moments,wpe as movingAverage,V as mul,rpe as multiRNNCell,TD as multinomial,kt as neg,OI as nextFrame,CI as norm,Xu as notEqual,vd as oneHot,Mn as ones,Qn as onesLike,L as op,ape as outerProduct,fi as pad,ipe as pad1d,ope as pad2d,upe as pad3d,lpe as pad4d,cpe as pool,pa as pow,sb as prelu,U_ as print,pI as prod,zde as profile,dpe as rand,ppe as randomGamma,KD as randomNormal,Ml as randomUniform,Yu as range,Bde as ready,wd as real,ZD as reciprocal,pp as registerBackend,_pe as registerCallbackConstructor,D$ as registerGradient,El as registerKernel,Epe as registerOp,wW as regularizers,Xs as relu,hI as relu6,Wde as removeBackend,G as reshape,Zn as reverse,hpe as reverse1d,fpe as reverse2d,mpe as reverse3d,gpe as reverse4d,cb as rfft,fI as round,mI as rsqrt,we as scalar,X3 as scatterND,Ak as scatter_util,gI as selu,c3 as separableConv2d,Tpe as sequential,ae as serialization,Lde as setBackend,Hde as setPlatform,Vpe as setThreadsCount,Lpe as setWasmPath,Bpe as setWasmPaths,S5 as setWebGLContext,p3 as setdiff1dAsync,Zy as shared,qs as sigmoid,f3 as sign,Spe as signal,bI as sin,yI as sinh,He as slice,ib as slice1d,vI as slice2d,ob as slice3d,kd as slice4d,wt as slice_util,ub as softmax,zl as softplus,nb as spaceToBatchND,Wc as sparse,NI as sparseToDense,Ipe as spectral,Bn as split,dn as sqrt,ct as square,wI as squaredDifference,mr as squeeze,Jn as stack,yp as step,D3 as stridedSlice,Mf as string,ge as sub,ve as sum,cp as sumOutType,O3 as tan,qu as tanh,fs as tensor,Zt as tensor1d,Ki as tensor2d,pA as tensor3d,bpe as tensor4d,ype as tensor5d,vpe as tensor6d,_s as tensor_util,_A as test_util,j as tidy,ps as tile,Mde as time,z3 as topk,Pi as train,qe as transpose,db as truncatedNormal,hx as unique,Ade as unregisterGradient,_de as unregisterKernel,V3 as unsortedSegmentSum,Fs as unstack,cn as upcastType,w as util,Jde as valueAndGrad,epe as valueAndGrads,U3 as variable,XR as variableGrads,Gpe as version,Fpe as version_converter,Rde as version_core,Ope as version_cpu,uS as version_layers,Upe as version_wasm,Ppe as version_webgl,zpe as webgl,I5 as webgl_util,roe as webgpu,vn as where,II as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        env.webgl.supported = false;\n        config.filter.enabled = false;\n        copy(inCanvas, outCanvas); // filter failed to initialize\n        // return { tensor: null, canvas: inCanvas };\n      } else {\n        fx.reset();\n        if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n        if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n        if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n        if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n        if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n        if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n        if (config.filter.negative) fx.add('negative');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.vintage) fx.add('brownie');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.kodachrome) fx.add('kodachrome');\n        if (config.filter.technicolor) fx.add('technicolor');\n        if (config.filter.polaroid) fx.add('polaroid');\n        if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n        if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n        else outCanvas = fx.draw(inCanvas);\n      }\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  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[0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "import type { Tensor } from '../tfjs/types';\n\nconst attentionDefinitions = {\n  eyeLLower: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  eyeRLower: [263, 249, 390, 373, 374, 380, 381, 382, 362], // 9\n  // eslint-disable-next-line max-len\n  lips: [61, 76, 91, 181, 84, 17, 314, 405, 321, 291, 291, 185, 40, 39, 37, 0, 267, 269, 270, 291, 62, 183, 88, 178, 87, 14, 268, 303, 304, 408, 291, 184, 42, 178, 87, 14, 268, 303, 304, 408, 61, 62, 90, 180, 85, 16, 315, 404, 307, 308, 291, 185, 40, 73, 72, 0, 302, 269, 270, 409, 61, 184, 95, 179, 86, 15, 316, 403, 324, 408, 291, 184, 74, 41, 38, 11, 268, 303, 304, 408],\n  // eslint-disable-next-line max-len\n  eyeL: [33, 7, 163, 144, 145, 153, 154, 155, 133, 246, 161, 160, 159, 158, 157, 173, 130, 25, 110, 24, 23, 22, 26, 112, 243, 247, 30, 29, 27, 28, 56, 190, 226, 31, 228, 229, 230, 231, 232, 233, 244, 113, 225, 224, 223, 222, 221, 189, 35, 124, 46, 53, 52, 65, 143, 111, 117, 118, 119, 120, 121, 128, 245, 156, 70, 63, 105, 66, 107, 55, 193],\n  // eslint-disable-next-line max-len\n  eyeR: [263, 249, 390, 373, 374, 380, 381, 382, 362, 466, 388, 387, 386, 385, 384, 398, 359, 255, 339, 254, 253, 252, 256, 341, 463, 467, 260, 259, 257, 258, 286, 414, 446, 261, 448, 449, 450, 451, 452, 453, 464, 342, 445, 444, 443, 442, 441, 413, 265, 353, 276, 283, 282, 295, 372, 340, 346, 347, 348, 349, 350, 357, 465, 383, 300, 293, 334, 296, 336, 285, 417],\n};\n\n/*\n// function used to determine heuristic mapping\n// values in attentionDefinitions are based on top values from 200 iterations\nfunction replaceClosestPoint(rawCoords, newCoords) {\n  const res: number[] = [];\n  for (let i = 0; i < newCoords.length / 2; i++) {\n    let minDist = Number.MAX_VALUE;\n    let minDistIdx = -1;\n    const pts = rawCoords.map((r) => [r[0], r[1]]);\n    for (let j = 0; j < rawCoords.length; j++) {\n      const x = pts[j][0] - newCoords[2 * i + 0];\n      const y = pts[j][1] - newCoords[2 * i + 1];\n      const dist = (x * x) + (y * y);\n      if (dist < minDist) {\n        minDist = dist;\n        minDistIdx = j;\n      }\n    }\n    res.push(minDistIdx);\n    rawCoords[minDistIdx] = [newCoords[2 * i + 0], newCoords[2 * i + 1], rawCoords[minDistIdx][2]];\n  }\n  return rawCoords;\n}\n*/\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    irisL: results[3].dataSync() as Float32Array, // 5 x 2d // output_left_iris\n    irisR: results[1].dataSync() as Float32Array, // 5 x 2d // output_right_iris\n    eyeL: results[0].dataSync() as Float32Array, // 71 x 2d // output_left_eye\n    eyeR: results[6].dataSync() as Float32Array, // 71 x 2d // output_right_eye\n    lips: results[5].dataSync() as Float32Array, // 80 x 2d // output_lips\n    // flag: results[4], // already processed in parent // conv_faceflag\n    // mesh: results[2], // already have it in rawCoords // output_mesh_identity\n  };\n\n  // rawCoords = replaceClosestPoint(rawCoords, t.eyeL);\n  // rawCoords = replaceClosestPoint(rawCoords, t.eyeR);\n  // rawCoords = replaceClosestPoint(rawCoords, t.lips);\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisRDepth = attentionDefinitions.eyeRLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeRLower.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n  const irisLDepth = attentionDefinitions.eyeLLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeLLower.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[attentionDefinitions.eyeL[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[attentionDefinitions.eyeL[i]][2]];\n  // for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords.push([t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], 0]);\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[attentionDefinitions.eyeR[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[attentionDefinitions.eyeR[i]][2]];\n  // for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords.push([t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], 0]);\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  // for (let i = 0; i < t.lips.length / 2; i++) rawCoords[attentionDefinitions.lips[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[attentionDefinitions.lips[i]][2]];\n  // for (let i = 0; i < t.lips.length / 2; i++) rawCoords.push([t.lips[2 * i + 0], t.lips[2 * i + 1], 0]);\n\n  return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidence = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const contourCoords = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // run iris model and augment results\n        }\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n      tf.dispose([...results, coordsReshaped]);\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention?.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 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x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should face attention keypoints be highlighted */\n  drawAttention: boolean;\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport { options, DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\nexport const colorDepth = (z: number, rgb: [boolean, boolean, boolean] = [true, true, false]): string => {\n  const r = rgb[0] ? 127 + Math.trunc(3 * z) : 255;\n  const g = rgb[1] ? 127 - Math.trunc(3 * z) : 255;\n  const b = rgb[2] ? 127 - Math.trunc(3 * z) : 255;\n  return `rgba(${r}, ${g}, ${b}, ${options.alpha})`;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? colorDepth(z, z === -255 ? [true, false, true] : [true, false, false]) : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? colorDepth(z) : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? colorDepth(z) : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        const length = Math.max(468, f.mesh.length);\n        for (let i = 0; i < length; i++) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n      }\n      if (localOptions.drawAttention && f.mesh.length > 468) {\n        for (let i = 468; i < f.mesh.length; i++) point(ctx, f.mesh[i][0], f.mesh[i][1], -255, localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? colorDepth(result[i].keypoints[pt].position[2] || 0) : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? colorDepth(pt.position[2]) : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? colorDepth(pt[2] || 0) : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? colorDepth(z) : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? colorDepth(i * z) : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor, GraphModel } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n    log('compile pass: skip');\n    return;\n  }\n  const models = Object.values(allModels).filter((m) => m !== null) as GraphModel[];\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  for (const model of models) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    const res = await model.executeAsync(tensor);\n    if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n    else tf.dispose(res);\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass kernels:', kernels.length);\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    // await runCompile(instance.models);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, 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*/\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    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The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=l;else{let p=e[c.id];e[c.id]=s(p,l),p.dispose()}}}}var ox=20,Cu=3,Pf=7;function J$(e,t,n,s){let r=oo(t),a=e_(e,t,n,r),i=t.length,o=ed(e,t,n,r,a),u=[\"Tensor\"];return s&&(u.push(`  dtype: ${n}`),u.push(`  rank: ${i}`),u.push(`  shape: [${t}]`),u.push(\"  values:\")),u.push(o.map(l=>\"    \"+l).join(`\n`)),u.join(`\n`)}function e_(e,t,n,s){let r=pt(t),a=s[s.length-1],i=new Array(a).fill(0),o=t.length,u=n===\"complex64\"?Au(e):e;if(o>1)for(let l=0;l<r/a;l++){let c=l*a;for(let p=0;p<a;p++)i[p]=Math.max(i[p],_u(u[c+p],0,n).length)}return i}function _u(e,t,n){let s;return Array.isArray(e)?s=`${parseFloat(e[0].toFixed(Pf))} + ${parseFloat(e[1].toFixed(Pf))}j`:or(e)?s=`'${e}'`:n===\"bool\"?s=ok(e):s=parseFloat(e.toFixed(Pf)).toString(),Mu(s,t)}function ok(e){return e===0?\"false\":\"true\"}function ed(e,t,n,s,r,a=!0){let i=n===\"complex64\"?2:1,o=t[0],u=t.length;if(u===0){if(n===\"complex64\"){let m=Au(e);return[_u(m[0],0,n)]}return n===\"bool\"?[ok(e[0])]:[e[0].toString()]}if(u===1){if(o>ox){let g=Cu*i,b=Array.from(e.slice(0,g)),y=Array.from(e.slice((o-Cu)*i,o*i));return n===\"complex64\"&&(b=Au(b),y=Au(y)),[\"[\"+b.map((v,x)=>_u(v,r[x],n)).join(\", \")+\", ..., \"+y.map((v,x)=>_u(v,r[o-Cu+x],n)).join(\", \")+\"]\"]}let m=n===\"complex64\"?Au(e):Array.from(e);return[\"[\"+m.map((g,b)=>_u(g,r[b],n)).join(\", \")+\"]\"]}let l=t.slice(1),c=s.slice(1),p=s[0]*i,d=[];if(o>ox){for(let m=0;m<Cu;m++){let g=m*p,b=g+p;d.push(...ed(e.slice(g,b),l,n,c,r,!1))}d.push(\"...\");for(let m=o-Cu;m<o;m++){let g=m*p,b=g+p;d.push(...ed(e.slice(g,b),l,n,c,r,m===o-1))}}else for(let m=0;m<o;m++){let g=m*p,b=g+p;d.push(...ed(e.slice(g,b),l,n,c,r,m===o-1))}let h=u===2?\",\":\"\";d[0]=\"[\"+d[0]+h;for(let m=1;m<d.length-1;m++)d[m]=\" \"+d[m]+h;let f=`,\n`;for(let m=2;m<u;m++)f+=`\n`;return d[d.length-1]=\" \"+d[d.length-1]+\"]\"+(a?\"\":f),d}function Au(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Vt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=pt(e),n!=null){let s=n.length;O(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t===\"complex64\")throw new Error(\"complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");this.values=n||Yw(t,this.size),this.strides=oo(e)}set(e,...t){t.length===0&&(t=[0]),O(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let s of e){if(s<0||s>=this.shape[t]){let r=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return cs().makeTensor(this.values,this.shape,this.dtype)}},cs=null,Gi=null,t_=null;function n_(e){cs=e}function s_(e){Gi=e}function r_(e){t_=e}var et=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||\"float32\",this.size=pt(e),this.strides=oo(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():\"higher\"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Gi.buffer(this.shape,this.dtype,e)}bufferSync(){return Gi.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ji(this.shape,e,this.dtype===\"complex64\")}arraySync(){return ji(this.shape,this.dataSync(),this.dtype===\"complex64\")}async data(){this.throwIfDisposed();let e=cs().read(this.dataId);if(this.dtype===\"string\"){let t=await e;try{return t.map(n=>md(n))}catch(n){throw new Error(\"Failed to decode the string bytes into utf-8. 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To get the original bytes, call tensor.bytes().\")}return e}async bytes(){this.throwIfDisposed();let e=await cs().read(this.dataId);return this.dtype===\"string\"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(cs().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error(\"Tensor is disposed.\")}print(e=!1){return Gi.print(this,e)}clone(){return this.throwIfDisposed(),Gi.clone(this)}toString(e=!1){let t=this.dataSync();return J$(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Gi.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),cs().makeVariable(this,e,t,n)}};Object.defineProperty(et,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function a_(){return og(\"Tensor\",()=>et)}a_();var gd=class extends et{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Ir(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);cs().disposeTensor(this),this.dataId=e.dataId,cs().incRef(this,null)}dispose(){cs().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(gd,Symbol.hasInstance,{value:e=>e instanceof et&&e.assign!=null&&e.assign instanceof Function});var _s={};Ae(_s,{assertTypesMatch:()=>pk,getTensorsInContainer:()=>Fg,isTensorInList:()=>u_,makeTypesMatch:()=>vt});var i_=(e=>(e.R0=\"R0\",e.R1=\"R1\",e.R2=\"R2\",e.R3=\"R3\",e.R4=\"R4\",e.R5=\"R5\",e.R6=\"R6\",e))(i_||{}),uk=(e=>(e.float32=\"float32\",e.int32=\"int32\",e.bool=\"int32\",e.complex64=\"complex64\",e))(uk||{}),lk=(e=>(e.float32=\"float32\",e.int32=\"int32\",e.bool=\"bool\",e.complex64=\"complex64\",e))(lk||{}),ck=(e=>(e.float32=\"float32\",e.int32=\"float32\",e.bool=\"float32\",e.complex64=\"complex64\",e))(ck||{}),dk=(e=>(e.float32=\"complex64\",e.int32=\"complex64\",e.bool=\"complex64\",e.complex64=\"complex64\",e))(dk||{}),o_={float32:ck,int32:uk,bool:lk,complex64:dk};function cn(e,t){if(e===\"string\"||t===\"string\"){if(e===\"string\"&&t===\"string\")return\"string\";throw new Error(`Can not upcast ${e} with ${t}`)}return o_[e][t]}function pp(e){return cn(e,\"int32\")}function vt(e,t){if(e.dtype===t.dtype)return[e,t];let n=cn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function pk(e,t){O(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function u_(e,t){return t.some(n=>n.id===e.id)}function Fg(e){let t=[];return hk(e,t,new Set),t}function hk(e,t,n){if(e==null)return;if(e instanceof et){t.push(e);return}if(!l_(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),hk(a,t,n))}}function l_(e){return Array.isArray(e)||typeof e==\"object\"}function zf(e){return e.kernelName!=null}var ux=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},nm=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ux}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(ir(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new K$(this.backendInstance),!0}setupRegisteredKernels(){em(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){em(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof sl)&&typeof n.then==\"function\"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ir(`Initialization of backend ${e} failed`),ir(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ir(`Initialization of backend ${e} failed`),ir(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof t!=\"function\")throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return nm.nextTensorId++}nextVariableId(){return nm.nextVariableId++}clone(e){let t=M.runKernel(Wa,{x:e}),n={x:e},s=a=>({x:()=>{let i=\"float32\",o={x:a},u={dtype:i};return M.runKernel(Ta,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Jf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=zf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(zf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Jf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=zf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=rx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&or(e[0])&&(r=e.map(o=>Dl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n===\"string\"){let o=this.state.tensorInfo.get(a),u=Jw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new gd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*Yf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*Yf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=rx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=jd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Fg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let 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s!=null&&(c=_(s,\"offset\",\"batchNorm\")),O(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),O(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),O(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Ku(i,o,u,c,l,a)}var PE=L({batchNorm3d_:OE});function zE(e,t,n,s,r,a){let i=_(e,\"x\",\"batchNorm\"),o=_(t,\"mean\",\"batchNorm\"),u=_(n,\"variance\",\"batchNorm\"),l;r!=null&&(l=_(r,\"scale\",\"batchNorm\"));let c;return s!=null&&(c=_(s,\"offset\",\"batchNorm\")),O(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),O(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Ku(i,o,u,c,l,a)}var ME=L({batchNorm4d_:zE});function LE(e,t,n){let s=_(e,\"x\",\"bincount\"),r=_(t,\"weights\",\"bincount\");O(s.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},i={size:n};return M.runKernel(cg,a,i)}var tI=L({bincount_:LE});function BE(e,t){let n=_(e,\"s0\",\"broadcastArgs\",\"int32\"),s=_(t,\"s1\",\"broadcastArgs\",\"int32\");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(dn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(ha(this.beta1,this.iterations_+1)),this.accBeta2.assign(ha(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};yb.className=\"Adam\";$r(yb);var vb=class extends Ar{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=_r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};vb.className=\"Adamax\";$r(vb);var Cp=class extends Ar{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Cp.className=\"SGD\";$r(Cp);var xb=class extends Cp{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=we(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulations[s]==null&&(this.accumulations[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(!1))});let a=this.accumulations[s].variable,i=Array.isArray(e)?e[s].tensor:e[n];i!=null&&j(()=>{let o,u=ie(V(this.m,a),i);this.useNesterov?o=ie(V(this.c,ie(i,V(u,this.m))),r):o=ie(V(this.c,u),r),a.assign(u),r.assign(o)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};xb.className=\"Momentum\";$r(xb);var wb=class extends Ar{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};wb.className=\"RMSProp\";$r(wb);var Ur=class{static sgd(e){return new Cp(e)}static momentum(e,t,n=!1){return new xb(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new wb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new yb(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new gb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new vb(e,t,n,s,r)}static adagrad(e,t=.1){return new bb(e,t)}},Mi={sgd:Ur.sgd,momentum:Ur.momentum,adadelta:Ur.adadelta,adagrad:Ur.adagrad,rmsprop:Ur.rmsprop,adamax:Ur.adamax,adam:Ur.adam},jO=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function LI(){return new Promise(e=>jO(()=>e()))}var S={};Ae(S,{ERF_A1:()=>rP,ERF_A2:()=>aP,ERF_A3:()=>iP,ERF_A4:()=>oP,ERF_A5:()=>uP,ERF_P:()=>sP,PARALLELIZE_THRESHOLD:()=>kb,SELU_SCALE:()=>VI,SELU_SCALEALPHA:()=>BI,applyActivation:()=>Ip,assertAndGetBroadcastShape:()=>ot,assertAxesAreInnerMostDims:()=>uD,assertParamsConsistent:()=>KO,assignToTypedArray:()=>fP,axesAreInnerMostDims:()=>Jg,calculateShapes:()=>Ok,checkEinsumDimSizes:()=>xP,checkPadOnDimRoundingMode:()=>hn,combineLocations:()=>dI,complexWithEvenIndex:()=>dP,complexWithOddIndex:()=>pP,computeConv2DInfo:()=>Ol,computeConv3DInfo:()=>Zk,computeDefaultPad:()=>Hg,computeDilation2DInfo:()=>gE,computeOptimalWindowSize:()=>YO,computeOutAndReduceShapes:()=>pI,computeOutShape:()=>XO,computePool2DInfo:()=>Qk,computePool3DInfo:()=>bE,convertConv2DDataFormat:()=>Jk,decodeEinsumEquation:()=>yP,eitherStridesOrDilationsAreOne:()=>Ps,expandShapeToKeepDim:()=>pa,exponent:()=>gP,exponents:()=>mP,fromStringArrayToUint8:()=>VP,fromUint8ToStringArray:()=>BP,getAxesPermutation:()=>hI,getBroadcastDims:()=>_k,getComplexWithIndex:()=>hP,getEinsumComputePath:()=>wP,getEinsumPermutation:()=>vP,getFusedBiasGradient:()=>kp,getFusedDyActivation:()=>wp,getImageCenter:()=>QO,getInnerMostAxes:()=>lD,getPermuted:()=>JO,getReductionAxes:()=>_t,getReshaped:()=>ZO,getReshapedPermuted:()=>eP,getSliceBeginCoords:()=>tP,getSliceSize:()=>nP,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>CP,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>NP,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>TP,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>AP,getSparseReshapeInputOutputMismatchErrorMessage:()=>RP,getSparseReshapeInputOutputMultipleErrorMessage:()=>EP,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>$P,getSparseReshapeNegativeOutputDimErrorMessage:()=>_P,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>PP,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>DP,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>FP,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>OP,getUndoAxesPermutation:()=>eb,isIdentityPermutation:()=>kP,log:()=>M$,mergeRealAndImagArrays:()=>lP,prepareAndValidate:()=>Dk,prepareSplitSize:()=>SP,segment_util:()=>WI,shouldFuse:()=>Sp,slice_util:()=>wt,splitRealAndImagArrays:()=>cP,tupleValuesAreOne:()=>mr,upcastType:()=>cn,validateInput:()=>Wg,validateUpdateShape:()=>Vg,warn:()=>ir});function KO(e,t){let n=e[0].length;e.forEach((r,a)=>{O(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),O(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let i=0;i<n;i++)O(i===t||r[i]===s[i],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function XO(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var kb=30;function YO(e){return e<=kb?e:hd(e,Math.floor(Math.sqrt(e)))}function QO(e,t,n){let s=n*(typeof e==\"number\"?e:e[0]),r=t*(typeof e==\"number\"?e:e[1]);return[s,r]}function ZO(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let i=0;i<a;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(a+1))}return r}function JO(e,t,n=!0){let s=[];if(n){s.push(t);for(let 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Error(\"Equations without an arrow are not supported.\");if(n>1)throw new Error(`Equation must contain exactly one arrow (\"${Vf}\").`);let[s,r]=e.split(Vf);O(s.indexOf(vx)===-1,()=>`The ellipsis notation (\"${vx}\") is not supported yet.`);let a=s.split(yx),i=a.length;if(t!==i)throw new Error(`Expected ${i} input tensors, received ${t}`);if(i>2)throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");let o=[];for(let d=0;d<r.length;++d){let h=r[d];if(!a.some(f=>f.indexOf(h)!==-1))throw new Error(`Output subscripts contain the label ${h} not present in the input subscripts.`);o.indexOf(h)===-1&&o.push(h)}for(let d=0;d<s.length;++d){let h=s[d];o.indexOf(h)===-1&&h!==yx&&o.push(h)}let u=new Array(a.length);for(let d=0;d<i;++d){if(new Set(a[d].split(\"\")).size!==a[d].length)throw new Error(`Found duplicate axes in input component ${a[d]}. 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e.every((t,n)=>t===n)}function IP(e,t){let n=[];for(let s=0;s<e.length;++s)(e[s].length===0||e[s].indexOf(t)!==-1||t===-1)&&n.push(s);return n}function SP(e,t,n=0){let s=[];if(typeof t==\"number\")O(e.shape[n]%t===0,()=>\"Number of splits must evenly divide the axis.\"),s=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);O(r<=1,()=>\"There should be only one negative value in split array.\");let a=t.indexOf(-1);if(a!==-1){let i=t.reduce((o,u)=>u>0?o+u:o);t[a]=e.shape[n]-i}O(e.shape[n]===t.reduce((i,o)=>i+o),()=>\"The sum of sizes must match the size of the axis dimension.\"),s=t}return s}function CP(e){return`Received SparseTensor with denseShape[0] = 0 but\n  indices.shape[0] = ${e}`}function NP(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function TP(e,t,n){return`indices(${e}, 0) is invalid: ${t} >= ${n}`}function $P(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function _P(e,t){return`size ${e} must be non-negative, 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Use \\`getOutputAt(nodeIndex)\\` instead.`);return bn(this.getNodeAtIndex(0,\"output\").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error(\"Cannot call the resetStates() method of a non-stateful Layer object.\")}assertInputCompatibility(e){if(e=dt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=dt(this.inputSpec);if(e.length!==t.length)throw new U(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");return ta(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return a}else{let a=gz(e),i=this.computeOutputShape(a),o,u=bz(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new hs(`The name \"${b}\" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split(\"/\");i=o.slice(0,-2).concat([o[o.length-1]]).join(\"/\")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Db(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${hS}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=Im(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Zr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Eu(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=ma(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ma(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!UP(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function NB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function fS(e,t){return NB(e,t,\"classWeight\")}async function mS(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=j(()=>{if(e.shape.length===1)return cr(e);if(e.shape.length===2){if(e.shape[1]>1)return qu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,\"float32\")}else return null}function TB(e,t){return V(e,t)}var $B=32;function gS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=zx(\"input\",e.inputNames,n),i=zx(\"output\",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function zx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function _B(e){if(e.length===3)throw new Fe(\"Validation with sample weights is not implemented yet.\");return{xs:e[0],ys:e[1]}}async function AB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>\"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).\"),w.assert(n!=null,()=>\"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.\"),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>\"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.\"),e.isTraining)throw new Error(\"Cannot start training because another fit() call is ongoing.\");e.isTraining=!0;try{let r=n.validationData!=null,a,i;if(r)if(Mx(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=_B(n.validationData);a=g.xs,i=g.ys}let o=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(g=>\"val_\"+g)):l=u.slice();let c=aS(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=iS(c,p,n.epochs,null,null,EB(t,n),null,r,l);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let b=0,y=0;for(s||(m=await t.iterator());!s||b<n.batchesPerEpoch;){let v=await m.next();if(s&&v.done){console.warn(`You provided \\`batchesPerEpoch\\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \\`batchesPerEpoch * epochs\\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Zr(a),o=Eu(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=St(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=St(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return OB(this,e,t,n)}async fitDataset(e,t){return AB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ds(n[0],e),ds(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=pm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-pm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss==\"string\")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!=\"string\")throw new Error(\"Serialization of non-string loss is not supported.\");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]==\"string\")e[s]=Ws(n[s]);else throw new Error(\"Serialization of non-string loss is not supported.\")}return e}getMetricIdentifiers(){if(typeof this.metrics==\"string\"||typeof this.metrics==\"function\")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error(\"Loading weight_metrics is not supported yet.\");if(e.loss_weights!=null)throw new Error(\"Loading loss_weights is not supported yet.\");if(e.sample_weight_mode!=null)throw new Error(\"Loading sample_weight_mode is not supported yet.\");let t=Ju(e.optimizer_config),n=ms(t),s;if(typeof e.loss==\"string\")s=Xr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Xr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Xr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Xr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Xr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e==\"string\"){let u=En.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");let n=await En.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:BB,generatedBy:`TensorFlow.js tfjs-layers v${hS}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u=\"optimizer\",{data:l,specs:c}=await En.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=En.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Px(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Px(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};hr.className=\"Model\";ae.registerClass(hr);var yS=class extends hr{};yS.className=\"Functional\";ae.registerClass(yS);async function VB(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Ju(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function WB(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return UB(e,void 0,t)}async function UB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Ju(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U(\"LayersModel artifacts contains weight data, but not weight specs. 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Add some layers first.\");this.model=new hr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hs(\"The model needs to be compiled before being used.\");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className===\"Merge\")throw new U(\"Legacy serialization format not supported yet.\");r=t}else w.assert(t.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Tm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return 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vr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},Hf(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},Hf(t)}else return e instanceof In?e:Hf(e)}function jb(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var RS=class extends ae.Serializable{},Gl=class extends RS{constructor(e){super(),jb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Vl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Gl.className=\"L1L2\";ae.registerClass(Gl);function qB(e){return jb(e),new Gl({l1:e!=null?e.l1:null,l2:0})}function jB(e){return jb(e),new Gl({l2:e!=null?e.l2:null,l1:0})}var Wx={l1l2:\"L1L2\"};function 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He{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Un(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Zb.className=\"ThresholdedReLU\";ae.registerClass(Zb);var Jb=class extends He{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new qb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Jb.className=\"Softmax\";ae.registerClass(Jb);function Qi(e,t,n){if(typeof e==\"number\")return ma(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!ez(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function gs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n===\"same\"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+br([n-t,0]);else if(s===\"same\")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function ey(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?Ge(e,[0,2,3,1]):e))}function DS(e,t){return j(()=>(Ct(t),t===\"channelsFirst\"?Ge(e,[0,2,3,4,1]):e))}function KB(e,t,n,s=1,r=\"valid\",a,i=1){return j(()=>{if(a==null&&(a=ys()),Ct(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=Ge(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let o=nI(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",i);return n!=null&&(o=ws(o,n)),o})}function Gx(e,t,n,s=[1,1],r=\"valid\",a,i,o=null){return j(()=>{if(a==null&&(a=ys()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=ey(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=fa.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:n,activation:o}),a===\"channelsFirst\"&&(u=Ge(u,[0,3,1,2])),u})}function XB(e,t,n,s=[1,1,1],r=\"valid\",a,i){return j(()=>{if(a==null&&(a=ys()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=DS(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return o=rI(o,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",i),n!=null&&(o=ws(o,n)),a===\"channelsFirst\"&&(o=Ge(o,[0,4,1,2,3])),o})}var ty=class extends He{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",ty.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Qi(t.kernelSize,e,\"kernelSize\"),this.strides=Qi(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Gn(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,Ct(this.dataFormat),this.activation=vr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Qi(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!Sb(e.kernelSize,\"number\",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:yr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hl=class extends ty{constructor(e,t){super(e,t),this.kernel=null,Hl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=qI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Gx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=KB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Gx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=XB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},FS=class extends Hl{constructor(e){super(2,e),FS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!Sb(e.kernelSize,\"number\",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Lp=FS;Lp.className=\"Conv2D\";ae.registerClass(Lp);var OS=class extends Hl{constructor(e){super(3,e),OS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Bp=OS;Bp.className=\"Conv3D\";ae.registerClass(Bp);var ny=class extends Lp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat===\"channelsFirst\"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ge(n,[0,2,3,1]));let g=sI(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=Ge(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ny.className=\"Conv2DTranspose\";ae.registerClass(ny);var sy=class extends Bp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat===\"channelsFirst\"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=Ge(n,[0,2,3,4,1]));let k=oR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=Ge(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};sy.className=\"Conv3DTranspose\";ae.registerClass(sy);var PS=class extends Hl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=Ge(e,[0,2,3,1])),n=m3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=Ge(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};PS.className=\"SeparableConv\";var ry=class extends PS{constructor(e){super(2,e)}};ry.className=\"SeparableConv2D\";ae.registerClass(ry);var zS=class extends Hl{constructor(e){super(1,e),zS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!Sb(e.kernelSize,\"number\",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ay=zS;ay.className=\"Conv1D\";ae.registerClass(ay);var iy=class extends He{constructor(e){super(e),typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Hc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Hc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Hc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Hc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};iy.className=\"Cropping2D\";ae.registerClass(iy);var oy=class extends He{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,QP(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=Ge(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation===\"nearest\"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a]);return Ge(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};oy.className=\"UpSampling2D\";ae.registerClass(oy);function YB(e,t,n=[1,1],s=\"valid\",r,a){return j(()=>{r==null&&(r=ys()),Ct(r);let i=ey(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=mp(i,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(i=Ge(i,[0,3,1,2])),i})}var uy=class extends ty{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=YB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};uy.className=\"DepthwiseConv2D\";ae.registerClass(uy);function MS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function LS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(bs(2,u));if(t=Ge(t,l),a!=null)throw new Fe(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),r!=null&&(r=ce(ce(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=Pn(r,-1)),r=Ge(r,l)),s&&(t=Jn(t,0),r!=null&&(r=Jn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Zn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=es(c,1)),[p,g,d]})}var BS=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new U(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Up({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return bs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){xm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");xm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=MS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let i={training:s},u=LS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Bl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?ym(t,[1,n]):t):this.cell.stateSize>1?[ym(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===BS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Er=BS;Er.className=\"RNN\";ae.registerClass(Er);var ql=class extends He{},Vp=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Vp.className=\"SimpleRNNCell\";ae.registerClass(Vp);var ly=class extends Er{constructor(e){e.cell=new Vp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};ly.className=\"SimpleRNN\";ae.registerClass(ly);var Wp=class extends ql{constructor(e){if(super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new U(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Wp.className=\"GRUCell\";ae.registerClass(Wp);var cy=class extends Er{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Wp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};cy.className=\"GRU\";ae.registerClass(cy);var jl=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new _p().apply([a]),c=r.apply([a*2]);return kx(kx(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};jl.className=\"LSTMCell\";ae.registerClass(jl);var dy=class extends Er{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new jl(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};dy.className=\"LSTM\";ae.registerClass(dy);var Up=class extends ql{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){xm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ta(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return wm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Db(t)}};Up.className=\"StackedRNNCells\";ae.registerClass(Up);function xr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):JI(t(),n),o=()=>Wl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var VS=class extends Er{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t===\"channelsFirst\",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};VS.className=\"ConvRNN2D\";var Gp=class extends jl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Qi(n,2,\"kernelSize\"),this.kernelSize.forEach(o=>Bt(o,\"kernelSize\")),this.strides=Qi(s||1,2,\"strides\"),this.strides.forEach(o=>Bt(o,\"strides\")),this.padding=r||\"valid\",Gn(this.padding),this.dataFormat=a||\"channelsLast\",Ct(this.dataFormat),this.dilationRate=Qi(i||1,2,\"dilationRate\"),this.dilationRate.forEach(o=>Bt(o,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return Cb([d,h,f])}},t.className=\"CustomInit\",t)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=da(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return da(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Gp.className=\"ConvLSTM2DCell\";ae.registerClass(Gp);var py=class extends VS{constructor(e){let t=new Gp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};py.className=\"ConvLSTM2D\";ae.registerClass(py);var Hp=class extends He{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wl(()=>JI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Hp.className=\"Dropout\";ae.registerClass(Hp);var hy=class extends Hp{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};hy.className=\"SpatialDropout1D\";ae.registerClass(hy);var fy=class extends He{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=vr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=qI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};fy.className=\"Dense\";ae.registerClass(fy);var my=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],pr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ge(n,s)}return sz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};my.className=\"Flatten\";ae.registerClass(my);var gy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.activation=vr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:yr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};gy.className=\"Activation\";ae.registerClass(gy);var by=class extends He{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),tz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};by.className=\"RepeatVector\";ae.registerClass(by);var yy=class extends He{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U(\"Can only specifiy one unknown dimension.\");else r*=u}let i=pr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};yy.className=\"Reshape\";ae.registerClass(yy);var vy=class extends He{constructor(e){if(super(e),e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=bs(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Ge(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};vy.className=\"Permute\";ae.registerClass(vy);var xy=class extends He{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return hm(Yu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=hm(Yu(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};xy.className=\"Masking\";ae.registerClass(xy);var wy=class extends He{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let 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Object.assign(e,t),e}};wy.className=\"Embedding\";ae.registerClass(wy);var xi=class extends He{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ty.className=\"Concatenate\";ae.registerClass(Ty);function Nu(e,t){for(;e<0;)e+=t;return e}function QB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let 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He{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Wl(()=>ie($p(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};_y.className=\"GaussianNoise\";ae.registerClass(_y);var Ay=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Wl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,$p(n.shape,1,r))},()=>n,t.training||!1):n})}};Ay.className=\"GaussianDropout\";ae.registerClass(Ay);var Ey=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=bs(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=ma(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,bs(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return el(s,b,y,v,x,this.epsilon)}else return el(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=eV(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ry.className=\"BatchNormalization\";ae.registerClass(Ry);var Dy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==dr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=sb(n,this.axis,!0),u=ma(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),el(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Dy.className=\"LayerNormalization\";ae.registerClass(Dy);function tV(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=ys()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n===\"channelsFirst\"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],bi(e,s)})}var Fy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ys():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding==\"number\")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]==\"number\")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>tV(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Fy.className=\"ZeroPadding2D\";ae.registerClass(Fy);function qp(e,t,n,s,r,a){return j(()=>{Ct(r),KI(a),Gn(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=ys()),a==null&&(a=\"max\"),e=ey(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=nb(e,t,n,o):i=qg(e,t,n,o),r===\"channelsFirst\"&&(i=Ge(i,[0,3,1,2])),i})}function WS(e,t,n,s,r,a){return j(()=>{Ct(r),KI(a),Gn(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=ys()),a==null&&(a=\"max\"),e=DS(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=mI(e,t,n,o):i=eI(e,t,n,o),r===\"channelsFirst\"&&(i=Ge(i,[0,4,1,2,3])),i})}var US=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Gn(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Bl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return gr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends US{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,\"max\")}};Oy.className=\"MaxPooling1D\";ae.registerClass(Oy);var Py=class extends US{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,\"avg\")}};Py.className=\"AveragePooling1D\";ae.registerClass(Py);var GS=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends GS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,\"max\")}};zy.className=\"MaxPooling2D\";ae.registerClass(zy);var My=class extends GS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,\"avg\")}};My.className=\"AveragePooling2D\";ae.registerClass(My);var HS=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ly=class extends HS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),WS(e,t,n,s,r,\"max\")}};Ly.className=\"MaxPooling3D\";ae.registerClass(Ly);var By=class extends HS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),WS(e,t,n,s,r,\"avg\")}};By.className=\"AveragePooling3D\";ae.registerClass(By);var qS=class extends He{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Vy=class extends qS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Vy.className=\"GlobalAveragePooling1D\";ae.registerClass(Vy);var Wy=class extends qS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};Wy.className=\"GlobalMaxPooling1D\";ae.registerClass(Wy);var jS=class extends He{constructor(e){super(e),this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Uy=class extends jS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?St(n,[1,2]):St(n,[2,3])})}};Uy.className=\"GlobalAveragePooling2D\";ae.registerClass(Uy);var Gy=class extends jS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?As(n,[1,2]):As(n,[2,3])})}};Gy.className=\"GlobalMaxPooling2D\";ae.registerClass(Gy);var KS=class extends He{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=ms(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Hy=class extends KS{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Oe(e),LS((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Hy.className=\"TimeDistributed\";ae.registerClass(Hy);function nV(e){yi(YP,\"BidirectionalMergeMode\",e)}var sV=\"concat\",qy=class extends KS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ms(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ms(s),this.forwardLayer.name=\"forward_\"+this.forwardLayer.name,this.backwardLayer.name=\"backward_\"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?sV:e.mergeMode,nV(this.mergeMode),e.weights)throw new Fe(\"weights support is not implemented for Bidirectional layer yet.\");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode===\"concat\"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=MS(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(s!=null)throw new Fe(\"Support for constants in Bidirectional layers is not implemented yet.\");let o=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==o)throw new U(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");if(o){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Jn(r,1));let i;return this.mergeMode===\"concat\"?i=Cb([s,r]):this.mergeMode===\"sum\"?i=ie(s,r):this.mergeMode===\"ave\"?i=V(.5,ie(s,r)):this.mergeMode===\"mul\"?i=V(s,r):this.mergeMode==null&&(i=[s,r]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){ta(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ta(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return 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dynamically resizeable`);let r=n===0?0:t.size/n,a=[];j(()=>{t=G(t,[1,n,r]);for(let o=0;o<e.length;++o){let u=o===0?0:s[o-1],l=[0,u,0],c=[1,e[o],r];a[o]=G(qe(t,l,c),this.elementShape)}return a});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,a)}},no=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);qn(t,r.shape,\"TensorList shape mismatch: \"),Ht(r)}),this.idTensor=we(0),this.maxNumElements=s,Ht(this.idTensor)}get id(){return this.idTensor.id}copy(){return new no([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements 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s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);qn(r,t,\"TensorList shape mismatch: \");let a=Fs(e);return new no(a,t,s)}function d4(e,t,n){return new no([],e,t,n)}function p4(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r}  vs. ${s})`);let a=new no([],n,e.dtype,s),i=Fs(e,0);return t.forEach((o,u)=>{a.setItem(o,i[u])}),a}function h4(e,t,n){let s=0,r=t.map(c=>(s+=c,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),i=Mm(a,n),o=s===0?0:e.size/s,u=j(()=>{let 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s=I(\"size\",e,t,n),r=I(\"dtype\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"dynamicSize\",e,t,n),o=I(\"clearAfterRead\",e,t,n),u=I(\"identicalElementShapes\",e,t,n),l=I(\"name\",e,t,n),c=new l4(l,r,s,a,u,i,o);return n.addTensorArray(c),[c.idTensor,we(1)]}case\"TensorArrayWriteV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case\"TensorArrayReadV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n);return[n.getTensorArray(s.id).read(r)]}case\"TensorArrayGatherV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"dtype\",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case\"TensorArrayScatterV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case\"TensorArrayConcatV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id),a=I(\"dtype\",e,t,n);return[r.concat(a)]}case\"TensorArraySplitV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"lengths\",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case\"TensorArraySizeV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return[we(r.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case\"TensorListSetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case\"TensorListGetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let s=I(\"indices\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"numElements\",e,t,n),o=p4(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let s=I(\"elementShape\",e,t,n),r=I(\"elementDType\",e,t,n),a;e.op===\"TensorListReserve\"?a=\"numElements\":a=\"maxNumElements\";let i=I(a,e,t,n),o=d4(s,r,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListGather\":{let s=I(\"tensorListId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case\"TensorListStack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=I(\"numElements\",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case\"TensorListFromTensor\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=c4(s,r,a);return n.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id),a=I(\"dtype\",e,t,n),i=I(\"elementShape\",e,t,n);return[r.concat(a,i)]}case\"TensorListPushBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"tensor\",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case\"TensorListPopBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"lengths\",e,t,n),i=h4(s,a,r);return n.addTensorList(i),[i.idTensor]}case\"TensorListLength\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),\"int32\")]}case\"TensorListResize\":{let s=I(\"tensorListId\",e,t,n),r=I(\"size\",e,t,n),i=n.getTensorList(s.id).resize(r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Yx(e,t,n){let[s,r]=I(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=!a,o=r===\"prelu\",u=s===\"fusedbatchnorm\",l=I(\"numArgs\",e,t,n);if(a){if(o&&l!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!o&&a&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",e,t,n),p=sd(e,t,n),d=I(\"dataFormat\",e,t,n).toUpperCase(),h=I(\"dilations\",e,t,n),[f,m]=I(\"args\",e,t,n);i&&(m=f,f=void 0);let g=I(\"leakyreluAlpha\",e,t,n);return{stride:c,pad:p,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var m4=(e,t,n)=>{switch(e.op){case\"Conv1D\":{let s=I(\"stride\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilation\",e,t,n);return[nI(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,r,a,i)]}case\"Conv2D\":{let s=I(\"strides\",e,t,n),r=sd(e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[da(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Yx(e,t,n);return[fa.conv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"FusedDepthwiseConv2dNative\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Yx(e,t,n);return[fa.depthwiseConv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let s=I(\"outputShape\",e,t,n),r=I(\"strides\",e,t,n),a=sd(e,t,n);return[sI(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,[r[1],r[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let s=I(\"strides\",e,t,n),r=sd(e,t,n),a=I(\"dilations\",e,t,n),i=I(\"dataFormat\",e,t,n).toUpperCase();return[mp(I(\"input\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,i,[a[1],a[2]])]}case\"Conv3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[rI(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2],s[3]],r,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[qg(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[nb(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPoolWithArgmax\":{let 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i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[St(I(\"x\",e,t,n),i,o)]}case\"Min\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[gm(I(\"x\",e,t,n),i,o)]}case\"Sum\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[ve(I(\"x\",e,t,n),i,o)]}case\"All\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[Yk(I(\"x\",e,t,n),i,o)]}case\"Any\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[hm(I(\"x\",e,t,n),i,o)]}case\"ArgMax\":{let i=I(\"axis\",e,t,n);return[qu(I(\"x\",e,t,n),i)]}case\"ArgMin\":{let i=I(\"axis\",e,t,n);return[aE(I(\"x\",e,t,n),i)]}case\"Prod\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[gI(I(\"x\",e,t,n),i,o)]}case\"Cumprod\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[mm(I(\"x\",e,t,n),i,o,u)]}case\"Cumsum\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[oI(I(\"x\",e,t,n),i,o,u)]}case\"Bincount\":let 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s=I(\"axis\",e,t,n);return[Pn(I(\"x\",e,t,n),s)]}case\"Squeeze\":{let s=I(\"axis\",e,t,n);return[gr(I(\"x\",e,t,n),s)]}case\"Reshape\":return[G(I(\"x\",e,t,n),I(\"shape\",e,t,n))];case\"MirrorPad\":return[CD(I(\"x\",e,t,n),I(\"padding\",e,t,n),I(\"mode\",e,t,n))];case\"PadV2\":case\"Pad\":return[bi(I(\"x\",e,t,n),I(\"padding\",e,t,n),I(\"constantValue\",e,t,n))];case\"SpaceToBatchND\":{let s=I(\"blockShape\",e,t,n),r=I(\"paddings\",e,t,n);return[rb(I(\"x\",e,t,n),s,r)]}case\"BatchToSpaceND\":{let s=I(\"blockShape\",e,t,n),r=I(\"crops\",e,t,n);return[jg(I(\"x\",e,t,n),s,r)]}case\"DepthToSpace\":{let s=I(\"blockSize\",e,t,n),r=I(\"dataFormat\",e,t,n).toUpperCase();return[mR(I(\"x\",e,t,n),s,r)]}case\"BroadcastTo\":return[nd(I(\"x\",e,t,n),I(\"shape\",e,t,n))];case\"BroadcastArgs\":return[VE(I(\"s0\",e,t,n),I(\"s1\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Qx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case\"arithmetic\":return j(()=>o4(a,i,o));case\"basic_math\":return j(()=>u4(a,i,o));case\"control\":return f4(a,i,o);case\"convolution\":return j(()=>m4(a,i,o));case\"creation\":return j(()=>g4(a,i,o));case\"dynamic\":return b4(a,i,o);case\"evaluation\":return j(()=>y4(a,i,o));case\"image\":return j(()=>k4(a,i,o));case\"graph\":return j(()=>v4(a,i,o));case\"logical\":return j(()=>I4(a,i,o));case\"matrices\":return j(()=>S4(a,i,o));case\"normalization\":return j(()=>C4(a,i,o));case\"reduction\":return j(()=>N4(a,i,o));case\"slice_join\":return j(()=>T4(a,i,o));case\"sparse\":return j(()=>$4(a,i,o));case\"spectral\":return j(()=>_4(a,i,o));case\"string\":return j(()=>A4(a,i,o));case\"transformation\":return j(()=>E4(a,i,o));case\"hash_table\":return w4(a,i,o,s);case\"custom\":let u=e0(a.op);if(u&&u.customExecutor)return u.customExecutor(new i4(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Zx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(\"\"),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Jx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((k0(d)||P4(d)||z4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function R4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var D4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],F4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],O4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function k0(e){return D4.indexOf(e.op)>=0}function P4(e){return F4.indexOf(e.op)>=0}function z4(e){return O4.indexOf(e.op)>=0}var Lm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Lm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=Jx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return R4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[An(c)[0]]),r=t.map(c=>An(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Zx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=An(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=Qx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>un(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!==\"control\"){let u=LW(o.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=i[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete i[l.id]}else c!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new Zx(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(l=>un(l,this.tensorsMap,a)),o=i.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[An(y)[0]]),i=n.map(y=>An(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=Jx(e,o,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=An(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,i,f,u);await Promise.all(y)}c==null&&!s&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=o.filter(y=>!k0(y)&&!un(y.name,h,t)).map(y=>y.name);if(b.length>0){let y=\"\";throw c!=null&&(y=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,s,r,a,i,o,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p=\"\";if(c.node.op===\"Enter\"&&I(\"isConstant\",c.node,s,n)&&([p]=Ts(c.node.name,n)),s[c.node.name]==null){let d=Qx(c.node,s,n,this._resourceManager);p||([p]=Ts(c.node.name,n));let h=n.currentContext;w.isPromise(d)?l.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u))}else this.processChildNodes(c.node,t,n,s,r,u)}return l}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Ts(i.name,n);r[o]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!un(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=An(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=An(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=An(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},M4=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},L4=\"?tfjs-format=file\",B4=\"model.json\",V4=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version=\"n/a\",t==null&&(this.loadOptions={}),this.resourceManager=new M4}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=En.browserHTTPRequest(e,this.loadOptions);else{let t=En.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(En.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=En.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Lm(jx.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=jx.Instance.transformGraph(e.modelInitializer);this.initializer=new Lm(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e==\"string\"){let n=En.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function jpe(e,t={}){if(e==null)throw new Error(\"modelUrl in loadGraphModel() cannot be null. 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t.done?(this.iterator=null,this.readFromChain(e)):t}},E0=(e=>(e[e.FAIL=0]=\"FAIL\",e[e.SHORTEST=1]=\"SHORTEST\",e[e.LONGEST=2]=\"LONGEST\",e))(E0||{}),lU=class extends Ut{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return\"{TODO: fill in upstream of zip summaries} -> Zip\"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await C0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. 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Kc:a=Xc;break;default:}break;default:}if(a===jf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},O0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new O0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},P0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Yi([a,r,o,i],[1,4])}else this.cropBox=Yi([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(!X().get(\"IS_BROWSER\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new P0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. 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OG={kernelName:Na,backendName:\"cpu\",kernelFunc:FC};function PG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=FC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=Kl({inputs:{a:d,b:i},backend:n}),m.push(d),d=h),c&&(f=lv(n,d,c,o,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var zG={kernelName:aa,backendName:\"cpu\",kernelFunc:PG},MG=st(rl,e=>Math.acos(e)),LG={kernelName:rl,backendName:\"cpu\",kernelFunc:MG},BG=st(al,e=>Math.acosh(e)),VG={kernelName:al,backendName:\"cpu\",kernelFunc:BG};function WG(e){let{inputs:t,backend:n}=e,s=t;be(t,\"addN\");let r=s.map(o=>n.data.get(o.dataId).values),a=De(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var UG={kernelName:Ia,backendName:\"cpu\",kernelFunc:WG};function 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KH={kernelName:ho,backendName:\"cpu\",kernelFunc:jH};function XH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumprod\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var YH={kernelName:po,backendName:\"cpu\",kernelFunc:XH};function QH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,\"cumsum\");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var ZH={kernelName:Da,backendName:\"cpu\",kernelFunc:QH};function JH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=nv(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=j0(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var e6={kernelName:mg,backendName:\"cpu\",kernelFunc:JH};function t6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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r6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],\"depthwiseConv2dNativeBackpropFilter\");let p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,\"float32\"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),T=n.data.get(a.dataId).values,N=new Vt(a.shape,a.dtype,T);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*N.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var a6={kernelName:gg,backendName:\"cpu\",kernelFunc:r6};function i6(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],\"depthwiseConv2DNativeBackpropInput\");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,\"float32\"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,T]=p,N=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let ke=me-se,Se=Math.max(0,Math.ceil(ke/te)),Ee=Math.min(Y,(F+ke)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-ke;for(let at=Je;at<Ye;++at){let Jt=at*ee-Xe,Nt=x*re+k*Ce+T*at,Cn=E*(F-1-ut)+A*($-1-Jt)+P*le;for(let Et=0;Et<oe;++Et){let en=le*oe+Et,Nn=v[Nt+en],Tn=N[Cn+Et];tt+=Nn*Tn}}}m[g*re+b*me+y*Pe+le]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var o6={kernelName:bg,backendName:\"cpu\",kernelFunc:i6};function u6(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=De([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var l6={kernelName:yg,backendName:\"cpu\",kernelFunc:u6},c6={kernelName:Zd,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:i,dilations:o}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:T,filterWidth:N,dilationHeight:E,dilationWidth:A,outShape:P}=S.computeDilation2DInfo(s.shape,r.shape,a,i,\"NHWC\",o),R=w.sizeFromShape(P),F=P.length,$=w.getArrayFromDType(s.dtype,R);for(let W=0;W<h;++W)for(let q=0;q<b;++q){let K=q*x-v.top;for(let Y=0;Y<y;++Y){let Z=Y*k-v.left;for(let te=0;te<g;++te){let ee=Number.MIN_SAFE_INTEGER;for(let ne=0;ne<T;++ne){let oe=K+ne*E;if(oe>=0&&oe<f)for(let re=0;re<N;++re){let le=Z+re*A;if(le>=0&&le<m){let me=w.locToIndex([W,oe,le,te],c,w.computeStrides(s.shape)),ke=w.locToIndex([ne,re,te],d,w.computeStrides(r.shape)),Se=l[me]+p[ke];Se>ee&&(ee=Se)}}}let 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e=Ne.getNumber(\"WEBGL_VERSION\");return e===0?0:p1(e)});Ne.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",()=>Ne.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!hp.isMobile());Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\",()=>h1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\",()=>Ne.getBool(\"WEBGL_FORCE_F16_TEXTURES\")?!1:Ne.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\"));Ne.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",()=>f1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_FENCE_API_ENABLED\",()=>m1(Ne.getNumber(\"WEBGL_VERSION\")));Ne.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\",()=>Ne.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0);Ne.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Ne.registerFlag(\"WEBGL_FLUSH_THRESHOLD\",()=>hp.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Ne.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\",()=>128);Ne.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\",()=>!1);Ne.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e5);Ne.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\",()=>128);function fn(){let e,t,n,s,r,a,i,o,u,l;return X().getNumber(\"WEBGL_VERSION\")===2?(e=\"#version 300 es\",t=\"in\",n=\"out\",s=\"in\",r=\"texture\",a=\"outputColor\",i=\"out vec4 outputColor;\",o=`\n      bool isnan_custom(float val) {\n        uint floatToUint = floatBitsToUint(val);\n        return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `,u=\"\",l=`\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `):(e=\"\",t=\"attribute\",n=\"varying\",s=\"varying\",r=\"texture2D\",a=\"gl_FragColor\",i=\"\",o=`\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `,u=`\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `,l=`\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function wi(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join(\"\")}function Kp(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join(\"\")}function X5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Y5(e,t,n=\"index\"){let s=e.map((a,i)=>i),r=X5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join(\"\")}function mv(e){let t=w.computeStrides(e).map(n=>n.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n  }\n`}function gv(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var g1=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:b1}=S;function Q5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=bv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 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n=e.shapeInfo.logicalShape;switch(n.length){case 0:return xK(e,t);case 1:return kK(e,t);case 2:return SK(e,t);case 3:return NK(e,t);case 4:return $K(e,t);case 5:return _K(e);case 6:return AK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function y1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return vK(e);case 1:return wK(e,t);case 2:return IK(e,t);case 3:return CK(e,t);default:return TK(e,t)}}function Z5(e,t,n=!1,s){let r=\"\";n?r+=y1(e,s):r+=ru(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=EK(e,t):r+=RK(e,t)),r}function J5(e,t,n){switch(e.length){case 0:return v1();case 1:return lK(e,t,n);case 2:return bK(e,t,n);case 3:return dK(e,t,n);default:return hK(e,t,n)}}function eK(e,t,n){switch(e.length){case 0:return v1();case 1:return cK(e,t,n);case 2:return yK(e,t,n);case 3:return pK(e,t,n);case 4:return fK(e,t,n);case 5:return mK(e,t);case 6:return gK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function tK(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function nK(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function sK(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function rK(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${aK}\n    ${iK}\n    ${oK}\n  `}var aK=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,iK=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,oK=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,uK=`\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;function v1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function lK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function cK(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function dK(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function pK(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${Kp([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=wi([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function hK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`\n      int b${l} = index / ${i};\n      index -= b${l} * ${i};\n    `+o,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function fK(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${Kp([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=wi([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function mK(e,t){let n=wi([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function gK(e,t){let n=wi([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function bK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function yK(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function ki(e){return`offset${e}`}function vK(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function xK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=ki(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${u}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function wK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function kK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${au(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=ki(n);return i===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function IK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function SK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=iu(e,u),h=[\"row\",\"col\"];return`\n      ${ru(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${ou(h,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${au(e)}\n      }\n    `;let l=a[0],c=a[1],p=ki(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function CK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=iu(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${y1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${ou(m,h)});\n        }\n      `}let o=fn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}function NK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=iu(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${ru(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${ou(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${au(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===i&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=ki(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${i} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function TK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function $K(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=iu(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${ru(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${ou(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${au(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=ki(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function _K(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=iu(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${ru(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${ou(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${au(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=ki(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function AK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=iu(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${ru(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${ou(b,a)});\n      }\n    `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${au(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=ki(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function au(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function EK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=b1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(i),l=i-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":i<2&&o.length>=1?c=\"coords = 0;\":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";i<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)i===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":o.indexOf(y)>-1?h=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":o.indexOf(v)>-1&&(h=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${r}() {\n      ${u} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${s}(${d});\n      ${h}\n    }\n  `}function RK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`\n      float ${r}() {\n        return sampleTexture(${n}, resultUV);\n      }\n    `;let l=rt(u),c=b1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];o===0?d=\"\":u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`\n`);let f=\"\";return u<2&&o>0?f=\"coords\":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(\", \"),`\n    float ${r}() {\n      ${l} coords = getOutputCoords();\n      ${d}\n      return get${s}(${f});\n    }\n  `}function rt(e){if(e<=1)return\"int\";if(e===2)return\"ivec2\";if(e===3)return\"ivec3\";if(e===4)return\"ivec4\";if(e===5)return\"ivec5\";if(e===6)return\"ivec6\";throw Error(`GPU for rank ${e} is not yet supported`)}function bv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function iu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ou(e,t){return t.map(n=>e[n]).join(\", \")}function DK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=Q5(r,i,t),u=XC(e.gl,o),l=e.createProgram(u);return X().get(\"ENGINE_COMPILE_ONLY\")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...x1(e,t,l)}}function x1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,\"NAN\",!1),X().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(n,\"INFINITY\",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,\"outShape\",d),l=e.getUniformLocation(n,\"outShapeStrides\",d),u=e.getUniformLocation(n,\"outTexShape\",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function aw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function FK(e,t,n,s,r){t.program.enableShapeUniforms||(aw(t.inShapeInfos,n),aw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=bv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new 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4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function OK(e,t,n){let s=\"\";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=bv(e.packedInputs,i.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${X().getNumber(\"WEBGL_VERSION\")}`,a}function Sn(e){return X().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var PK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Kp([\"r\",\"c\",\"d\"],e):wi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}},zK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Kp([\"r\",\"c\",\"d\"],e):wi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}},MK=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${g1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},LK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`\n      ${g1}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}},BK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?gv():mv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},VK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. 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_X=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=ln(\"rc\",this.rank),n=rt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},V1=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${AX(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?gv():mv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function AX(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?Y5([\"r\",\"c\",\"d\"],\"inputShape\"):wi([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var EX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=ow(t,n),r=uw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=iw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=ow(n,s),a=uw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=iw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function RX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function iw(e,t,n,s,r){let a=DX(t,s),i;if(r){let[u,l]=nu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=RX(n,a);return i*o}function DX(e,t){switch(e){case 3:return wv(t);case 4:return kv(t);case 1:return yv(t);case 0:return vv(t);case 2:return xv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function FX(e){return X().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function ow(e,t){if(e===1)return 3;if(e===0||e==null)return FX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function uw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n        ${t}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},ss=\"if (isnan(x)) return x;\",OX=\"return x;\",lw=\"return abs(x);\",PX=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",zX=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,MX=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Bi=\"return x;\",LX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",BX=\"return x;\",VX=`\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`,WX=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,UX=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,GX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Jr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},HX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln(\"rc\",t),s=rt(t),r=$X(t,n),a=n.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},qX=xs.whereImpl,jX=1e-7,KX=1e-4,Zc={};function XX(e){return e in Zc||(Zc[e]={}),Zc[e]}var YX=X().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),QX=600;function ZX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*QX/1024/1024}var W1=class extends sl{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof Xf)t=e;else{let n=vs(X().getNumber(\"WEBGL_VERSION\"),e);t=new Xf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber(\"WEBGL_VERSION\"));t=new Xf(n),this.binaryCache=XX(X().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new EX(this.gpgpu),this.numMBBeforeWarning=ZX(),this.texData=new Hd(this,Ss())}nextDataId(){return W1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||X().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Jr(i,Bi):p=new Hs(i,Bi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Jr(s,Bi):h=new Hs(s,Bi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool(\"DEBUG\")&&!X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&X().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&X().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;o?d=new Jr(r,Bi):d=new Hs(r,Bi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!qC(n))throw X().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool(\"WEBGL_PACK\")&&s===!0,i=a?id(t):t,o=a?new LK(i):new MK(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=YX){return X().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return qX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Jr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=M1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,lw,e.dtype);let t=new Hs(e.shape,lw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new HX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new _X(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ya(e.shape),...va(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ya(t),...va(t)],a=new V1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=id(r),o;s?o=new zK(i):o=new PK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!tl(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=OK(e,l,c),d=this.getAndSaveBinary(p,()=>DK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get(\"ENGINE_COMPILE_ONLY\")||FK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool(\"WEBGL_LAZILY_UNPACK\")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=X().getBool(\"DEBUG\");X().set(\"DEBUG\",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?jX:KX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=l1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=nu(c[0],c[1])),o?d=new VK(p,m):d=new BK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=JX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await LI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(fv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=x1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},U1=W1;U1.nextDataId=0;function JX(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ype=\"0.0.0\";function e8(){X().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}hp.isBrowser()&&fp(\"webgl\",()=>new U1,2);var Qpe={forceHalfFloat:e8},G1=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,io=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}},Xp=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,Zl=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Sn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${rt(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let o=ln(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function kn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var t8={kernelName:Wa,backendName:\"webgl\",kernelFunc:kn};function Dr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.texData.get(a.dataId),o=kn({inputs:{x:s},backend:n}),u=kn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var n8={kernelName:Xd,backendName:\"webgl\",kernelFunc:Dr},H1=\"return (a < 0.) ? b * a : a;\",q1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function s8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),o=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(q1,r.shape,i.shape):new io(H1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],\"float32\");return n.disposeIntermediateTensorInfo(i),u}var r8={kernelName:Ua,backendName:\"webgl\",kernelFunc:s8},j1=\"return (a < 0.) ? b * a : a;\",K1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function a8(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(K1,s.shape,r.shape):new io(j1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var i8={kernelName:ti,backendName:\"webgl\",kernelFunc:a8},uu=\"if (isnan(x)) return x;\",o8=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,u8=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Jr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new io(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Dr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?S.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new io(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Yp(e,t=!1){if(e===\"linear\")return t?BX:OX;if(e===\"relu\")return t?WX:zX;if(e===\"elu\")return t?VX:PX;if(e===\"relu6\")return t?UX:MX;if(e===\"prelu\")return t?K1:j1;if(e===\"leakyrelu\")return t?q1:H1;if(e===\"sigmoid\")return t?GX:LX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var X1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";i&&(o?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:m=`vec4 activation(vec4 x) {\n          ${i}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},cw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},dw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},pw=\"return a * b;\";function Sv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new dw(cw.REAL,s.shape,r.shape),c=new dw(cw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Dr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=lX(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?i=new Zl(pw,s.shape,r.shape):i=new io(pw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var l8={kernelName:Za,backendName:\"webgl\",kernelFunc:Sv};function c8(e,t,n){let s=[ya(e.shape),...va(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ya(t),...va(t)],i=new V1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!tl(r.shape,u)&&!(c.texture!==null&&tl(c.shape,u))?c8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var d8={kernelName:Ro,backendName:\"webgl\",kernelFunc:he},hw=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l=\"\";r%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${o===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${o===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${o===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},p8=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=\"0.0\",o=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",o=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",o=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(i=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function h8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=S.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function Ii(e,t,n,s){let r=h8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n===\"mean\"?c=i===0?new hw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new hw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new p8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var f8=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=m8(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function m8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var g8=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=B1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function Qp(e,t,n){let s=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new g8(e.shape,t):new f8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function b8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Qp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims(\"sum\",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=pp(e.dtype),v=Ii(b,y,\"sum\",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Zp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return b8(r,a,i,n)}var y8={kernelName:ci,backendName:\"webgl\",kernelFunc:Zp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=Iv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Qp(r,a,i);return l}var v8={kernelName:mi,backendName:\"webgl\",kernelFunc:qt},Y1=1e3;function Ld({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ko.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=n?N.shape[1]:N.shape[2],F=a!=null,$=i!=null,z=u===\"leakyrelu\",W=u!=null?Yp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>Y1&&q===!1){let Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=Sv({inputs:{a:ne,b:re},backend:r});K=Zp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new X1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function x8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Ld({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var w8={kernelName:aa,backendName:\"webgl\",kernelFunc:x8},fw=\"return abs(x);\";function k8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!==\"complex64\"){let a=n.texData.get(s.dataId),i=M1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?r=new Jr(s.shape,fw):r=new Hs(s.shape,fw),n.runWebGLProgram(r,[s],s.dtype)}var I8={kernelName:uo,backendName:\"webgl\",kernelFunc:k8},S8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`,C8=Ke({opSnippet:S8}),N8={kernelName:rl,backendName:\"webgl\",kernelFunc:C8},T8=ss+`\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,$8=Ke({opSnippet:T8}),_8={kernelName:al,backendName:\"webgl\",kernelFunc:$8},mw=\"return a + 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ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return kn({inputs:{x:s[0]},backend:n});if(s.length>X().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),o=X().getBool(\"WEBGL_PACK\")?new D8(s[0].shape,a):new R8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var F8={kernelName:Ia,backendName:\"webgl\",kernelFunc:ld};function O8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims(\"all\",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ii(m,m.dtype,\"all\",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var P8={kernelName:il,backendName:\"webgl\",kernelFunc:O8};function z8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims(\"any\",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ii(m,m.dtype,\"any\",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var M8={kernelName:ol,backendName:\"webgl\",kernelFunc:z8},L8=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let i=t===\"max\"?\">\":\"<\",o=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}},B8=class{constructor(e,t,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,o=i.length,u=rt(o),l=ln(\"coords\",o),c,p;if(a===1){p=o+1;let N=rt(p);c=`\n        ${N} sourceLocR = ${N}(${l.join()}, 0);\n        ++${l[o-1]};\n        ${N} sourceLocG = ${N}(${l.join()}, 0);\n        ++${l[o-2]};\n        ${N} sourceLocA = ${N}(${l.join()}, 0);\n        --${l[o-1]};\n        ${N} sourceLocB = ${N}(${l.join()}, 0);\n        --${l[o-2]};`}else p=o,c=`\n        ${u} sourceLocR = coords;\n        ++${l[o-1]};\n        ${u} sourceLocG = coords;\n        ++${l[o-2]};\n        ${u} sourceLocA = coords;\n        --${l[o-1]};\n        ${u} sourceLocB = coords;\n        --${l[o-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map(N=>\"int \"+N),m=ln(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=ln(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=ln(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=ln(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,T=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${T}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function Q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new L8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=Q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function Z1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new B8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,\"int32\");if(l.shape.length===t.shape.length){let c=Z1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function J1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=Q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return Z1(e,t,s)}function V8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=J1(n,u,i[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var W8={kernelName:Sa,backendName:\"webgl\",kernelFunc:V8};function U8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=J1(n,u,i[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var G8={kernelName:ul,backendName:\"webgl\",kernelFunc:U8},H8=ss+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,q8=Ke({opSnippet:H8}),j8={kernelName:ll,backendName:\"webgl\",kernelFunc:q8},K8=ss+\"return log(x + sqrt(x * x + 1.0));\",X8=Ke({opSnippet:K8}),Y8={kernelName:cl,backendName:\"webgl\",kernelFunc:X8},Q8=ss+`\n  return atan(x);\n`,Z8=Ke({opSnippet:Q8}),J8={kernelName:dl,backendName:\"webgl\",kernelFunc:Z8},eY=o8+`\n  return atan(a, b);\n`,tY=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+u8+`\n  return result;\n`,nY=jt({opSnippet:eY,packedOpSnippet:tY}),sY={kernelName:hl,backendName:\"webgl\",kernelFunc:nY},rY=ss+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,aY=Ke({opSnippet:rY}),iY={kernelName:pl,backendName:\"webgl\",kernelFunc:aY},nl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let N=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${N} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,T=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${T}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${T}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${T}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},Cv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let A=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${A} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let T=Math.floor(a/4)*4,N=a%4,E=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${T}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${E}\n            }\n\n            int xC = xCCorner + ${T};\n            if (${N===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${N===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${E}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;su(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new nl(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var uY={kernelName:Ca,backendName:\"webgl\",kernelFunc:oY};function lY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Cv(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var cY={kernelName:Kd,backendName:\"webgl\",kernelFunc:lY},dY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},pY=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function hY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new pY(d);return n.runWebGLProgram(h,[r],i.dtype)}var fY={kernelName:lg,backendName:\"webgl\",kernelFunc:hY};function mY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;su([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new dY(c);return n.runWebGLProgram(p,[r],i.dtype)}var gY={kernelName:ug,backendName:\"webgl\",kernelFunc:mY};function bY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return 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o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=lu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},_Y={kernelName:lo,backendName:\"webgl\",kernelFunc:$Y};function AY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=z1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var EY={kernelName:cg,backendName:\"webgl\",kernelFunc:AY};function RY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var DY={kernelName:dg,backendName:\"webgl\",kernelFunc:RY},FY=\"return float(a != b);\",e2=jt({opSnippet:FY,cpuKernelImpl:dX,dtype:\"bool\"}),OY={kernelName:No,backendName:\"webgl\",kernelFunc:e2};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.real},backend:n})}var PY={kernelName:rp,backendName:\"webgl\",kernelFunc:Jl},zY=\"return float(int(x));\";function MY(e,t){let n=new Hs(e.shape,zY),s=t.runWebGLProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function jm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return kn({inputs:{x:r},backend:n});let i=$t(r.shape),o=jm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=Dr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype===\"complex64\"){let i=Jl({inputs:{input:r},backend:n}),o=jm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=kn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return MY(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=e2({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var LY={kernelName:Ta,backendName:\"webgl\",kernelFunc:jm},gw=\"return ceil(x);\",BY=Ke({opSnippet:gw,packedOpSnippet:gw,cpuKernelImpl:qK}),VY={kernelName:$a,backendName:\"webgl\",kernelFunc:BY},WY=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `}},UY=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `}};function GY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool(\"WEBGL_PACK_CLIP\")?o=new UY(r.shape):o=new WY(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var HY={kernelName:Cr,backendName:\"webgl\",kernelFunc:GY},qY=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function bw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function jY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new qY(s.shape),i=[bw(s,r.complexTensorInfos.real),bw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var KY={kernelName:Yd,backendName:\"webgl\",kernelFunc:jY},XY=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},YY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln(\"coords\",s),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`\n        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {\n          return getChannel(\n            getT${f}(${Jc(i,u,m)}),\n            vec2(${Jc(l,u,m)}));\n        }`}let d=o.length,h=o[o.length-1];p+=`\n        return getChannel(\n          getT${d}(${Jc(i,u,h)}),\n          vec2(${Jc(l,u,h)}));`,this.userCode=`\n      float getValue(${i.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 1;\n        if (${a[s-2]} < ${n[s-2]} &&\n            ${a[s-1]} < ${n[s-1]}) {\n          result.b = getValue(${a});\n        }\n        setOutput(result);\n      }\n    `}};function Jc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Jp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var QY={kernelName:ep,backendName:\"webgl\",kernelFunc:Jp};function qi(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let c=e.map(m=>Jl({inputs:{input:m},backend:n})),p=e.map(m=>Jp({inputs:{input:m},backend:n})),d=qi(c,t,n),h=qi(p,t,n),f=Dr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=S.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=jK(p,d,s,h),m=S.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let c=Math.floor(e.length/2),p=qi(e.slice(0,c),t,n),d=qi(e.slice(c),t,n),h=qi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&e[0].shape.length>1){let c=new YY(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=ZY(e,t,n),o=new XY(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function ZY(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function t2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return kn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),qi(o,a,n)}var JY={kernelName:co,backendName:\"webgl\",kernelFunc:t2},n2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat===\"channelsLast\",g=m?1:2,b=m?2:3,y=m?3:1,v=\"\",x=\"\";n&&(s?v=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?v=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:v=`\n          float activation(float x) {\n            ${n}\n          }\n        `,x=\"result = activation(result);\");let k=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${v}\n\n      const ivec2 strides = ivec2(${o}, ${u});\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${y}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${b}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},e9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},t9=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"},{name:\"pad\",type:\"ivec2\"},{name:\"stride\",type:\"ivec2\"},{name:\"dilation\",type:\"ivec2\"},{name:\"inChannels\",type:\"int\"},{name:\"itemsPerBlockRow\",type:\"int\"},{name:\"outWidth\",type:\"int\"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n===\"channelsLast\",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?\"if(blockIndex < outShape[1] && pos < outShape[0]) {\":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.y + ${c};\n          pos = rc.x + ${l};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${s.output} = result;\n      }\n    `}};function s2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>Y1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(tl(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=Ld({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Ld({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function r2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new t9(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],\"float32\",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o===\"leakyrelu\",z=o?Yp(o,!0):null,W=new X1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,\"float32\"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=s2({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)h=r2({x:r,filter:a,convInfo:d,backend:n});else{let m=new n2(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var s9={kernelName:_a,backendName:\"webgl\",kernelFunc:n9},r9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},a9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},i9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},o9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function u9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new r9(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var l9={kernelName:pg,backendName:\"webgl\",kernelFunc:u9};function c9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new a9(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var d9={kernelName:Aa,backendName:\"webgl\",kernelFunc:c9};function p9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new e9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var h9={kernelName:Qd,backendName:\"webgl\",kernelFunc:p9};function f9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new i9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var m9={kernelName:hg,backendName:\"webgl\",kernelFunc:f9};function g9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new o9(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var b9={kernelName:fg,backendName:\"webgl\",kernelFunc:g9},y9=uu+`\n  return cos(x);\n`,v9=Ke({opSnippet:y9}),x9={kernelName:Ea,backendName:\"webgl\",kernelFunc:v9},w9=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,k9=Ke({opSnippet:w9}),I9={kernelName:Ra,backendName:\"webgl\",kernelFunc:k9},S9=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},C9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new S9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],\"float32\")},N9={kernelName:ho,backendName:\"webgl\",kernelFunc:C9},yw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"1.0\":`getX(${vw(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${xw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${xw(s,\"coords\")} = idx;\n          val *= getX(${vw(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function xw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function T9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new yw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new yw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var $9={kernelName:po,backendName:\"webgl\",kernelFunc:T9},ww=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"0.0\":`getX(${kw(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${rt(s)} coords = getOutputCoords();\n        int end = ${Iw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${Iw(s,\"coords\")} = idx;\n          val += getX(${kw(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Iw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function _9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new ww(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new ww(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var A9={kernelName:Da,backendName:\"webgl\",kernelFunc:_9};function E9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=z1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=HK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var R9={kernelName:mg,backendName:\"webgl\",kernelFunc:E9},D9=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function F9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=new D9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var O9={kernelName:fo,backendName:\"webgl\",kernelFunc:F9},a2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},i2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,o===1){if(b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function P9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new i2(p):d=new a2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var z9={kernelName:Fa,backendName:\"webgl\",kernelFunc:P9},M9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},L9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new M9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var V9={kernelName:gg,backendName:\"webgl\",kernelFunc:B9};function W9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new L9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var U9={kernelName:bg,backendName:\"webgl\",kernelFunc:W9},G9=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function H9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new G9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var q9={kernelName:yg,backendName:\"webgl\",kernelFunc:H9},j9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function K9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeDilation2DInfo(r.shape,a.shape,i,o,\"NHWC\",u),c,p=new j9(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var X9={kernelName:Zd,backendName:\"webgl\",kernelFunc:K9};function Y9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=qt({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=Sv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Zp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of 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n.disposeData(d.dataId),f}function OQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))b=s2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)b=r2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,T=h===\"leakyrelu\",N=h?Yp(h,!1):null,E=new n2(g,x,N,k,T),A=[r,a];if(i&&A.push(i),o&&A.push(o),T){let 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    ${r} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${a};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `}};function BQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=ZK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new LQ(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var VQ={kernelName:xo,backendName:\"webgl\",kernelFunc:BQ},WQ=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=UQ(e,2);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${s}));\n      }\n    `}};function UQ(e,t){let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(\"index\"):s.push(`${n[r]}`);return s.join()}function l2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get(\"DEBUG\")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype===\"string\"){let y=n.bufferSync(h),v=n.bufferSync(d),x=JK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new WQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var GQ={kernelName:vo,backendName:\"webgl\",kernelFunc:l2},HQ=\"return float(a > b);\",qQ=`\n  return vec4(greaterThan(a, b));\n`,jQ=jt({opSnippet:HQ,packedOpSnippet:qQ,cpuKernelImpl:eX,dtype:\"bool\"}),KQ={kernelName:wo,backendName:\"webgl\",kernelFunc:jQ},XQ=\"return float(a >= b);\",YQ=`\n  return vec4(greaterThanEqual(a, b));\n`,QQ=jt({opSnippet:XQ,packedOpSnippet:YQ,dtype:\"bool\",cpuKernelImpl:tX}),ZQ={kernelName:Va,backendName:\"webgl\",kernelFunc:QQ};function JQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return u2(s,!0,n)}var eZ={kernelName:wg,backendName:\"webgl\",kernelFunc:JQ},tZ=\"return float(!isnan(x) && !isinf(x));\",nZ=Ke({opSnippet:tZ,dtype:\"bool\"}),sZ={kernelName:gl,backendName:\"webgl\",kernelFunc:nZ},rZ=\"return float(isinf(x));\",aZ=Ke({opSnippet:rZ,dtype:\"bool\"}),iZ={kernelName:bl,backendName:\"webgl\",kernelFunc:aZ},oZ=\"return float(isnan(x));\",uZ=Ke({opSnippet:oZ,dtype:\"bool\"}),lZ={kernelName:yl,backendName:\"webgl\",kernelFunc:uZ},cZ=\"return float(a < b);\",dZ=`\n  return vec4(lessThan(a, b));\n`,pZ=jt({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:nX,dtype:\"bool\"}),hZ={kernelName:ko,backendName:\"webgl\",kernelFunc:pZ},fZ=\"return float(a <= b);\",mZ=`\n  return vec4(lessThanEqual(a, b));\n`,gZ=jt({opSnippet:fZ,packedOpSnippet:mZ,cpuKernelImpl:sX,dtype:\"bool\"}),bZ={kernelName:Io,backendName:\"webgl\",kernelFunc:gZ};function yZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=rX(s,r,a);return t.makeTensorInfo([i.length],\"float32\",i)}var vZ={kernelName:kg,backendName:\"webgl\",kernelFunc:yZ},xZ=uu+`\n  return x < 0.0 ? 0./0. : log(x);\n`,wZ=`\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`,kZ=Ke({opSnippet:xZ,packedOpSnippet:wZ,cpuKernelImpl:aX}),IZ={kernelName:Ga,backendName:\"webgl\",kernelFunc:kZ},SZ=uu+`\n  return log(1.0 + x);\n`,CZ=Ke({opSnippet:SZ}),NZ={kernelName:vl,backendName:\"webgl\",kernelFunc:CZ},TZ=\"return float(a >= 1.0 && b >= 1.0);\",$Z=`\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`,_Z=jt({opSnippet:TZ,packedOpSnippet:$Z,dtype:\"bool\"}),AZ={kernelName:So,backendName:\"webgl\",kernelFunc:_Z},EZ=\"return float(!(x >= 1.0));\",RZ=Ke({opSnippet:EZ}),DZ={kernelName:xl,backendName:\"webgl\",kernelFunc:RZ},FZ=\"return float(a >= 1.0 || b >= 1.0);\",OZ=`\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`,PZ=jt({opSnippet:FZ,packedOpSnippet:OZ,dtype:\"bool\"}),zZ={kernelName:tp,backendName:\"webgl\",kernelFunc:PZ},MZ=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${a}; j <= ${a}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${i}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${o};\n        setOutput(val);\n      }\n    `}},LZ=class{constructor(e,t,n,s,r){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${a};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${a}; j <= ${a}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${o};\n        setOutput(result);\n      }\n    `}},BZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool(\"WEBGL_PACK_NORMALIZATION\")?new LZ(r.shape,a,i,o,u):new MZ(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},VZ={kernelName:np,backendName:\"webgl\",kernelFunc:BZ},WZ=class{constructor(e,t,n,s,r){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${s}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${s})\n                * float(${r})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${r});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}},UZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new WZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},GZ={kernelName:Ig,backendName:\"webgl\",kernelFunc:UZ};function HZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=Ii(o,e.dtype,\"max\",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function c2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[c[N]];let k=Iv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Qp(r,c,n);l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims(\"max\",l,o);let[f,m]=S.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=S.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=iX(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=HZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var qZ={kernelName:Ha,backendName:\"webgl\",kernelFunc:c2},jZ=G1+`\n  return max(a, b);\n`,KZ=`\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+Xp+`\n  return result;\n`,XZ=jt({opSnippet:jZ,packedOpSnippet:KZ,cpuKernelImpl:oX}),YZ={kernelName:qa,backendName:\"webgl\",kernelFunc:XZ};function QZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;su(r,\"maxPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new nl(c,\"max\",!1);return n.runWebGLProgram(p,[r],r.dtype)}var ZZ={kernelName:ja,backendName:\"webgl\",kernelFunc:QZ};function JZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Cv(p,\"max\",!1);return n.runWebGLProgram(d,[r],r.dtype)}var e7={kernelName:sp,backendName:\"webgl\",kernelFunc:JZ},t7=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},n7=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${o};\n           wD += ${r}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${u};\n              wR += ${a}) {\n            float dyR = float(dyRCorner + wR) / ${n}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${l};\n                wC += ${i}) {\n              float dyC = float(dyCCorner + wC) / ${s}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${h} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${u} * ${l} +\n                  wR * ${l} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function s7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Cv(d,\"max\",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new n7(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var r7={kernelName:Cg,backendName:\"webgl\",kernelFunc:s7};function a7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;su([a,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new nl(d,\"max\",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new t7(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var i7={kernelName:Sg,backendName:\"webgl\",kernelFunc:a7};function o7(e,t,n,s){let r=new nl(n,\"max\",!1),a=s.runWebGLProgram(r,[e],\"float32\");r=new nl(n,\"max\",!0,!0,t);let i=s.runWebGLProgram(r,[e],\"float32\");return[a,i]}var u7={kernelName:Ng,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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setOutput(value);\n        } else {\n          ${r} coords = outC - start;\n          setOutput(getX(${o}));\n        }\n      }\n    `}},eJ=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(\",\"),i=t.map((f,m)=>f[0]+e[m]).join(\",\"),o=ln(\"rc\",s),u=ln(\"source\",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;\n       if(${l}) {\n      `,s===1?\"\":`}\n       rc = outputLoc;\n       ${o[s-2]} += 1;\n       if(${o[s-2]} < ${this.outputShape[s-2]}) {`,s===1?\"\":`  ${o[s-1]} += 1;\n         if(${l}) {`],d=s===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",h=\"\";for(let f=0,m=s===1?2:4;f<m;f++)h+=`\n        ${p[f]}\n        if (${d}) {\n          result[${f}] = 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sign(a) * pow(abs(a), b);\n`,sJ=`\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  `+Xp+`\n  return result;\n`,rJ=jt({opSnippet:nJ,packedOpSnippet:sJ}),aJ={kernelName:ei,backendName:\"webgl\",kernelFunc:rJ};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims(\"prod\",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=pX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=pp(r.dtype),v=Ii(b,y,\"prod\",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var oJ={kernelName:ni,backendName:\"webgl\",kernelFunc:iJ},g2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=hX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},uJ={kernelName:Il,backendName:\"webgl\",kernelFunc:g2},lJ=\"return 1.0 / x;\",cJ=Ke({opSnippet:lJ}),dJ={kernelName:Sl,backendName:\"webgl\",kernelFunc:cJ},pJ=ss+`\n  return (x < 0.0) ? 0.0 : x;\n`,hJ=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,fJ=Ke({opSnippet:pJ,packedOpSnippet:hJ}),mJ={kernelName:si,backendName:\"webgl\",kernelFunc:fJ},gJ=ss+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,bJ=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,yJ=Ke({opSnippet:gJ,packedOpSnippet:bJ}),vJ={kernelName:ai,backendName:\"webgl\",kernelFunc:yJ},xJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}},wJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":p=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function kJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new wJ(r.shape,u,l,a,i):new xJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],\"float32\")}var IJ={kernelName:ri,backendName:\"webgl\",kernelFunc:kJ},SJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function CJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new SJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var NJ={kernelName:_g,backendName:\"webgl\",kernelFunc:CJ},TJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},$J=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function _J(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new $J(r.shape,u,l,a,i):new TJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var AJ={kernelName:Cl,backendName:\"webgl\",kernelFunc:_J},EJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function RJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new EJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var DJ={kernelName:$g,backendName:\"webgl\",kernelFunc:RJ},FJ=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet 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jJ={kernelName:Oo,backendName:\"webgl\",kernelFunc:qJ},KJ=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r=\"resRC\",s=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=rt(n);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${s});\n        if (cVal >= 1.0) {\n          setOutput(getA(${r}));\n        } else {\n          setOutput(getB(${r}));\n        }\n      }\n    `}};function XJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new KJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var YJ={kernelName:Po,backendName:\"webgl\",kernelFunc:XJ},QJ=`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: 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u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=CX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new ete(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var nte={kernelName:Nr,backendName:\"webgl\",kernelFunc:y2},ste=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `}},rte=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `}};function Gr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Aw(e){let t=1;for(;t<e;)t*=2;return t}function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=X().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=NX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Gr(n,h);let b=Aw(a),y=Aw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new ste(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,\"int32\",q),Gr(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new rte([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,\"int32\",z),Gr(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=lu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Gr(n,T);let N=l2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Gr(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Gr(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Gr(n,A),[N,v]}var ite={kernelName:Uo,backendName:\"webgl\",kernelFunc:ate},ote=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=n===\"nearest\"?1:2,o;switch(s){case\"constant\":o=1;break;case\"reflect\":o=2;break;case\"wrap\":o=3;break;case\"nearest\":o=4;break;default:o=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function ute(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new ote(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var lte={kernelName:Go,backendName:\"webgl\",kernelFunc:ute};function cte(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;su(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=TX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],\"int32\",l)]}var dte={kernelName:Rg,backendName:\"webgl\",kernelFunc:cte};function pte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=lu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var hte={kernelName:Ho,backendName:\"webgl\",kernelFunc:pte},fte=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function mte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=pp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new fte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=g2({backend:n,attrs:{start:0,stop:E,step:1,dtype:\"float32\"}}),q=y2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,\"unsortedSegmentSum\",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var gte={kernelName:cp,backendName:\"webgl\",kernelFunc:mte},bte=[w8,I8,N8,_8,E8,F8,P8,M8,W8,G8,j8,Y8,J8,sY,iY,uY,cY,fY,gY,yY,kY,_Y,EY,DY,LY,VY,HY,n8,KY,JY,s9,l9,d9,h9,m9,b9,x9,I9,N9,$9,A9,R9,O9,z9,V9,U9,q9,X9,Q9,tQ,aQ,lQ,pQ,mQ,gQ,yQ,xQ,kQ,SQ,NQ,AQ,DQ,PQ,MQ,VQ,GQ,KQ,ZQ,t8,eZ,QY,sZ,iZ,lZ,r8,hZ,bZ,vZ,IZ,NZ,AZ,DZ,zZ,VZ,GZ,qZ,YZ,ZZ,e7,r7,i7,u7,c7,p7,g7,x7,S7,R7,l8,P7,L7,W7,H7,OY,K7,Y7,Z7,tJ,aJ,i8,oJ,uJ,PY,$7,dJ,mJ,vJ,d8,IJ,NJ,AJ,DJ,zJ,LJ,WJ,HJ,jJ,YJ,JJ,see,iee,lee,pee,TY,A7,mee,bee,vee,wee,Iee,Cee,Tee,_ee,Eee,Fee,Pee,Mee,Vee,Uee,Hee,jee,_7,y8,Yee,Jee,nte,ite,lte,v8,dte,hte,gte,X7];for(let e of bte)El(e);var zs=X();zs.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);zs.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);zs.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D\",()=>!1);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);zs.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);zs.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);zs.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);zs.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);zs.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);var yte=\"return a + b;\",vte=\"return areal * breal - aimag * bimag;\",xte=\"return areal * bimag + aimag * breal;\",wte=\"return a / b;\",kte=\"return a * b;\",Ite=\"return (a - b) * (a - b);\",Ste=\"return a - b;\",Cte=\"return f32(a == b);\",Nte=\"return vec4<f32>(a == b);\",Tte=\"return f32(a > b);\",$te=\"return vec4<f32>(a > b);\",_te=\"return f32(a >= b);\",Ate=\"return vec4<f32>(a >= b);\",Ete=\"return f32(a < b);\",Rte=\"return vec4<f32>(a < b);\",Dte=\"return f32(a <= b);\",Fte=\"return vec4<f32>(a <= b);\",Ote=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",Pte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,zte=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,v2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,Mte=`\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `,Lte=`\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `,Bte=\"return f32(a != b);\",Vte=\"return vec4<f32>(a != b);\",Wte=`\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `,Ute=`\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  ${v2}\n  return resultTemp;\n  `,Gte=\"if (a < 0.0) { return b * a; }  return a;\",Hte=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function Ew(e,t){let n=t?v2:zte;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function tc(e,t){switch(e){case 0:return kte;case 1:return yte;case 2:return Ste;case 3:return wte;case 4:return t?Nte:Cte;case 5:return t?$te:Tte;case 6:return t?Ate:_te;case 7:return t?Rte:Ete;case 8:return t?Fte:Dte;case 9:return t?Pte:Ote;case 10:return t?Vte:Bte;case 11:return Ite;case 12:return t?Lte:Mte;case 14:return t?Hte:Gte;case 15:return Ew(\"max\",t);case 16:return Ew(\"min\",t);case 13:return t?Ute:Wte;case 17:return vte;case 18:return xte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var qte=\"return abs(a);\",jte=\"return ceil(a);\",Kte=\"return cos(a);\",Xte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Yte=\"return exp(a) - 1.0;\",Qte=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",Zte=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Jte=\"return exp(a);\",ene=\"return floor(a);\",tne=\"return a;\",nne=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,sne=\"return f32(!(a >= 1.0));\",rne=\"return -a;\",ane=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",ine=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`,one=\"if(a < 0.0) { return 0.0; } return a;\",une=\"return clamp(a, 0.0, 6.0);\",lne=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",cne=`\n  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));\n  let isNaN = isnanVec4(a);\n\n  if (isNaN.r) {\n    resFloat.r = a.r;\n  }\n  if (isNaN.g) {\n    resFloat.g = a.g;\n  }\n  if (isNaN.b) {\n    resFloat.b = a.b;\n  }\n  if (isNaN.a) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,dne=\"return 1.0/sqrt(a);\",pne=\"return 1.0 / (1.0 + exp(-1.0 * a));\",hne=\"return sin(a);\",fne=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,mne=\"return sqrt(a);\",gne=\"return a * a;\",bne=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,yne=\"return f32(i32((a)));\";function qr(e,t){switch(e){case 0:return qte;case 2:return Kte;case 3:return Xte;case 1:return jte;case 4:return t?Zte:Qte;case 5:return Jte;case 6:return Yte;case 7:return ene;case 8:return tne;case 9:return nne;case 10:return sne;case 11:return rne;case 14:return t?ine:ane;case 12:return t?cne:one;case 13:return t?lne:une;case 15:return dne;case 18:return pne;case 16:return hne;case 17:return fne;case 19:return mne;case 20:return gne;case 21:return bne;case 22:return yne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Js(e,t=!1){if(e===null)return null;if(e===\"linear\")return qr(8);if(e===\"relu\")return qr(12,t);if(e===\"elu\")return qr(4,t);if(e===\"relu6\")return qr(13,t);if(e===\"prelu\")return tc(14,t);if(e===\"sigmoid\")return qr(18,t);if(e===\"leakyrelu\")return qr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function vne(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function Nv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function Fr(){return`\n  ${Nv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Ue(){return`\n    ${Fr()}\n      let index = getGlobalIndex();\n`}function xne(e,t,n,s=!1){let r=[];if(r.push(`\n    let workGroupSizeX = ${n.workGroupSize[0]}u;\n    let workGroupSizeY = ${n.workGroupSize[1]}u;\n    let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {\n        return i32(globalId.x);\n      }\n\n      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n          localId.y * workGroupSizeX + localId.x;\n      let workGroupID = (globalId - localId)/vec3<u32>(\n          workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n        workGroupID.y * numWorkgroups.x + workGroupID.x) *\n        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n        localInvocationIndex);\n    }\n  `),s===!0)return r.push(`\n      struct Uniform {\n        size            : i32,\n        numChannels     : i32,\n        outShapeStrides : vec2<i32>,\n        dispatchSize    : vec3<u32>,\n      };\n\n      @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `),[Rw,r.join(`\n`),Dw(t.shape),n.getUserCode()].join(`\n`);let a=\"struct Uniforms { NAN : f32, \";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}, `}),a+=`outShape : ${Wt(t.shape.length)}, `;let i=t.shape.length-1;a+=`\n       outShapeStrides: ${Wt(i)}, `,n.size&&(a+=\"size : i32, \"),n.uniforms&&(a+=n.uniforms),a+=\"};\",r.push(a),n.atomic?r.push(`\n    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n  `):r.push(`\n    @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;\n  `),n.variableNames.forEach((p,d)=>{r.push(`\n    @group(0) @binding(${1+d}) var<storage, read> ${p}: array<${cd(e[d].dtype,n.isVec4)}>;\n    `)}),a!==\"\"&&r.push(`\n    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;\n    `);let[o,u]=Nne(t.shape,n.dispatchLayout),l=[Rw,r.join(`\n`),Dw(t.shape),o,wne(t.shape.length)];if(n.atomic||l.push(kne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>Ine(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);l.push(p)}return l.push(n.getUserCode()),l.join(`\n`)}var Rw=`\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;function wne(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function kne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${r}(value);\n    }`,s>=2){let i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=Wt(s);n?a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}function Ine(e,t,n,s){let r=Sne(e,n);return e.shape.length<=t.length&&(r+=Cne(e,t,n,s)),r}function Sne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=i.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${o}) -> vec4<f32> {\n        return vec4<f32>(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${o}) -> f32 {\n      return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n        ${u})]);\n    }\n   `}function Cne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i=\"get\"+a+\"ByOutput\",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n        fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${r}[globalIndex]);\n        }\n\n        fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n          return vec4<f32>(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n        }\n        `:`\n      fn ${i}Index(globalIndex : i32) -> f32 {\n        return f32(${r}[globalIndex]);\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32 {\n        return f32(${r}[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n      }\n      `;let c=S.getBroadcastDims(e.shape,t),p=u-o,d=\"\";if(o===0)return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n        return get${a}();\n      }\n    `:`\n      fn ${i}Index(globalIndex : i32) -> f32{\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32{\n        return get${a}();\n      }\n    `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords[${g+p}] = 0;`).join(`\n`);let h=\"\";if(u<2&&o>0)h=\"coords\";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+p}]`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n\n      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${d}\n        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n    `:`\n    fn ${i}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n\n    fn ${i}Coords(coordsIn : ${l}) -> f32 {\n      var coords = coordsIn;\n      ${d}\n      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n  `}function Nne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `,a];let i=\"\",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=vne(h,\"uniforms.outShape\");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n    ${i}\n  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function Dw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a=\"var index2 = index;\"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join(\"\");return`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}var x2={};Ae(x2,{ArrayBufferToTypedArray:()=>k2,GPUBytesPerElement:()=>Xm,computeDispatch:()=>$e,computeWorkGroupSizeForConv2d:()=>Tv,computeWorkGroupSizeForMatMul:()=>w2,computeWorkPerThreadForConv2d:()=>$v,flatDispatchLayout:()=>Le,isWebGPUSupported:()=>_v,tilesFitEvenlyIntoShape:()=>Ks});var sa=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function $e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(sa(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(sa(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(sa(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Tv(e,t){let n=sa(e.x.map(r=>t[r])),s=sa(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function w2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function $v(e,t){let n=sa(e.x.map(r=>t[r])),s=sa(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Le(e){return{x:e.map((t,n)=>n)}}function Xm(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function k2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function _v(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}function I2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>\"tileInner must be divisible by 4. And ColPerThread must be 4\"),`\n  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let TileInner = ${s};\n\n  ${Fr()}\n\n    let tileRow = ${t===1?\"0\":\"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var ACached : vec4<f32>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / ColPerThread;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {\n            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];\n            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];\n            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];\n            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                acc[i] = BCached[3] * ACached.w + acc[i];\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var Tne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let i=Js(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${i}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${i}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${I2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}\n    `}};function Av(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`\n    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;\n    ${Fr()}\n      let tileRow = i32(localId.y) * ${e[1]};\n      let tileCol = i32(localId.x) * ${e[0]};\n\n      let globalRow = i32(globalId.y) * ${e[1]};\n      let globalCol = i32(globalId.x) * ${e[0]};\n\n      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;\n\n      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;\n      var ACached : f32;\n      var BCached : array<f32, ${e[0]}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let ColPerThreadA = ${r} / ${t[0]};\n      let tileColA = i32(localId.x) * ColPerThreadA;\n      let RowPerThreadB = ${r} / ${t[1]};\n      let tileRowB = i32(localId.y) * RowPerThreadB;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileColA + innerCol;\n\n            mm_Asub[inputRow][inputCol] = mm_readA(\n                globalRow + innerRow,\n                t * ${r} + inputCol, globalId);\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${r} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${r}; k = k + 1) {\n          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n            ACached = mm_Asub[tileRow + innerRow][k];\n            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `}function $ne(e){return`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${Fr()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),\n                                mm_readA(globalRow, colA + 1, globalId),\n                                mm_readA(globalRow, colA + 2, globalId),\n                                mm_readA(globalRow, colA + 3, globalId));\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `}var S2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=w2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?\"return A[batch * batchASize + row * uniforms.dimInner + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch * batchASize + row * uniforms.dimInner + col];\n           }\n           return 0.0;`:e=this.fitA?\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch* batchASize + col * uniforms.dimAOuter + row];\n           }\n           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n           }\n           return 0.0;`:t=this.fitB?\"return B[batch * batchBSize + col * uniforms.dimInner + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + col * uniforms.dimInner + row];\n           }\n           return 0.0;`;let n=\"\",s=\"\";if(this.activation){let i=Js(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1]>1?Av([this.workPerThread,this.workPerThread,1],this.workGroupSize):$ne(this.workGroupSize)}\n    `}};function _ne(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${Fr()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var Ane=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e=\"return A[batch * batchASize + row * uniforms.dimInner + col];\":e=\"return A[batch * batchASize + col * uniforms.dimAOuter + row];\";let t;this.transposeB===!1?t=\"return B[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let i=Js(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        ${e}\n      }\n\n      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${_ne()}\n    `}};function Ene(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${Fr()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var Rne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push(\"bias\");let o=a!=null;o&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let i=Js(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${i}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${i}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${Ene(this.workGroupSize)}\n    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Dne={kernelName:Ro,backendName:\"webgpu\",kernelFunc:Me};function Ev({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ko.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new Ane([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new Rne(k,T,[P,h,f],a,u,i):R?F=new Tne(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),a,u,i):F=new S2(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}];u===\"leakyrelu\"&&(z.push({type:\"float32\",data:[o]}),F.uniforms+=\" alpha : f32,\");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Fne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Ev({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var One={kernelName:aa,backendName:\"webgpu\",kernelFunc:Fne},Fw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},Pne=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${tc(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},zne=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${tc(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},C2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function Ow(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new zne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Pne(e,t,n,a):new C2(e,t,n)}function Wn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Mne={kernelName:Wa,backendName:\"webgpu\",kernelFunc:Wn};function cu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.tensorMap.get(a.dataId),o=Wn({inputs:{x:s},backend:n}),u=Wn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var Lne={kernelName:Xd,backendName:\"webgpu\",kernelFunc:cu},nc=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${qr(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype===\"complex64\"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Ow(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Fw(17,i.shape,o.shape),b=new Fw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=cu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype===\"string\"||o.dtype===\"string\"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype===\"string\"?S.fromUint8ToStringArray(p):p,f=i.dtype===\"string\"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Ow(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:Bne,ceilImpl:Vne,concatImpl:Wne,equalImpl:Une,expImpl:Gne,expm1Impl:Hne,floorImpl:qne,gatherNdImpl:jne,gatherV2Impl:Kne,greaterEqualImpl:Xne,greaterImpl:Yne,lessEqualImpl:Qne,lessImpl:Zne,logImpl:Jne,maxImpl:ese,maximumImpl:tse,minimumImpl:nse,multiplyImpl:sse,negImpl:rse,notEqualImpl:ase,prodImpl:ise,rangeImpl:ose,rsqrtImpl:use,simpleAbsImpl:lse,sliceImpl:cse,stridedSliceImpl:dse,stringNGramsImpl:pse,subImpl:hse,tileImpl:fse,topKImpl:mse,transposeImpl:gse,uniqueImpl:Zpe}=ev,bse=Kt({opType:0,cpuKernelImpl:lse}),yse={kernelName:uo,backendName:\"webgpu\",kernelFunc:bse},vse=mn({opSnippet:1,cpuKernelImpl:Bne,supportsComplex:!0}),xse={kernelName:Sr,backendName:\"webgpu\",kernelFunc:vse},wse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function kse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Wn({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new wse(a);return n.runWebGPUProgram(i,s,r)}var Ise={kernelName:Ia,backendName:\"webgpu\",kernelFunc:kse},N2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"axis : i32, infinityValue : f32,\",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape[${r}]`;return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      // In order to get a flattened index into the input tensor, we need to\n      // add back the index along the reduced dimension to |outputCoords|.\n      // This function outputs the offset to the first value along\n      // |axis| and the stride to get the next value of the input along |axis|.\n      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        var i = ${this.outputShape.length-1};\n\n        var stride = 1;\n        var inputStride = 1;\n        var offset = 0;\n\n        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {\n          let length = ${n(`${this.inputShape.length} - r`)};\n          if (${this.inputShape.length} - r == uniforms.axis) {\n            inputStride = stride;\n          } else {\n            offset = offset + ${t(\"outputCoords\",\"i\")} * stride;\n            i = i - 1;\n          }\n          stride = stride * length;\n        }\n\n        return vec2<i32>(offset, inputStride);\n      }\n\n      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{\n        return coordInfo[0] + coordInfo[1] * index;\n      }\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let coordInfo = getInputCoordInfo(outputIndex);\n        let Length = ${n(\"uniforms.axis\")};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n\n        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = f32(x[getInputIndex(coordInfo, k)]);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(Length), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},Sse=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${Nv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},Cse=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=Nse(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function Nse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function Xs(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=gse(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new Sse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new Cse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var Tse={kernelName:mi,backendName:\"webgpu\",kernelFunc:Xs};function $se(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Xs({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=new N2(u.shape,i[0],\"max\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var _se={kernelName:Sa,backendName:\"webgpu\",kernelFunc:$se};function Ase(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Xs({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=new N2(u.shape,i[0],\"min\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var Ese={kernelName:ul,backendName:\"webgpu\",kernelFunc:Ase},T2=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,\",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},$2=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>,\",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function Rse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new $2(c):(p=new T2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var Dse={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Rse};function Fse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Ev({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Ose={kernelName:Na,backendName:\"webgpu\",kernelFunc:Fse},Pse=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=zse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${Ym[a]} = uniforms.start[${a}] + coords.${Ym[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},Ym=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function zse(e){if(e===1)return\"sourceLoc\";if(e<=6)return Ym.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function du(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=cse(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Pse(o,u),c=[{type:\"int32\",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var Mse={kernelName:zo,backendName:\"webgpu\",kernelFunc:du},Lse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=Xs({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=du({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},Bse={kernelName:lo,backendName:\"webgpu\",kernelFunc:Lse},_2=mn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:ase}),Vse={kernelName:No,backendName:\"webgpu\",kernelFunc:_2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var Wse={kernelName:rp,backendName:\"webgpu\",kernelFunc:sc};function Use(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Qm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Wn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Qm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=cu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype===\"complex64\"){let i=sc({inputs:{input:r},backend:n}),o=Qm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return Use(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=_2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Gse={kernelName:Ta,backendName:\"webgpu\",kernelFunc:Qm},Hse=Kt({opType:1,cpuKernelImpl:Vne}),qse={kernelName:$a,backendName:\"webgpu\",kernelFunc:Hse},jse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},Kse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function Xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new jse(r.shape):o=new Kse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Yse={kernelName:Cr,backendName:\"webgpu\",kernelFunc:Xse},Qse=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function eh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Zse={kernelName:ep,backendName:\"webgpu\",kernelFunc:eh};function Zm(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>eh({inputs:{input:y},backend:n})),m=Zm(h,t,n),g=Zm(f,t,n),b=cu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Wne(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Jse(e,t,n),o=a.map(h=>h.shape),u=new Qse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Jse(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function A2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Zm(o,a,n)}var ere={kernelName:co,backendName:\"webgpu\",kernelFunc:A2},tre=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.hasLeakyreluAlpha&&this.variableNames.push(\"leakyreluAlpha\"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);\n    let divBy4Remainder${e} = flatIndex${e} % 4;\n    let divBy4Index${e} = flatIndex${e} / 4;\n    let curData${e} = x[divBy4Index${e}];\n    if (divBy4Remainder${e} == 0) {\n      temp = curData${e};\n    } else {\n      // TODO: This could end up being a redundant load with another one in\n      // the same shader invocation. Perhaps there's an opportunity for\n      // optimization\n      let nextData${e} = x[divBy4Index${e} + 1];\n      if (divBy4Remainder${e} == 1) {\n        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);\n      } else if (divBy4Remainder${e} == 2) {\n        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);\n      } else if (divBy4Remainder${e} == 3) {\n        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);\n      }\n    }\n    `}getUserCode(){let e=I2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        var coord = vec4<i32>(\n            batch,\n            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n            inChCoord);\n        var resData = vec4<f32>(0.0);\n        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for\n          // the 'same' padding type.\n          if (coordsInBounds4D(coord, uniforms.xShape)) {\n            resData = x[getIndexFromCoords4D(coord, uniforms.xShape) / 4];\n          } else {\n            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);\n          ${this.getSampleAWithRemainder(1)}\n          resData = temp;\n          if (WCol == (uniforms.filterDims[1] - 1)) {\n            coord = vec4<i32>(\n              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);\n              ${this.getSampleAWithRemainder(2)}\n            if (inChCoord == 0) {\n              resData = vec4<f32>(resData.xyz, temp.x);\n            } else if (inChCoord == 1) {\n              resData = vec4<f32>(resData.xy, temp.xy);\n            } else {\n              resData = vec4<f32>(resData.x, temp.xyz);\n            }\n          }\n          `}\n        return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${s}\n         }\n         return vec4<f32>(0.0);\n        `,a=this.fitB?\"return W[row * uniforms.dimBOuter / 4 + col];\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `,i=\"\",o=\"\";if(this.activation){let c=Js(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${c}\n        }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {\n          let b = getLeakyreluAlphaByOutputCoords(outCoord);\n          ${c}\n        }`,new Error(\"Leakyrelu is not supported.\");i=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${c}\n        }`}o=\"value = activation(value, outCoord);\"}let u=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n        ${i}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          let r = row;\n          let c = col * 4;\n          var batch = i32(globalId.z);\n          ${r}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${a}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${u}\n            ${o}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${e}\n      `}},nre=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tv(this.dispatchLayout,this.outputShape),this.elementsPerThread=$v(this.dispatchLayout,this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=Av(this.elementsPerThread,this.workGroupSize),t=`\n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);\n    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];\n    let coord = vec4<i32>(\n        batch,\n        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n        col % uniforms.xShape[3]);\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${t}\n    }\n    return 0.0;\n    `,s=this.fitB?\"return W[row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter + col];\n\t }\n\t return 0.0;\n\t `,r=\"\",a=\"\";if(this.activation){let u=Js(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${u}\n                }`:r=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${u}\n                  }\n                `,a=\"value = activation(value, outCoord);\"}let i=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n    ${r}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${n}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      ${s}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      ${i}\n      ${a}\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${e}\n  `}},sre=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Js(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${r}\n             }`:e=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n                    ${r}\n                  }\n                `,t=\"value = activation(value, outCoord);\"}let n=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${e}\n      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if(coordsInBounds4D(coord, uniforms.xShape)) {\n          return getX(batch, row, col, chan);\n        }\n        return 0.0;\n      }\n\n      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n        let coord = vec4<i32>(row, col, xChannel, outChannel);\n        if(coordsInBounds4D(coord, uniforms.wShape)) {\n          return getW(row, col, xChannel, outChannel);\n        }\n        return 0.0;\n      }\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          ${n}\n          ${t}\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Fr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let outChannel = coords[3];\n\n        var acc = 0.0;\n\n        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {\n              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n              let v = readInp(batch, coordRow, coordCol, xChannel);\n              let f = readFilt(row, col, xChannel, outChannel);\n              acc = acc + v * f;\n            }\n          }\n        }\n\n        writeResult(batch, coords[1], coords[2], outChannel, acc);\n      }\n    `}},rre=class{constructor(e,t){this.variableNames=[\"A\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,\n      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`\n    ${Ue()}\n\n      for(var i = 0; i<${this.workPerThread}; i = i + 1) {\n        let flatIndex = index * ${this.workPerThread} + i;\n\n        let rc = getCoordsFromIndex(flatIndex);\n\n        if(flatIndex < uniforms.size) {\n          let blockIndex = rc[0];\n          let pos = rc[1];\n\n          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];\n          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;\n          var value = 0.0;\n          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {\n            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -\n              uniforms.pad[0];\n            let d1 = offsetX + uniforms.dilation[0] * ((pos %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n            let ch = pos % uniforms.inChannels;\n            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {\n              value = getA(d0, d1, ch);\n            }\n          }\n          setOutputAtIndex(flatIndex, value);\n        }\n      }\n    }\n  `}};function are({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat===\"channelsLast\",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=Ev({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function ire({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y===\"channelsLast\",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new rre(T,v),$=[{type:\"int32\",data:[h.left,h.top]},{type:\"int32\",data:[p,d]},{type:\"int32\",data:[g,b]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*u]},{type:\"int32\",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new S2(q,[1,k,n.outChannels],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:\"int32\",data:[Y]},{type:\"int32\",data:[te]},{type:\"int32\",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o===\"leakyrelu\"&&($.push({type:\"float32\",data:[i]}),K.uniforms+=\" alpha : f32,\");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function E2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return are({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")&&e.shape[0]===1)return ire({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool(\"WEBGPU_USE_NAIVE_CONV2D\"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type===\"VALID\")&&n.outChannels%4===0,f=[n.padInfo.top,n.padInfo.left],m=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...f]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new sre(n,u,o,l);else{h?c=new tre(n,u,o,l):c=new nre(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:\"int32\",data:[b]},{type:\"int32\",data:[y]},{type:\"int32\",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o===\"leakyrelu\"&&(m.push({type:\"float32\",data:[i]}),c.uniforms+=\" alpha : f32,\"),s.runWebGPUProgram(c,g,e.dtype,m)}function ore(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return E2({x:r,filter:a,convInfo:d,backend:s})}var ure={kernelName:_a,backendName:\"webgpu\",kernelFunc:ore},lre=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tv(this.dispatchLayout,this.outputShape),this.elementsPerThread=$v(this.dispatchLayout,this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${Av(this.elementsPerThread,this.workGroupSize)}\n  `}},cre=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new cre(d);else{f=new lre(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var pre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:dre},hre=Kt({opType:2}),fre={kernelName:Ea,backendName:\"webgpu\",kernelFunc:hre},mre=Kt({opType:3}),gre={kernelName:Ra,backendName:\"webgpu\",kernelFunc:mre},bre=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${i};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${o};\n        if( in_x < 0.0 || in_x > ${t} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `}},yre=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new bre(r.shape[3],a.shape,o,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],\"float32\",p)},vre={kernelName:ho,backendName:\"webgpu\",kernelFunc:yre},Pw=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===\"*\"?\"1.0\":\"0.0\",n=this.exclusive?t:`getX(${zw(e,\"coords\",this.op)})`,s=this.outputShape[this.outputShape.length-1],r=\"\",a=\"\";return this.exclusive?(r=this.reverse?`end != ${s-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(r=this.reverse?`end + pow2 < ${s}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n      ${Ue()}\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${Mw(e,\"coords\",this.op)};\n         var val = ${n};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${r}) {\n           let idx = ${a};\n           ${Mw(e,\"coords\",this.op)} = idx;\n           val ${this.op}= getX(${zw(e,\"coords\",this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `}};function zw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function Mw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function R2(e,t,n,s,r,a){let i=t.shape.length,o=S.getAxesPermutation([s],i),u=t;o!=null&&(u=Xs({inputs:{x:t},backend:n,attrs:{perm:o}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Wn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Pw(e,u.shape,!1,a),f=p,m=[{type:\"float32\",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new Pw(e,u.shape,r,a),h=p,f=[{type:\"float32\",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=Xs({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(u.dataId),h}return p}function xre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return R2(\"*\",r,n,a,i,o)}var wre={kernelName:po,backendName:\"webgpu\",kernelFunc:xre};function kre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return R2(\"+\",r,n,a,i,o)}var Ire={kernelName:Da,backendName:\"webgpu\",kernelFunc:kre},Sre=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=[{type:\"int32\",data:[a]}],g=new Sre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Nre={kernelName:fo,backendName:\"webgpu\",kernelFunc:Cre},D2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Js(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${Nv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},F2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,\n      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Js(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Fr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function Tre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new D2(p):(h=new F2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var $re={kernelName:Fa,backendName:\"webgpu\",kernelFunc:Tre},O2=mn({opSnippet:0,cpuKernelImpl:sse,supportsComplex:!0}),_re={kernelName:Za,backendName:\"webgpu\",kernelFunc:O2},Are=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32,\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=Xs({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=ese(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=ise(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":pp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new Are(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Rv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"sum\",n)}var Ere={kernelName:ci,backendName:\"webgpu\",kernelFunc:Rv};function Rre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=Xs({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=O2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Rv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var Dre={kernelName:Jd,backendName:\"webgpu\",kernelFunc:Rre},Fre=Kt({opType:4}),Ore={kernelName:Pa,backendName:\"webgpu\",kernelFunc:Fre},Pre=mn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:Une}),zre={kernelName:mo,backendName:\"webgpu\",kernelFunc:Pre},P2=Kt({opType:5,cpuKernelImpl:Gne,dtype:\"float32\"}),Mre={kernelName:za,backendName:\"webgpu\",kernelFunc:P2};function Jm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Lre={kernelName:go,backendName:\"webgpu\",kernelFunc:Jm},Bre=Kt({opType:6,cpuKernelImpl:Hne}),Vre={kernelName:bo,backendName:\"webgpu\",kernelFunc:Bre},Wre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function pu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Wre(s),o=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var Ure={kernelName:ml,backendName:\"webgpu\",kernelFunc:pu},Gre=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},Hre={kernelName:yo,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Gre(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},qre=Kt({opType:7,cpuKernelImpl:qne}),jre={kernelName:Ma,backendName:\"webgpu\",kernelFunc:qre},Kre=mn({opSnippet:12,dtype:\"int32\"}),Xre={kernelName:La,backendName:\"webgpu\",kernelFunc:Kre},Yre=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},z2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=xne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:\"main\"},label:t.constructor.name})};function M2(e,t,n,s=\"\",r=\"\"){return e.shaderKey+\"_\"+(e.workGroupSize?e.workGroupSize.join(\",\"):\"\")+t.map(i=>i.length).join(\",\")+n.join(\",\")+e.variableNames.join(\",\")+s+r}function Lw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,\"int32\"),c=n.getFromPixelsProgram(a?\"import\":\"copyExternal\");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?\"import\":\"copyExternal\"],h=M2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>z2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var Qre={kernelName:fd,backendName:\"webgpu\",kernelFunc:Zre},Wi;function Zre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let i=typeof HTMLVideoElement!=\"undefined\"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!=\"undefined\"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!=\"undefined\"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!=\"undefined\"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool(\"WEBGPU_USE_IMPORT\")&&i)return Lw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Wi==null&&(Wi=document.createElement(\"canvas\").getContext(\"2d\")),Wi.canvas.width=c,Wi.canvas.height=p,Wi.drawImage(r,0,0,c,p),r=Wi.canvas),l||u||i||o)return Lw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,\"int32\"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Jre=class{constructor(e,t,n,s,r){this.uniforms=\"varianceEpsilon : f32,\",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\")),this.offsetShape=s,this.scaleShape=r,this.shaderKey=\"batchNorm\"}getUserCode(){let e=\"0.0\";this.offsetShape!=null&&(e=\"getOffsetByOutputIndex(index)\");let t=\"1.0\";return this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},eae={kernelName:Ba,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Jre(s.shape,i.shape,o.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return E2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var nae={kernelName:ia,backendName:\"webgpu\",kernelFunc:tae};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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rae={kernelName:oa,backendName:\"webgpu\",kernelFunc:sae},aae=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function iae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=jne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new aae(i,[l,c]),m=[{type:\"int32\",data:[i]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var oae={kernelName:xo,backendName:\"webgpu\",kernelFunc:iae},uae=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=lae(this.aShape);return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${e}));\n        }\n      }\n    `}};function lae(e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],n=[];for(let s=0;s<e.length;s++)s===2?n.push(\"indexZ\"):n.push(`${t[s]}`);return n.join()}function L2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=Kne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new uae(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var cae={kernelName:vo,backendName:\"webgpu\",kernelFunc:L2},dae=mn({opSnippet:5,cpuKernelImpl:Yne,dtype:\"bool\"}),pae={kernelName:wo,backendName:\"webgpu\",kernelFunc:dae},hae=mn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:Xne}),fae={kernelName:Va,backendName:\"webgpu\",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:\"float32\",data:[a]}],o=new nc(r.shape,14);return o.uniforms=\"alpha : f32,\",n.runWebGPUProgram(o,[r],\"float32\",i)}var gae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:mae},bae=mn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:Zne}),yae={kernelName:ko,backendName:\"webgpu\",kernelFunc:bae},vae=mn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:Qne}),xae={kernelName:Io,backendName:\"webgpu\",kernelFunc:vae},wae=Kt({opType:9,cpuKernelImpl:Jne}),kae={kernelName:Ga,backendName:\"webgpu\",kernelFunc:wae},Iae=mn({opSnippet:9,dtype:\"bool\"}),Sae={kernelName:So,backendName:\"webgpu\",kernelFunc:Iae},Cae=Kt({opType:10}),Nae={kernelName:xl,backendName:\"webgpu\",kernelFunc:Cae};function B2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,\"max\",n)}var Tae={kernelName:Ha,backendName:\"webgpu\",kernelFunc:B2},$ae=mn({opSnippet:15,cpuKernelImpl:tse}),_ae={kernelName:qa,backendName:\"webgpu\",kernelFunc:$ae};function Aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});p=new $2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new T2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var Eae={kernelName:ja,backendName:\"webgpu\",kernelFunc:Aae};function Rae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,\"mean\",n)}var Dae={kernelName:Ka,backendName:\"webgpu\",kernelFunc:Rae};function 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uniforms.xShape${e>1?`[${l}]`:\"\"}`).join(\",\"),s=e===1?\"start\":\"start[i]\",r=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=Wt(e),o=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${i}(${t});\n          let end = ${i}(${n});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${e}; i = i + 1) {\n            if (${a} < ${s}) {\n              ${a} = ${s} * 2 - ${a} - ${this.offset};\n            } else if(${a} >= ${r}) {\n              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${o}));\n        }\n      }\n    `}},Lae={kernelName:Qa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:\"int32\",data:[c[0],c[1]]})),u=new Mae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Bae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=rse(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Vae={kernelName:Co,backendName:\"webgpu\",kernelFunc:Bae};function Wae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var Uae={kernelName:To,backendName:\"webgpu\",kernelFunc:Wae};function Gae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var Hae={kernelName:$o,backendName:\"webgpu\",kernelFunc:Gae};function Vd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=Vd({inputs:{x:r},backend:n}),i=eh({inputs:{input:s},backend:n}),o=Vd({inputs:{x:i},backend:n}),u=cu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return pu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var qae={kernelName:qo,backendName:\"webgpu\",kernelFunc:Vd};function V2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=V2({inputs:{x:r},backend:n}),i=eh({inputs:{input:s},backend:n}),o=Vd({inputs:{x:i},backend:n}),u=cu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return pu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var jae={kernelName:_o,backendName:\"webgpu\",kernelFunc:V2};function Kae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Jm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let o=[],u=t.map(c=>{let p=Jm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=A2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Xae={kernelName:Eo,backendName:\"webgpu\",kernelFunc:Kae},Yae=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?\"any(outC < start)\":\"outC < start\",o=e>1?\"any(outC >= end)\":\"outC >= 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n.runWebGPUProgram(u,[r],r.dtype,o)},Qae={kernelName:Ja,backendName:\"webgpu\",kernelFunc:W2},Zae=mn({opSnippet:13}),Jae={kernelName:ei,backendName:\"webgpu\",kernelFunc:Zae};function eie(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new C2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var tie={kernelName:ti,backendName:\"webgpu\",kernelFunc:eie};function nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"prod\",n)}var sie={kernelName:ni,backendName:\"webgpu\",kernelFunc:nie},rie=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=ose(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},aie={kernelName:Il,backendName:\"webgpu\",kernelFunc:rie},U2=mn({opSnippet:3}),iie={kernelName:Oa,backendName:\"webgpu\",kernelFunc:U2},oie=Kt({opType:12}),uie={kernelName:si,backendName:\"webgpu\",kernelFunc:oie},lie=Kt({opType:13}),cie={kernelName:ai,backendName:\"webgpu\",kernelFunc:lie},die=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[o?.5:0]}],f=new die(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var hie={kernelName:ri,backendName:\"webgpu\",kernelFunc:pie},fie=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>, roundBase : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function mie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new fie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var gie={kernelName:Cl,backendName:\"webgpu\",kernelFunc:mie},bie=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},yie={kernelName:jo,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new bie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},vie=Kt({opType:15,cpuKernelImpl:use}),xie={kernelName:ii,backendName:\"webgpu\",kernelFunc:vie},wie=class{constructor(e,t,n,s,r,a,i){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Le(e),this.dispatch=$e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${s})`,o=this.type===\"int32\"?\"atomicAdd(&(result[flatIndex]), i32(updateValue));\":`\n     var assumed = atomicLoad(&(result[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${i};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${o}\n        }\n      }`}};function kie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=pu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[o]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new wie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Iie={kernelName:Oo,backendName:\"webgpu\",kernelFunc:kie},Sie=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function Cie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Sie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var Nie={kernelName:Po,backendName:\"webgpu\",kernelFunc:Cie},Tie=Kt({opType:18}),$ie={kernelName:ui,backendName:\"webgpu\",kernelFunc:Tie},_ie=Kt({opType:16}),Aie={kernelName:oi,backendName:\"webgpu\",kernelFunc:_ie},Eie=Kt({opType:17}),Rie={kernelName:Mo,backendName:\"webgpu\",kernelFunc:Eie},G2=mn({opSnippet:2,cpuKernelImpl:hse,supportsComplex:!0}),Die={kernelName:hi,backendName:\"webgpu\",kernelFunc:G2};function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=B2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=G2({inputs:{a:r,b:l},backend:n}),p=P2({inputs:{x:c},backend:n}),d=Rv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=U2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var Oie={kernelName:di,backendName:\"webgpu\",kernelFunc:Fie},Pie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=W2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Xs({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},zie={kernelName:Lo,backendName:\"webgpu\",kernelFunc:Pie},Mie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:c}],f=new Mie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Bie={kernelName:up,backendName:\"webgpu\",kernelFunc:Lie};function Vie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=du({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var Wie={kernelName:Bo,backendName:\"webgpu\",kernelFunc:Vie},Uie=Kt({opType:19}),Gie={kernelName:li,backendName:\"webgpu\",kernelFunc:Uie},Hie={kernelName:Al,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},qie=mn({opSnippet:11}),jie={kernelName:pi,backendName:\"webgpu\",kernelFunc:qie},Kie=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function Xie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=du({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=dse(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Kie(h),E=[{type:\"int32\",data:y},{type:\"int32\",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Yie={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Xie};function Qie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=pse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var Zie={kernelName:lp,backendName:\"webgpu\",kernelFunc:Qie},Jie=Kt({opType:21}),eoe={kernelName:fi,backendName:\"webgpu\",kernelFunc:Jie},toe=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=noe(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function noe(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function soe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=fse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new toe(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var roe={kernelName:Nr,backendName:\"webgpu\",kernelFunc:soe},aoe=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},ioe=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Ui(e,t){t!==null&&e.disposeData(t.dataId)}function Bw(e){let t=1;for(;t<e;)t*=2;return t}function ooe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=mse(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,\"int32\",[])];if(u===1)return[r,pu({attrs:{shape:o,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Bw(a),h=Bw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new aoe(N),R=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,\"int32\",R),Ui(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new ioe([c,k/2]),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,\"int32\",A),Ui(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=du({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Ui(n,b);let y=L2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Ui(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Ui(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Ui(n,x),[y,f]}var uoe={kernelName:Uo,backendName:\"webgpu\",kernelFunc:ooe},loe=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function coe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new loe(g),y=i===\"nearest\"?1:2,v;switch(o){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var doe={kernelName:Go,backendName:\"webgpu\",kernelFunc:coe};function poe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=du({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var hoe={kernelName:Ho,backendName:\"webgpu\",kernelFunc:poe},foe=[One,yse,xse,Ise,_se,Ese,Dse,Ose,Bse,Gse,qse,Yse,Lne,ere,ure,pre,fre,gre,vre,wre,Ire,Nre,$re,Dre,Ore,zre,Mre,Lre,Vre,Ure,Hre,Qre,jre,Xre,eae,nae,rae,oae,cae,pae,fae,Mne,Zse,gae,yae,xae,kae,Sae,Nae,Tae,_ae,Eae,Dae,Oae,zae,Lae,_re,Vae,Uae,Hae,Vse,jae,Xae,Qae,Jae,tie,sie,aie,Wse,iie,uie,cie,Dne,hie,gie,yie,xie,Iie,Nie,$ie,Aie,Rie,Mse,Yie,Zie,Oie,zie,Bie,Wie,Gie,Hie,jie,Die,Ere,eoe,roe,uoe,doe,Tse,hoe,qae];for(let e of foe)El(e);var moe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Vw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Vw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Vw(e,t){return`${e}_${t}`}var H2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey=\"fromPixels\",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:\"rgba8unorm\",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},goe=class extends H2{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},boe=X().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Ww=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[a,a,a]):[a,a,1]},q2=class extends sl{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!_v())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new moe(this.device),this.tensorMap=new Hd(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return q2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*Xm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*Xm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case\"copyExternal\":return this.fromPixelProgram||(this.fromPixelProgram=new H2),this.fromPixelProgram;case\"import\":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new goe),this.fromPixelImportProgram;default:w.assert(!1,()=>\"Unsupported fromPixels shape\");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=k2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type===\"int32\"?new Int32Array(s,o,a.data.length).set(a.data):a.type===\"uint32\"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Ww(this.device,e);let a=[{type:\"float32\",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o=\"int32\";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join(\"_\"),f=d.map(N=>N.join(\"_\")).join(\";\"),m=M2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>z2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Yre(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Ww(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=boe){return X().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Dv=q2;Dv.nextDataId=0;var yoe={};Ae(yoe,{WebGPUBackend:()=>Dv,webgpu_util:()=>x2});_v()&&fp(\"webgpu\",async()=>{X().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:X().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has(\"timestamp-query\");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=[\"timestamp-query\"]:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.\");let a=await t.requestDevice(s);return new Dv(a,r)},3);var It=(e=>(e[e.float32=0]=\"float32\",e[e.int32=1]=\"int32\",e[e.bool=2]=\"bool\",e[e.string=3]=\"string\",e[e.complex64=4]=\"complex64\",e))(It||{}),th=(e=>(e[e.linear=0]=\"linear\",e[e.relu=1]=\"relu\",e[e.relu6=2]=\"relu6\",e[e.prelu=3]=\"prelu\",e[e.leakyrelu=4]=\"leakyrelu\",e[e.sigmoid=5]=\"sigmoid\",e[e.elu=6]=\"elu\",e))(th||{}),j2;function voe(e){j2=e.wasm.cwrap(aa,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function xoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=th[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=Ko.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return j2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var woe={kernelName:aa,backendName:\"wasm\",setupFunc:voe,kernelFunc:xoe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var koe=Xt(uo);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var Ioe=!0,Soe=gn(Sr,Ioe),K2;function Coe(e){K2=e.wasm.cwrap(Ia,null,[\"array\",\"number\",\"number\",\"number\"])}function Noe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return K2(a,r.length,It[s.dtype],i),s}var Toe={kernelName:Ia,backendName:\"wasm\",setupFunc:Coe,kernelFunc:Noe};function nh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var $oe={kernelName:Wa,backendName:\"wasm\",kernelFunc:nh},X2;function _oe(e){X2=e.wasm.cwrap(mi,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function kr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Eoe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=Aoe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=nh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return X2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function Aoe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function Eoe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var Roe={kernelName:mi,backendName:\"wasm\",kernelFunc:kr,setupFunc:_oe};function Or(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=kr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var Y2;function Doe(e){Y2=e.wasm.cwrap(il,null,[\"number, number, number\"])}function Foe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;Y2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ooe={kernelName:il,backendName:\"wasm\",setupFunc:Doe,kernelFunc:Foe},Q2;function Poe(e){Q2=e.wasm.cwrap(ol,null,[\"number, number, number\"])}function zoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;Q2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Moe={kernelName:ol,backendName:\"wasm\",setupFunc:Poe,kernelFunc:zoe},Z2;function Loe(e){Z2=e.wasm.cwrap(Sa,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Boe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Or(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return Z2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Voe={kernelName:Sa,backendName:\"wasm\",kernelFunc:Boe,setupFunc:Loe},J2;function Woe(e){J2=e.wasm.cwrap(Ca,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Uoe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return J2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var Goe={kernelName:Ca,backendName:\"wasm\",setupFunc:Woe,kernelFunc:Uoe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. 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t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype===\"string\"){let f=Pd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Xoe(u,c[0],d,a,i);else if(h===3)Yoe(u,c[0],c[1],d,a,i);else if(h===4)Qoe(u,c[0],c[1],c[2],d,a,i);else{let f=Pd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Xoe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Yoe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Qoe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Zoe={kernelName:zo,backendName:\"wasm\",kernelFunc:xa};function Joe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=kr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=xa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var eue={kernelName:lo,backendName:\"wasm\",kernelFunc:Joe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var tue={kernelName:Ta,backendName:\"wasm\",kernelFunc:ac},nue=Xt($a),tN;function sue(e){tN=e.wasm.cwrap(Cr,null,[\"number\",\"number\",\"number\",\"number\"])}function 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uue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=S.convertConv2DDataFormat(d),f=S.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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due(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=S.convertConv2DDataFormat(u),h=S.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:T,strideHeight:N,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat===\"channelsLast\",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,\"float32\"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return rN(me,ke,f,m,g,y,v,b,k,T,x,N,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var pue={kernelName:Aa,backendName:\"wasm\",setupFunc:cue,kernelFunc:due},hue=Xt(Ea),fue=Xt(Ra),aN=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(aN||{}),iN;function mue(e){iN=e.wasm.cwrap(ho,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!==\"float32\"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return iN(g,b,y,c,k,p,d,aN[r],a,x),m!=null&&t.disposeData(m.dataId),v}var bue={kernelName:ho,backendName:\"wasm\",setupFunc:mue,kernelFunc:gue},oN;function yue(e){oN=e.wasm.cwrap(po,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function vue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=kr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumprod\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;oN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=kr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var xue={kernelName:po,backendName:\"wasm\",setupFunc:yue,kernelFunc:vue},uN;function wue(e){uN=e.wasm.cwrap(Da,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function kue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=kr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;uN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=kr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Iue={kernelName:Da,backendName:\"wasm\",setupFunc:wue,kernelFunc:kue},lN;function Sue(e){lN=e.wasm.cwrap(fo,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Cue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return lN(b,a,i===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var Nue={kernelName:fo,backendName:\"wasm\",setupFunc:Sue,kernelFunc:Cue},cN;function Tue(e){cN=e.wasm.cwrap(Fa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function $ue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=S.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return fN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var jue={kernelName:ia,backendName:\"wasm\",setupFunc:Hue,kernelFunc:que},mN;function Kue(e){mN=e.wasm.cwrap(oa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Xue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=th[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return mN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Yue={kernelName:oa,backendName:\"wasm\",setupFunc:Kue,kernelFunc:Xue},gN;function Que(e){gN=e.wasm.cwrap(xo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Zue(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=Rk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return gN(h,It[s.dtype],m,i,p,o,g,b),l}var Jue={kernelName:xo,backendName:\"wasm\",setupFunc:Que,kernelFunc:Zue},bN;function ele(e){bN=e.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function tle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let A=0;A<l.length;++A){let P=l[A];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,T=t.dataIdMap.get(g.dataId).id,N=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return bN(v,It[r.dtype],N,b,k,p.batchSize,E,T),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var nle={kernelName:vo,backendName:\"wasm\",setupFunc:ele,kernelFunc:tle},sle=!1,rle=gn(wo,sle,\"bool\"),ale=!1,ile=gn(Va,ale,\"bool\"),yN;function ole(e){yN=e.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\"])}function ule(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,\"float32\");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;yN(r,It[t.dtype],n,i)}return a}var lle={kernelName:Ua,backendName:\"wasm\",setupFunc:ole,kernelFunc:ule},cle=!1,dle=gn(ko,cle,\"bool\"),ple=!1,hle=gn(Io,ple,\"bool\"),fle=Xt(Ga),mle=!1,gle=gn(So,mle,\"bool\"),vN;function ble(e){vN=e.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function yle(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"max\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;vN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var vle={kernelName:Ha,backendName:\"wasm\",setupFunc:ble,kernelFunc:yle},xle=!1,wle=gn(qa,xle),xN;function kle(e){xN=e.wasm.cwrap(ja,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ile(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,T=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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x=t.dataIdMap.get(v.dataId).id;wN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!==\"float32\"&&t.disposeData(y.dataId),v}var Tle={kernelName:Ka,backendName:\"wasm\",setupFunc:Cle,kernelFunc:Nle},kN;function $le(e){kN=e.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\",\"number\"])}function _le(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims(\"min\",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;kN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ale={kernelName:Xa,backendName:\"wasm\",setupFunc:$le,kernelFunc:_le},Ele=!1,Rle=gn(Ya,Ele),IN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(IN||{}),SN;function Dle(e){SN=e.wasm.cwrap(Qa,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Fle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return SN(i,l,t.shape.length,It[t.dtype],d,h,IN[r],u),o}var Ole={kernelName:Qa,backendName:\"wasm\",kernelFunc:Fle,setupFunc:Dle},Ple=!0,zle=gn(Za,Ple),Mle=Xt(Co);function Fv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var CN;function Lle(e){CN=e.wasm.cwrap(To,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ble(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=CN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Fv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],\"int32\",d)}var Vle={kernelName:To,backendName:\"wasm\",setupFunc:Lle,kernelFunc:Ble},NN;function Wle(e){NN=e.wasm.cwrap(kl,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function Ule(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=NN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Fv(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var Gle={kernelName:kl,backendName:\"wasm\",setupFunc:Wle,kernelFunc:Ule},TN;function Hle(e){TN=e.wasm.cwrap($o,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function qle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=TN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Fv(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var jle={kernelName:$o,backendName:\"wasm\",setupFunc:Hle,kernelFunc:qle},Kle=!1,Xle=gn(No,Kle,\"bool\"),$N;function Yle(e){$N=e.wasm.cwrap(Ao,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function Qle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return $N(p,a,i,o,l),u}var Zle={kernelName:Ao,backendName:\"wasm\",setupFunc:Yle,kernelFunc:Qle};function Jle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var ece={kernelName:_o,backendName:\"wasm\",kernelFunc:Jle};function tce(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return eg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack 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AN={kernelName:Ja,backendName:\"wasm\",kernelFunc:rce,setupFunc:sce},ace=!1,ice=gn(ei,ace),EN;function oce(e){EN=e.wasm.cwrap(ti,null,[\"number\",\"number\",\"number\"])}function uce(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!==\"float32\"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return EN(o,i,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var lce={kernelName:ti,backendName:\"wasm\",setupFunc:oce,kernelFunc:uce},RN;function cce(e){RN=e.wasm.cwrap(ni,null,[\"number\",\"number\",\"number\",\"number\"])}function dce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t),f=p;if(h){let 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BroadcastTo,OW as Callback,QL as CallbackList,Ta as Cast,$a as Ceil,Cr as ClipByValue,Xd as Complex,Yd as ComplexAbs,co as Concat,_a as Conv2D,pg as Conv2DBackpropFilter,Aa as Conv2DBackpropInput,Qd as Conv3D,hg as Conv3DBackpropFilterV2,fg as Conv3DBackpropInputV2,Ea as Cos,Ra as Cosh,ho as CropAndResize,po as Cumprod,Da as Cumsum,eB as CustomCallback,Hd as DataStorage,mg as DenseBincount,fo as DepthToSpace,Fa as DepthwiseConv2dNative,gg as DepthwiseConv2dNativeBackpropFilter,bg as DepthwiseConv2dNativeBackpropInput,yg as Diag,Zd as Dilation2D,Zf as Dilation2DBackpropFilter,Qf as Dilation2DBackpropInput,sk as ENV,PW as EarlyStopping,Jd as Einsum,Pa as Elu,vg as EluGrad,A$ as Environment,mo as Equal,fl as Erf,za as Exp,go as ExpandDims,bo as Expm1,xg as FFT,ml as Fill,yo as FlipLeftRight,Ma as Floor,La as FloorDiv,fd as FromPixels,Ba as FusedBatchNorm,ia as FusedConv2D,oa as FusedDepthwiseConv2D,Xf as GPGPUContext,xo as GatherNd,vo as GatherV2,V4 as GraphModel,wo as Greater,Va as GreaterEqual,JL as History,wg as IFFT,Wa as Identity,ep as Imag,Dt as InputSpec,gl as IsFinite,bl as IsInf,yl as IsNan,sl as KernelBackend,np as LRN,Ig as LRNGrad,pz as LayerVariable,hr as LayersModel,Ua as LeakyRelu,ko as Less,Io as LessEqual,kg as LinSpace,Ga as Log,vl as Log1p,z$ as LogSoftmax,So as LogicalAnd,xl as LogicalNot,tp as LogicalOr,G0 as MathBackendCPU,U1 as MathBackendWebGL,Ha as Max,ja as MaxPool,sp as MaxPool3D,Cg as MaxPool3DGrad,Sg as MaxPoolGrad,Ng as MaxPoolWithArgmax,qa as Maximum,Ka as Mean,Xa as Min,Ya as Minimum,Qa as MirrorPad,wl as Mod,xb as MomentumOptimizer,Tg as Multinomial,Za as Multiply,Co as Neg,To as NonMaxSuppressionV3,kl as NonMaxSuppressionV4,$o as NonMaxSuppressionV5,No as NotEqual,m_ as OP_SCOPE_SUFFIX,Ao as OneHot,_o as OnesLike,Ar as Optimizer,Ur as OptimizerConstructors,Eo as Pack,Ja as PadV2,Wde as Pool,ei as Pow,ti as Prelu,ni as Prod,wb as RMSPropOptimizer,Er as RNN,Il as Range,i_ as Rank,rp as Real,Oa as RealDiv,Sl as Reciprocal,pO as Reduction,si as Relu,ai as Relu6,Ro as Reshape,ri as ResizeBilinear,_g as ResizeBilinearGrad,Cl as ResizeNearestNeighbor,$g as ResizeNearestNeighborGrad,Do as Reverse,jo as RotateWithOffset,Fo as Round,ii as Rsqrt,Cp as SGDOptimizer,Oo as ScatterNd,Po as Select,Nl as Selu,Hb as Sequential,ui as Sigmoid,Tl as Sign,oi as Sin,Mo as Sinh,zo as Slice,di as Softmax,$l as Softplus,Lo as SpaceToBatchND,ap as SparseFillEmptyRows,_l as SparseReshape,ip as SparseSegmentMean,op as SparseSegmentSum,up as SparseToDense,Bo as SplitV,li as Sqrt,Al as Square,pi as SquaredDifference,gi as Step,Vo as StridedSlice,lp as StringNGrams,Ag as StringSplit,Eg as StringToHashBucketFast,hi as Sub,ci as Sum,$s as SymbolicTensor,Wo as Tan,fi as Tanh,et as Tensor,Vt as TensorBuffer,Nr as Tile,Uo as TopK,Go as Transform,mi as Transpose,Rg as Unique,Ho as Unpack,cp as UnsortedSegmentSum,gd as Variable,qo as ZerosLike,aa as _FusedMatMul,Mt as abs,YA as acos,ZA as acosh,ie as add,eE as addN,Yk as all,hm as any,qu as argMax,aE as argMin,oE as asin,lE as asinh,dE as atan,hE as atan2,mE as atanh,qg as avgPool,eI as avgPool3d,UA as backend,S as backend_util,ipe as basicLSTMCell,Ku as batchNorm,FE as batchNorm2d,PE as batchNorm3d,ME as batchNorm4d,jg as batchToSpaceND,tI as bincount,Fpe as booleanMaskAsync,VE as broadcastArgs,nd as broadcastTo,Ko as broadcast_util,Ak as browser,De as buffer,Gpe as callbacks,ce as cast,GE as ceil,Vn as clipByValue,cr as clone,ua as complex,Ft as concat,jE as concat1d,XE as concat2d,QE as concat3d,JE as concat4d,$L as constraints,nI as conv1d,da as conv2d,sI as conv2dTranspose,rI as conv3d,oR as conv3dTranspose,Hde as copyRegisteredKernels,Xg as cos,iI as cosh,EI as cosineWindow,mm as cumprod,oI as cumsum,js as customGrad,W4 as data,hR as denseBincount,Kk as deprecationWarn,mR as depthToSpace,mp as depthwiseConv2d,qpe as deregisterOp,hp as device_util,ope as diag,vR as dilation2d,Xde as disableDeprecationWarnings,Re as dispose,Yde as disposeVariables,xe as div,SR as divNoNan,upe as dot,oF as dropout,TR as einsum,gp as elu,Kde as enableDebugMode,jde as enableProdMode,uF as enclosingPowerOfTwo,Ss as engine,X as env,Xn as equal,AR as erf,Yn as exp,Pn as expandDims,FR as expm1,uI as eye,db as fft,Pl as fill,spe as findBackend,rpe as findBackendFactory,bp as floor,Xk as floorDiv,e8 as forceHalfFloat,fa as fused,Xu as gather,rF as gatherND,Rk as gather_util,tpe as getBackend,rx as getGradient,Jf as getKernel,em as getKernelsForBackend,nhe as getThreadsCount,WK as gpgpu_util,dpe as grad,ppe as grads,Un as greater,Xo as greaterEqual,Id as ifft,Yg as imag,jn as image,Ppe as inTopKAsync,DL as initializers,HB as input,En as io,SI as irfft,lpe as isFinite,cpe as isInf,HR as isNaN,Ht as keep,xs as kernel_impls,XL as layers,Qg as leakyRelu,lI as less,Yo as lessEqual,qO as linalg,XR as linspace,jpe as loadGraphModel,Wpe as loadLayersModel,QR as localResponseNormalization,Qn as log,Zg as log1p,mpe as logSigmoid,cI as logSoftmax,dD as logSumExp,Ds as logicalAnd,tb as logicalNot,fI as logicalOr,gpe as logicalXor,Lpe as losses,We as matMul,pA as math,As as max,nb as maxPool,mI as maxPool3d,vD as maxPoolWithArgmax,_r as maximum,St as mean,pm as memory,bpe as meshgrid,fW as metrics,gm as min,vp as minimum,CD as mirrorPad,TD as mod,Bpe as model,AW as models,sb as moments,Ope as movingAverage,V as mul,ype as multiRNNCell,RD as multinomial,kt as neg,LI as nextFrame,_I as norm,Yu as notEqual,vd as oneHot,Mn as ones,Zn as onesLike,L as op,vpe as outerProduct,bi as pad,xpe as pad1d,wpe as pad2d,kpe as pad3d,Ipe as pad4d,Spe as pool,ha as pow,ab as prelu,X_ as print,gI as prod,Qde as profile,Cpe as rand,Npe as randomGamma,JD as randomNormal,Ml as randomUniform,Qu as range,epe as ready,wd as real,s3 as reciprocal,fp as registerBackend,Upe as registerCallbackConstructor,L$ as registerGradient,El as registerKernel,Hpe as registerOp,EW as regularizers,Ys as relu,bI as relu6,npe as removeBackend,G as reshape,Jn as reverse,Tpe as reverse1d,$pe as reverse2d,_pe as reverse3d,Ape as reverse4d,pb as rfft,yI as round,vI as rsqrt,we as scalar,eF as scatterND,Fk as scatter_util,xI as selu,m3 as separableConv2d,Vpe as sequential,ae as serialization,Jde as setBackend,ape as setPlatform,the as setThreadsCount,Jpe as setWasmPath,ehe as setWasmPaths,F5 as setWebGLContext,b3 as setdiff1dAsync,ev as shared,qs as sigmoid,v3 as sign,Mpe as signal,wI as sin,kI as sinh,qe as slice,ub as slice1d,II as slice2d,lb as slice3d,kd as slice4d,wt as slice_util,cb as softmax,zl as softplus,rb as spaceToBatchND,Wc as sparse,AI as sparseToDense,zpe as spectral,Bn as split,dn as sqrt,ct as square,CI as squaredDifference,gr as squeeze,es as stack,xp as step,M3 as stridedSlice,Bf as string,ge as sub,ve as sum,pp as sumOutType,B3 as tan,ju as tanh,fs as tensor,Zt as tensor1d,Yi as tensor2d,yA as tensor3d,Epe as tensor4d,Rpe as tensor5d,Dpe as tensor6d,_s as tensor_util,OA as test_util,j as tidy,ps as tile,Zde as time,W3 as topk,Mi as train,Ge as transpose,hb as truncatedNormal,mx as unique,Gde as unregisterGradient,Ude as unregisterKernel,q3 as unsortedSegmentSum,Fs as unstack,cn as upcastType,w as util,hpe as valueAndGrad,fpe as valueAndGrads,K3 as variable,eD as variableGrads,rhe as version,Kpe as version_converter,qde as version_core,Xpe as version_cpu,hS as version_layers,she as version_wasm,Ype as version_webgl,Qpe as webgl,D5 as webgl_util,yoe as webgpu,vn as where,TI as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        env.webgl.supported = false;\n        config.filter.enabled = false;\n        copy(inCanvas, outCanvas); // filter failed to initialize\n        // return { tensor: null, canvas: inCanvas };\n      } else {\n        fx.reset();\n        if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n        if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n        if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n        if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n        if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n        if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n        if (config.filter.negative) fx.add('negative');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.vintage) fx.add('brownie');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.kodachrome) fx.add('kodachrome');\n        if (config.filter.technicolor) fx.add('technicolor');\n        if (config.filter.polaroid) fx.add('polaroid');\n        if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n        if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n        else outCanvas = fx.draw(inCanvas);\n      }\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  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[0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "import type { Tensor } from '../tfjs/types';\n\nconst attentionDefinitions = {\n  eyeLLower: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  eyeRLower: [263, 249, 390, 373, 374, 380, 381, 382, 362], // 9\n  // eslint-disable-next-line max-len\n  lips: [61, 76, 91, 181, 84, 17, 314, 405, 321, 291, 291, 185, 40, 39, 37, 0, 267, 269, 270, 291, 62, 183, 88, 178, 87, 14, 268, 303, 304, 408, 291, 184, 42, 178, 87, 14, 268, 303, 304, 408, 61, 62, 90, 180, 85, 16, 315, 404, 307, 308, 291, 185, 40, 73, 72, 0, 302, 269, 270, 409, 61, 184, 95, 179, 86, 15, 316, 403, 324, 408, 291, 184, 74, 41, 38, 11, 268, 303, 304, 408],\n  // eslint-disable-next-line max-len\n  eyeL: [33, 7, 163, 144, 145, 153, 154, 155, 133, 246, 161, 160, 159, 158, 157, 173, 130, 25, 110, 24, 23, 22, 26, 112, 243, 247, 30, 29, 27, 28, 56, 190, 226, 31, 228, 229, 230, 231, 232, 233, 244, 113, 225, 224, 223, 222, 221, 189, 35, 124, 46, 53, 52, 65, 143, 111, 117, 118, 119, 120, 121, 128, 245, 156, 70, 63, 105, 66, 107, 55, 193],\n  // eslint-disable-next-line max-len\n  eyeR: [263, 249, 390, 373, 374, 380, 381, 382, 362, 466, 388, 387, 386, 385, 384, 398, 359, 255, 339, 254, 253, 252, 256, 341, 463, 467, 260, 259, 257, 258, 286, 414, 446, 261, 448, 449, 450, 451, 452, 453, 464, 342, 445, 444, 443, 442, 441, 413, 265, 353, 276, 283, 282, 295, 372, 340, 346, 347, 348, 349, 350, 357, 465, 383, 300, 293, 334, 296, 336, 285, 417],\n};\n\n/*\n// function used to determine heuristic mapping\n// values in attentionDefinitions are based on top values from 200 iterations\nfunction replaceClosestPoint(rawCoords, newCoords) {\n  const res: number[] = [];\n  for (let i = 0; i < newCoords.length / 2; i++) {\n    let minDist = Number.MAX_VALUE;\n    let minDistIdx = -1;\n    const pts = rawCoords.map((r) => [r[0], r[1]]);\n    for (let j = 0; j < rawCoords.length; j++) {\n      const x = pts[j][0] - newCoords[2 * i + 0];\n      const y = pts[j][1] - newCoords[2 * i + 1];\n      const dist = (x * x) + (y * y);\n      if (dist < minDist) {\n        minDist = dist;\n        minDistIdx = j;\n      }\n    }\n    res.push(minDistIdx);\n    rawCoords[minDistIdx] = [newCoords[2 * i + 0], newCoords[2 * i + 1], rawCoords[minDistIdx][2]];\n  }\n  return rawCoords;\n}\n*/\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    irisL: results[3].dataSync() as Float32Array, // 5 x 2d // output_left_iris\n    irisR: results[1].dataSync() as Float32Array, // 5 x 2d // output_right_iris\n    eyeL: results[0].dataSync() as Float32Array, // 71 x 2d // output_left_eye\n    eyeR: results[6].dataSync() as Float32Array, // 71 x 2d // output_right_eye\n    lips: results[5].dataSync() as Float32Array, // 80 x 2d // output_lips\n    // flag: results[4], // already processed in parent // conv_faceflag\n    // mesh: results[2], // already have it in rawCoords // output_mesh_identity\n  };\n\n  // rawCoords = replaceClosestPoint(rawCoords, t.eyeL);\n  // rawCoords = replaceClosestPoint(rawCoords, t.eyeR);\n  // rawCoords = replaceClosestPoint(rawCoords, t.lips);\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisRDepth = attentionDefinitions.eyeRLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeRLower.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n  const irisLDepth = attentionDefinitions.eyeLLower.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / attentionDefinitions.eyeLLower.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[attentionDefinitions.eyeL[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[attentionDefinitions.eyeL[i]][2]];\n  // for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords.push([t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], 0]);\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[attentionDefinitions.eyeR[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[attentionDefinitions.eyeR[i]][2]];\n  // for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords.push([t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], 0]);\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  // for (let i = 0; i < t.lips.length / 2; i++) rawCoords[attentionDefinitions.lips[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[attentionDefinitions.lips[i]][2]];\n  // for (let i = 0; i < t.lips.length / 2; i++) rawCoords.push([t.lips[2 * i + 0], t.lips[2 * i + 1], 0]);\n\n  return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidence = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const contourCoords = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // run iris model and augment results\n        }\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n      tf.dispose([...results, coordsReshaped]);\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention?.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, 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0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should face attention keypoints be highlighted */\n  drawAttention: boolean;\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport { options, DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\nexport const colorDepth = (z: number, rgb: [boolean, boolean, boolean] = [true, true, false]): string => {\n  const r = rgb[0] ? 127 + Math.trunc(3 * z) : 255;\n  const g = rgb[1] ? 127 - Math.trunc(3 * z) : 255;\n  const b = rgb[2] ? 127 - Math.trunc(3 * z) : 255;\n  return `rgba(${r}, ${g}, ${b}, ${options.alpha})`;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? colorDepth(z, z === -255 ? [true, false, true] : [true, false, false]) : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? colorDepth(z) : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? colorDepth(z) : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        const length = Math.max(468, f.mesh.length);\n        for (let i = 0; i < length; i++) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n      }\n      if (localOptions.drawAttention && f.mesh.length > 468) {\n        for (let i = 468; i < f.mesh.length; i++) point(ctx, f.mesh[i][0], f.mesh[i][1], -255, localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? colorDepth(result[i].keypoints[pt].position[2] || 0) : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? colorDepth(pt.position[2]) : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? colorDepth(pt[2] || 0) : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? colorDepth(z) : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? colorDepth(i * z) : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor, GraphModel } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n    log('compile pass: skip');\n    return;\n  }\n  const models = Object.values(allModels).filter((m) => m !== null) as GraphModel[];\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  for (const model of models) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    const res = await model.executeAsync(tensor);\n    if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n    else tf.dispose(res);\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass kernels:', kernels.length);\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    // await runCompile(instance.models);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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 }
diff --git a/dist/human.js b/dist/human.js
index a928f6ad..af51a183 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -4,63 +4,63 @@
   author: <https://github.com/vladmandic>'
 */
 
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n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,Is(`Initialization of backend ${e} failed`),Is(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return Is(`Initialization of backend ${e} failed`),Is(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new 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b;let{dataId:v,shape:S,dtype:T}=b;return this.makeTensorFromDataId(v,S,T)});if(n){let b=this.getTensorsForGradient(c,f,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,f=m=>{!n||(r=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:d}=e,h=q1(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=oy(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let 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r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*iy(e.dtype)),this.state.numBytes+=r,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r})),e instanceof Op||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let r=e.size*iy(e.dtype);this.state.numBytes-=r}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(a instanceof rt,()=>"The result y returned by f() must be a tensor.");let s=jR(this.state.activeTape,t,a);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[a.id]=r==null?nM(a.shape):r,HR(i,s,l=>this.tidy(l),aM);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return P(Rs(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{P(t.every(i=>i instanceof rt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let r,n={};t.forEach((i,o)=>{n[o]=i});let a=(i,o)=>(r=e(...t,o),P(r.value instanceof rt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),P(Rs(r.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),r.value),s=(i,o)=>{let l=r.gradFunc(i,o),u=Array.isArray(l)?l:[l];P(u.length===t.length,()=>"The function f passed in customGrad(f) must 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Buffer shape=${this.shape}`;throw new Error(a)}t++}let r=e[e.length-1];for(let n=0;n<e.length-1;++n)r+=this.strides[n]*e[n];return this.values[r]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let r=0;r<e.length-1;++r)t+=this.strides[r]*e[r];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let r=0;r<t.length-1;++r)t[r]=Math.floor(e/this.strides[r]),e-=t[r]*this.strides[r];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return ia().makeTensor(this.values,this.shape,this.dtype)}},ia=null,pu=null,eM=null;function tM(e){ia=e}function rM(e){pu=e}function nM(e){eM=e}var nt=class{constructor(e,t,r,n){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Tt(e),this.strides=Pu(e),this.dataId=r,this.id=n,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return pu.buffer(this.shape,this.dtype,e)}bufferSync(){return pu.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return fu(this.shape,e,this.dtype==="complex64")}arraySync(){return fu(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=ia().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(r=>Af(r))}catch(r){throw new Error("Failed to decode the string bytes into utf-8. 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To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await ia().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(ia().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return pu.print(this,e)}clone(){return this.throwIfDisposed(),pu.clone(this)}toString(e=!1){let t=this.dataSync();return JR(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),pu.cast(this,e)}variable(e=!0,t,r){return this.throwIfDisposed(),ia().makeVariable(this,e,t,r)}};Object.defineProperty(nt,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function aM(){return c2("Tensor",()=>nt)}aM();var Op=class extends nt{constructor(e,t,r,n){super(e.shape,e.dtype,e.dataId,n),this.trainable=t,this.name=r}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!qs(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);ia().disposeTensor(this),this.dataId=e.dataId,ia().incRef(this,null)}dispose(){ia().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Op,Symbol.hasInstance,{value:e=>e instanceof nt&&e.assign!=null&&e.assign instanceof Function});var da={};Le(da,{assertTypesMatch:()=>Vw,getTensorsInContainer:()=>m2,isTensorInList:()=>iM,makeTypesMatch:()=>Ot});var Ow=(e=>(e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6",e))(Ow||{}),Dw=(e=>(e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64",e))(Dw||{}),Lw=(e=>(e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64",e))(Lw||{}),Bw=(e=>(e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64",e))(Bw||{}),Ww=(e=>(e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64",e))(Ww||{}),sM={float32:Bw,int32:Dw,bool:Lw,complex64:Ww};function Cr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return sM[e][t]}function ch(e){return Cr(e,"int32")}function Ot(e,t){if(e.dtype===t.dtype)return[e,t];let r=Cr(e.dtype,t.dtype);return[e.cast(r),t.cast(r)]}function Vw(e,t){P(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function iM(e,t){return t.some(r=>r.id===e.id)}function m2(e){let t=[];return Uw(e,t,new Set),t}function Uw(e,t,r){if(e==null)return;if(e instanceof nt){t.push(e);return}if(!oM(e))return;let n=e;for(let a in n){let s=n[a];r.has(s)||(r.add(s),Uw(s,t,r))}}function oM(e){return Array.isArray(e)||typeof e=="object"}function Z1(e){return e.kernelName!=null}var _3=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},py=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new _3}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t];if(await this.initializeBackend(r).success){await this.setBackend(r);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. 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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:r},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:r}=this.initializeBackend(e);if(!(r?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new qR(this.backendInstance),!0}setupRegisteredKernels(){Ra(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Ra(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let r=t.factory();if(r&&!(r instanceof $u)&&typeof r.then=="function"){let n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,Ss(`Initialization of backend ${e} failed`),Ss(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return Ss(`Initialization of backend ${e} failed`),Ss(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t],{success:n,asyncInit:a}=this.initializeBackend(r);if(a||n)return{name:r,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let r=this.state.tensorInfo.get(t),n=r.backend,a=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),r.backend=e,e.move(t,a,r.shape,r.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new 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n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=Z1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Z1(e)){let{kernelName:c,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=yf(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:S,dtype:T}=b;return this.makeTensorFromDataId(v,S,T)});if(n){let b=this.getTensorsForGradient(c,f,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,f=m=>{!n||(r=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:d}=e,h=Z1(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=uy(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&Ts(e[0])&&(a=e.map(o=>hh(o)));let s=n.write(a,t,r),i=new nt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=Sw(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a=new nt(t,r,e,this.nextTensorId());return this.trackTensor(a,n),a}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new Op(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*ly(e.dtype)),this.state.numBytes+=r,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:r})),e instanceof Op||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let r=e.size*ly(e.dtype);this.state.numBytes-=r}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,r=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,r,n,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:r,saved:a},o=uy(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=r[d],p=qf(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),n(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=m2(e),r=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(a instanceof nt,()=>"The result y returned by f() must be a tensor.");let s=ZR(this.state.activeTape,t,a);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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+          ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(ih,i)}var AD=W({sparseSegmentMean_:yD});function xD(e,t,r){let n=F(e,"data","sparseSegmentSum"),a=F(t,"indices","sparseSegmentSum","int32"),s=F(r,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${a.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
-         ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(oh,i)}var mD=W({sparseSegmentSum_:fD});function gD(e,t,r,n,a,s,i,o){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=F(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let d={separator:r,nGramWidths:n,leftPad:a,rightPad:s,padWidth:i,preserveShortSequences:o},h={data:l,dataSplits:u},p=B.runKernel(uh,h,d);return{nGrams:p[0],nGramsSplits:p[1]}}var yD=W({stringNGrams_:gD});function AD(e,t,r=!0){let n=F(e,"input","stringSplit","string"),a=F(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(a.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${a.shape}`);let s={skipEmpty:r},i={input:n,delimiter:a},o=B.runKernel(cm,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var xD=W({stringSplit_:AD});function bD(e,t){let r=F(e,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let a={input:r};return B.runKernel(fm,a,n)}var vD=W({stringToHashBucketFast_:bD}),wD={fft:Mm,ifft:Wp,rfft:Fm,irfft:aA},kD={hammingWindow:Yz,hannWindow:T7,frame:N7,stft:tO},Ie={flipLeftRight:sO,grayscaleToRGB:oO,resizeNearestNeighbor:MO,resizeBilinear:EO,rotateWithOffset:uO,cropAndResize:nO,nonMaxSuppression:pO,nonMaxSuppressionAsync:xO,nonMaxSuppressionWithScore:vO,nonMaxSuppressionWithScoreAsync:kO,nonMaxSuppressionPadded:SO,nonMaxSuppressionPaddedAsync:NO,threshold:PO,transform:zO},F7={bandPart:DO,gramSchmidt:BO,qr:VO},ID={absoluteDifference:jO,computeWeightedLoss:ts,cosineDistance:qO,hingeLoss:XO,huberLoss:YO,logLoss:QO,meanSquaredError:tD,sigmoidCrossEntropy:aD,softmaxCrossEntropy:oD},bp={sparseFillEmptyRows:uD,sparseReshape:pD,sparseSegmentMean:cD,sparseSegmentSum:mD},rf={stringNGrams:yD,stringSplit:xD,stringToHashBucketFast:vD},rs=class extends Ak{minimize(e,t=!1,r){let{value:n,grads:a}=this.computeGradients(e,r);if(r!=null){let s=r.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return re(a),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return e7(e,t)}dispose(){this.iterations_!=null&&re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(rs,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Dm=class extends rs{constructor(e,t,r=null){super(),this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:K(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;K(()=>{let l=le(L(i,this.rho),L(At(s),1-this.rho)),u=L(pe(Er(le(o,this.epsilon)),Er(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(At(u),1-this.rho));i.assign(l),o.assign(d);let h=le(L(u,-this.learningRate),n);n.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(re(this.accumulatedGrads.map(e=>e.variable)),re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,r=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Dm.className="Adadelta";Bi(Dm);var Lm=class extends rs{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:K(()=>dd(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;K(()=>{let i=le(s,At(a));s.assign(i);let o=le(L(pe(a,Er(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Lm.className="Adagrad";Bi(Lm);var Bm=class extends rs{constructor(e,t,r,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=ce(1,this.accBeta1),n=ce(1,this.accBeta2);t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:K(()=>at(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:K(()=>at(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=le(L(d,this.beta2),L(At(l),1-this.beta2)),c=pe(h,r),f=pe(p,n);u.assign(h),d.assign(p);let m=le(L(pe(c,le(Er(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign(Ds(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ds(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Bm.className="Adam";Bi(Bm);var Wm=class extends rs{constructor(e,t,r,n=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=ce(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:at(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:at(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=L(d,this.beta2),c=rr(l),f=es(p,c);u.assign(h),d.assign(f);let m=le(L(pe(n,r),pe(h,le(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(le(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Wm.className="Adamax";Bi(Wm);var kh=class extends rs{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=cr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};kh.className="SGD";Bi(kh);var Vm=class extends kh{constructor(e,t,r=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=r,this.accumulations=[],this.m=Se(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulations[r]==null&&(this.accumulations[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(!1))});let a=this.accumulations[r].variable,s=Array.isArray(e)?e[r].tensor:e[t];s!=null&&K(()=>{let i,o=le(L(this.m,a),s);this.useNesterov?i=le(L(this.c,le(s,L(o,this.m))),n):i=le(L(this.c,o),n),a.assign(o),n.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Vm.className="Momentum";Bi(Vm);var Um=class extends rs{constructor(e,t=.9,r=0,n=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,n==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:K(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;K(()=>{let l=le(L(i,this.decay),L(At(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Er(ce(l,le(At(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=ce(n,p);n.assign(c)}else{let u=le(L(i,this.decay),L(At(s),1-this.decay)),d=le(L(o,this.momentum),pe(L(s,this.learningRate),Er(le(u,this.epsilon))));i.assign(u),o.assign(d);let h=ce(n,d);n.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Um.className="RMSProp";Bi(Um);var ws=class{static sgd(e){return new kh(e)}static momentum(e,t,r=!1){return new Vm(e,t,r)}static rmsprop(e,t=.9,r=0,n=null,a=!1){return new Um(e,t,r,n,a)}static adam(e=.001,t=.9,r=.999,n=null){return new Bm(e,t,r,n)}static adadelta(e=.001,t=.95,r=null){return new Dm(e,t,r)}static adamax(e=.002,t=.9,r=.999,n=null,a=0){return new Wm(e,t,r,n,a)}static adagrad(e,t=.1){return new Lm(e,t)}},co={sgd:ws.sgd,momentum:ws.momentum,adadelta:ws.adadelta,adagrad:ws.adagrad,rmsprop:ws.rmsprop,adamax:ws.adamax,adam:ws.adam},SD=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function hA(){return new Promise(e=>SD(()=>e()))}var 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ns{constructor(e,t,r=null){super(),this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:K(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;K(()=>{let l=le(L(i,this.rho),L(At(s),1-this.rho)),u=L(pe(Er(le(o,this.epsilon)),Er(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(At(u),1-this.rho));i.assign(l),o.assign(d);let 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ns{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:K(()=>dd(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;K(()=>{let i=le(s,At(a));s.assign(i);let o=le(L(pe(a,Er(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Vm.className="Adagrad";Ui(Vm);var Um=class extends ns{constructor(e,t,r,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=ce(1,this.accBeta1),n=ce(1,this.accBeta2);t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:K(()=>at(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:K(()=>at(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=le(L(d,this.beta2),L(At(l),1-this.beta2)),c=pe(h,r),f=pe(p,n);u.assign(h),d.assign(p);let m=le(L(pe(c,le(Er(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign(Ls(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ls(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Um.className="Adam";Ui(Um);var Gm=class extends ns{constructor(e,t,r,n=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=ce(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:at(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:at(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=L(d,this.beta2),c=rr(l),f=ts(p,c);u.assign(h),d.assign(f);let m=le(L(pe(n,r),pe(h,le(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(le(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Gm.className="Adamax";Ui(Gm);var kh=class extends ns{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=cr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};kh.className="SGD";Ui(kh);var jm=class extends kh{constructor(e,t,r=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=r,this.accumulations=[],this.m=Se(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulations[r]==null&&(this.accumulations[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(!1))});let a=this.accumulations[r].variable,s=Array.isArray(e)?e[r].tensor:e[t];s!=null&&K(()=>{let i,o=le(L(this.m,a),s);this.useNesterov?i=le(L(this.c,le(s,L(o,this.m))),n):i=le(L(this.c,o),n),a.assign(o),n.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};jm.className="Momentum";Ui(jm);var Hm=class extends ns{constructor(e,t=.9,r=0,n=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,n==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:K(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;K(()=>{let l=le(L(i,this.decay),L(At(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Er(ce(l,le(At(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=ce(n,p);n.assign(c)}else{let u=le(L(i,this.decay),L(At(s),1-this.decay)),d=le(L(o,this.momentum),pe(L(s,this.learningRate),Er(le(u,this.epsilon))));i.assign(u),o.assign(d);let h=ce(n,d);n.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Hm.className="RMSProp";Ui(Hm);var ks=class{static sgd(e){return new kh(e)}static momentum(e,t,r=!1){return new jm(e,t,r)}static rmsprop(e,t=.9,r=0,n=null,a=!1){return new Hm(e,t,r,n,a)}static adam(e=.001,t=.9,r=.999,n=null){return new Um(e,t,r,n)}static adadelta(e=.001,t=.95,r=null){return new Wm(e,t,r)}static adamax(e=.002,t=.9,r=.999,n=null,a=0){return new Gm(e,t,r,n,a)}static 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Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new la(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Sf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return Sy(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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R=0;R<E.length;++R){c.hasKey(E[R])||c.add(E[R],v[R],Array.isArray(S)?S[0]:S);let _=o.indexOf(E[R].name);_!==-1&&(l[_]=v[R])}a||re(x)}return c.disposeMasks(),s?l:l[0]}function UV(e,t){w.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let r=[],n={};if(e.length===1){let a=ov(e[0],t);r=a.sorted,n=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=ov(s,t);for(let l of i)a.has(l.name)||(r.push(l),a.add(l.name));for(let l in o)n[l]==null&&(n[l]=new Set),o[l].forEach(u=>n[l].add(u))}}return{sorted:r,recipientCounts:GV(n)}}function GV(e){let t={};for(let r in e)t[r]=e[r].size;return t}function ov(e,t){let r=new Set,n=[],a={};for(let o of t.names())r.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(r.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),n.push(o),r.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)a[u.name]==null&&(a[u.name]=new Set),a[u.name].add(o.name),!r.has(u.name)&&s.push(u)}}return{sorted:n,recipientMap:a}}function jV(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let r=null;for(let n=0;n<e.sourceLayer.inboundNodes.length;++n)for(let a of e.sourceLayer.inboundNodes[n].outputTensors)if(a.id===e.id){r=n;break}t=e.sourceLayer.getOutputAt(r)}return t}var Sa=class extends st{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Qm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Cs(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Cs(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;Ta(x===0,"input layer has >1 nodes"),Ta(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof gd))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},r={},n={},a={},s={},i=[],o=(y,A,x,b,v,S)=>{(b==null||v==null||S==null)&&(b=y.sourceLayer,v=y.nodeIndex,S=y.tensorIndex);let T=b.inboundNodes[v];if(x.indexOf(T)!==-1)throw new la(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(T)!==-1)return;this.containerNodes.add(Sa.nodeKey(b,v)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(T)===-1&&x.push(T);let E=T.inboundLayers.length;for(let R=0;R<E;R++){let _=T.inputTensors[R],M=T.inboundLayers[R],I=T.nodeIndices[R],z=T.tensorIndices[R];o(_,A,x,M,I,z)}for(A.push(T);x.indexOf(T)>=0;)x.splice(x.indexOf(T),1);i.push(T)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let d=i.slice().reverse();for(let y of d){r[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=n[y.outboundLayer.id]==null?0:n[y.outboundLayer.id];A=Math.max(A,x),n[y.outboundLayer.id]=A,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let v=y.inboundLayers[b],S=y.nodeIndices[b],T=v.inboundNodes[S],E=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(A+1,E),r[T.id]=T}}let h={};for(let y in t){let A=t[y];A in h||(h[A]=[]),h[A].push(r[y])}let p={};for(let y in n){let A=n[y];A in p||(p[A]=[]),p[A].push(a[y])}let c=Object.keys(p).map(y=>parseInt(y,10)).sort(Gc);this.layers=[];for(let y of c){let A=p[y];A.sort((x,b)=>{let v=s[x.id],S=s[b.id];return v<S?-1:v>S?1:0});for(let x of A)x instanceof Sa&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,c=Object.keys(h).map(y=>parseInt(y,10)).sort(Gc);let f=this.inputs.slice(),m=[];for(let y of c)for(let A of h[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new la(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new la(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new e0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}EA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${_A}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=Ny(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=It(e);let r=new bo;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return vp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=It(e);let r;return t==null?r=Mo(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=If(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Gc);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(Qr(d)),p=If(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ta(o in r),a.push(r[o])}return Qr(a)}runInternalGraph(e,t){t==null&&(t=Mo(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Gc);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(d.call(x,f)),A=It(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(d.call(m,f)),A=It(d.computeMask(m,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=y[x],S=A[x];r[b.id]=[v,S]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ta(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof Sa?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=Sa.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=Sa.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=Sa.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=Sa.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Sa.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Sa.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],S=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let T=a[b];if(T.inboundNodes.length<=v){i(m,g);return}let E=T.inboundNodes[v];y.push(E.outputTensors[S])}y.length>0&&m.apply(Qr(y),A)}function l(m){let g=m.name,y=ha(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!wW(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ta(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ta(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function HV(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function h4(e,t){return HV(e,t,"classWeight")}async function c4(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Br(e);if(e.shape.length===2){if(e.shape[1]>1)return Cn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),St(i,"float32")}else return null}function qV(e,t){return L(e,t)}var KV=32;function f4(e,t){let r,n,a=t;r=a.xs,n=a.ys,w.assert(r!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=lv("input",e.inputNames,r),i=lv("output",e.outputNames,n),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function lv(e,t,r){if(r instanceof rt)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function XV(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function ZV(e,t,r){let n=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. 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if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=yo(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Tr.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Tr.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:iU,generatedBy:`TensorFlow.js tfjs-layers v${_A}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Tr.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Tr.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(sv(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){sv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Xa.className="Model";ue.registerClass(Xa);var g4=class extends Xa{};g4.className="Functional";ue.registerClass(g4);async function oU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let n=Up(r),a=ha(n,t);if(e.weightsManifest!=null){let s=await Tr.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),re(s)}return a}async function lU(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Tr.getLoadHandlers(e,t);if(r.length===0)r.push(Tr.browserHTTPRequest(e,t));else if(r.length>1)throw new q(`Found more than one (${r.length}) 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compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new la("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,r={}){if(!this.built)throw new la("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new la("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof My))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=ha(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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t={};return t.className="linear",t.config={},ty(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},ty(t)}else return e instanceof on?e:ty(e)}function DA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var M4=class extends ue.Serializable{},Eh=class extends M4{constructor(e){super(),DA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Wt([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,rr(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,Nh(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Eh.className="L1L2";ue.registerClass(Eh);function mU(e){return DA(e),new Eh({l1:e!=null?e.l1:null,l2:0})}function gU(e){return DA(e),new Eh({l2:e!=null?e.l2:null,l1:0})}var cv={l1l2:"L1L2"};function xt(e){return fA(e)}function fv(e,t={}){return Ih(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in cv?cv[e]:e,config:{}};return fv(t)}else return e instanceof M4?e:fv(e)}var LA=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=je(e);let r=_a(e);return this.maxValue!=null&&(r=cn(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};LA.className="ReLU";ue.registerClass(LA);var BA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return wm(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};BA.className="LeakyReLU";ue.registerClass(BA);var WA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=ir(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new Kt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=je(e),Em(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:sr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};WA.className="PReLU";ue.registerClass(WA);var VA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return xh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};VA.className="ELU";ue.registerClass(VA);var UA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=je(e);return L(r,me(fn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};UA.className="ThresholdedReLU";ue.registerClass(UA);var GA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new OA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=je(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};GA.className="Softmax";ue.registerClass(GA);function gu(e,t,r){if(typeof e=="number")return Mo(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!zW(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ca(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Na(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+Bs([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function jA(e,t){return K(()=>(Gt(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function F4(e,t){return K(()=>(Gt(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function yU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=ma()),Gt(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=_2(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=xa(o,r)),o})}function mv(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=ma()),Gt(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=jA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ls.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function AU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=ma()),Gt(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=F4(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=D2(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=xa(o,r)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var HA=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",HA.verifyArgs(t),this.rank=e,fr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gu(t.kernelSize,e,"kernelSize"),this.strides=gu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,_n(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Vs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ir(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=gu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ta("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!mA(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Ws(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:sr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Rh=class extends HA{constructor(e,t){super(e,t),this.kernel=null,Rh.verifyArgs(t),this.filters=t.filters,fr(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ir(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return K(()=>{e=je(e);let r,n=this.bias==null?null:this.bias.read(),a=W7(this.activation.getClassName());if(a!=null&&this.rank===2)r=mv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=yU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=mv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=AU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=ft(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ca(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:sr(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},$4=class extends Rh{constructor(e){super(2,e),$4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!mA(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},a0=$4;a0.className="Conv2D";ue.registerClass(a0);var P4=class extends Rh{constructor(e){super(3,e),P4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},s0=P4;s0.className="Conv3D";ue.registerClass(s0);var qA=class extends a0{constructor(e){if(super(e),this.inputSpec=[new Kt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Na(o,h,u,this.padding),f=Na(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,1]));let g=O2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=nt(g,[0,3,1,2])),this.bias!=null&&(g=xa(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Na(t[n],o,s,this.padding),t[a]=Na(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};qA.className="Conv2DTranspose";ue.registerClass(qA);var KA=class extends s0{constructor(e){if(super(e),this.inputSpec=[new Kt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Na(l,f,h,this.padding),A=Na(u,m,p,this.padding),x=Na(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,4,1]));let v=Vk(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=nt(v,[0,4,1,2,3])),this.bias!==null&&(v=xa(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Na(t[n],u,i,this.padding),t[a]=Na(t[a],d,o,this.padding),t[s]=Na(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};KA.className="Conv3DTranspose";ue.registerClass(KA);var _4=class extends Rh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=ir(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=ir(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Kt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=je(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),r=d7(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=xa(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=nt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=sr(this.depthwiseConstraint),e.pointwiseConstraint=sr(this.pointwiseConstraint),e}};_4.className="SeparableConv";var XA=class extends _4{constructor(e){super(2,e)}};XA.className="SeparableConv2D";ue.registerClass(XA);var z4=class extends Rh{constructor(e){super(1,e),z4.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!mA(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ZA=z4;ZA.className="Conv1D";ue.registerClass(ZA);var YA=class extends st{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=je(e),this.dataFormat==="channelsLast"){let r=jc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return jc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=jc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return jc(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};YA.className="Cropping2D";ue.registerClass(YA);var JA=class extends st{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,$W(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=je(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=nt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};JA.className="UpSampling2D";ue.registerClass(JA);function xU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=ma()),Gt(a);let i=jA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ah(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var QA=class extends HA{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ir(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=je(e);let r=xU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=xa(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ca(t,this.kernelSize[0],this.padding,this.strides[0]),s=ca(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=sr(this.depthwiseRegularizer),e}};QA.className="DepthwiseConv2D";ue.registerClass(QA);function O4(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function D4(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ga(2,l));if(t=nt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=qt(a,-1)),a=nt(a,u)),n&&(t=$n(t,0),a!=null&&(a=$n(a,0)));let d=[],h,p=r,c=t.shape[0],f=tn(t),m;a!=null&&(m=tn(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let v=m[y],S=ce(Fn(v),v),T=le(L(x[0],v),L(p[0],S)),E=p.map((R,_)=>le(L(x[1][_],v),L(R,S)));return{output:T,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=or(d,1)),[h,g,p]})}var L4=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new l0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Kt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ga(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Iy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");Iy(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Kt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new Kt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_=[Wt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_[0]=Wt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>cr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=O4(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new Kt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof ua){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=je(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=D4((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=Wt(e.shape);return t=ke(t,[1,2]),t=Th(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?wy(t,[1,r]):t):this.cell.stateSize>1?[wy(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===L4.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=ha(n,r);return new e(Object.assign(t,{cell:a}))}},ns=L4;ns.className="RNN";ue.registerClass(ns);var Mh=class extends st{},i0=class extends Mh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,fr(this.units,"units"),this.activation=Vs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=Su([1,Bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Su([1,Bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Us({ones:()=>Fn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Us({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ea(L(e,s),this.kernel.read()):a=Ea(e,this.kernel.read()),this.bias!=null&&(a=xa(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Ea(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ws(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),recurrentConstraint:sr(this.recurrentConstraint),biasConstraint:sr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};i0.className="SimpleRNNCell";ue.registerClass(i0);var ex=class extends ns{constructor(e){e.cell=new i0(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};ex.className="SimpleRNN";ue.registerClass(ex);var o0=class extends Mh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,fr(this.units,"units"),this.activation=Vs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=Su([1,Bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Su([1,Bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Us({ones:()=>Fn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Us({ones:()=>Fn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Ea(e,this.kernel.read());this.useBias&&(u=xa(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Xt(d,[2*this.units,this.units],d.rank-1),c=Ea(n,h),[f,m,g]=Xt(u,3,u.rank-1),[y,A]=Xt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Ea(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ws(this.activation),recurrentActivation:Ws(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),recurrentConstraint:sr(this.recurrentConstraint),biasConstraint:sr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};o0.className="GRUCell";ue.registerClass(o0);var tx=class extends ns{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new o0(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};tx.className="GRU";ue.registerClass(tx);var Fh=class extends Mh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,fr(this.units,"units"),this.activation=Vs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Vs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=Su([1,Bs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Su([1,Bs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Kn{apply(i,o){let l=a.apply([s]),u=new Hm().apply([s]),d=a.apply([s*2]);return ev(ev(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Us({ones:()=>Fn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Us({ones:()=>Fn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Ea(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Ea(n,this.recurrentKernel.read())),this.useBias&&(h=xa(h,this.bias.read()));let[p,c,f,m]=Xt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Ws(this.activation),recurrentActivation:Ws(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),recurrentConstraint:sr(this.recurrentConstraint),biasConstraint:sr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Fh.className="LSTMCell";ue.registerClass(Fh);var rx=class extends ns{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Fh(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};rx.className="LSTM";ue.registerClass(rx);var l0=class extends Mh{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){Iy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{ko(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(ha(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Sy(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}EA(t)}};l0.className="StackedRNNCells";ue.registerClass(l0);function Us(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):K7(t(),r),o=()=>Ch(i,t,n);return!a||a<=1?cr(o().clone()):Array(a).fill(void 0).map(o).map(l=>cr(l.clone()))}var B4=class extends ns{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Kt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Wt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_=[Wt(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_[0]=Wt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!w.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>cr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ca(l,n[0],a,s[0],i[0]),h=ca(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};B4.className="ConvRNN2D";var u0=class extends Fh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,fr(this.filters,"filters"),this.kernelSize=gu(r,2,"kernelSize"),this.kernelSize.forEach(o=>fr(o,"kernelSize")),this.strides=gu(n||1,2,"strides"),this.strides.forEach(o=>fr(o,"strides")),this.padding=a||"valid",_n(this.padding),this.dataFormat=s||"channelsLast",Gt(this.dataFormat),this.dilationRate=gu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>fr(o,"dilationRate"))}build(e){var t;e=ft(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Kn{apply(d,h){let p=l.apply([u]),c=hn([u]),f=l.apply([u*2]);return vA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Us({ones:()=>Fn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,J)=>!ee||!ee[J]?V:L(ee[J],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Us({ones:()=>Fn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,v,S]=Xt(this.kernel.read(),i,A),[T,E,R,_]=this.useBias?Xt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,T,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,v,R,this.padding),p=this.inputConv(p,S,_,this.padding);let[M,I,z,O]=Xt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,z),y=this.recurrentConv(y,O);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=zs(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?xa(a,r,this.dataFormat):a}recurrentConv(e,t){return zs(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};u0.className="ConvLSTM2DCell";ue.registerClass(u0);var nx=class extends B4{constructor(e){let t=new u0(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};nx.className="ConvLSTM2D";ue.registerClass(nx);var d0=class extends st{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return Ch(()=>K7(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};d0.className="Dropout";ue.registerClass(d0);var ax=class extends d0{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};ax.className="SpatialDropout1D";ue.registerClass(ax);var sx=class extends st{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,fr(this.units,"units"),this.activation=Vs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ir(e.kernelConstraint),this.biasConstraint=ir(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=W7(this.activation.getClassName()),a;return n!=null?a=Ea(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Ea(r,this.kernel.read()),this.bias!=null&&(a=xa(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Ws(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),biasConstraint:sr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};sx.className="Dense";ue.registerClass(sx);var ix=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Es(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=nt(r,n)}return LW(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};ix.className="Flatten";ue.registerClass(ix);var ox=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.activation=Vs(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.activation.apply(r)})}getConfig(){let e={activation:Ws(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};ox.className="Activation";ue.registerClass(ox);var lx=class extends st{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=je(e),OW(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};lx.className="RepeatVector";ue.registerClass(lx);var ux=class extends st{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Es(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};ux.className="Reshape";ue.registerClass(ux);var dx=class extends st{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ga(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Kt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return nt(je(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};dx.className="Permute";ue.registerClass(dx);var px=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=je(e),n=-1;return wf(ku(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=-1,a=!0,s=wf(ku(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};px.className="Masking";ue.registerClass(px);var hx=class extends st{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,fr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,fr(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=ir(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=je(e),ku(e,at(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);r.dtype!=="int32"&&(r=Gm(r,"int32"));let n=q7(this.embeddings.read(),G(r,[r.size]));return G(n,ft(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:sr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};hx.className="Embedding";ue.registerClass(hx);var Fl=class extends st{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Cs(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Cs(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=Bs(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Th(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Es(u.slice(1))));p=nt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=ga(1,l).concat([0]);r.push(nt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(nt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(ga(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Cs(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:qt(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=fa(r,t[n]);return r})}},cx=class extends Fl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};cx.className="Add";ue.registerClass(cx);var fx=class extends Fl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};fx.className="Multiply";ue.registerClass(fx);var mx=class extends Fl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};mx.className="Average";ue.registerClass(mx);var gx=class extends Fl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=es(t,e[r]);return t})}};gx.className="Maximum";ue.registerClass(gx);var yx=class extends Fl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=vh(t,e[r]);return t})}};yx.className="Minimum";ue.registerClass(yx);var Ax=class extends Fl{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>vA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Fn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(qt(t[s],-1)):n.push(t[s]);let a=kt(n,this.axis);return M2(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ax.className="Concatenate";ue.registerClass(Ax);function mp(e,t){for(;e<0;)e+=t;return e}function bU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(nt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=qt(o,1)),o})}var xx=class extends Fl{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>mp(a,e[s].shape.length)):n=[mp(this.axes,t.shape.length),mp(this.axes,r.shape.length)],this.normalize&&(t=Tf(t,n[0]),r=Tf(r,n[1])),bU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[mp(this.axes,e.length),mp(this.axes,t.length)],r}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};xx.className="Dot";ue.registerClass(xx);var bx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return Ch(()=>le(jm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};bx.className="GaussianNoise";ue.registerClass(bx);var vx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.rate>0&&this.rate<1?Ch(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,jm(r.shape,1,n))},()=>r,t.training||!1):r})}};vx.className="GaussianDropout";ue.registerClass(vx);var wx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Ch(()=>{let n=je(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=Nl(cd(r),this.rate);o=Gm(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>je(e),t.training||!1)}return e})}};wx.className="AlphaDropout";ue.registerClass(wx);function Gp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=$k(e,t,r,n,a,s);else if(e.rank===3)i=Pk(e,t,r,n,a,s);else if(e.rank===4)i=_k(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function vU(e,t,r,n,a=.001){return K(()=>{let s=Nm(e,n),i=s.mean,o=s.variance;return[Gp(e,i,o,r,t,a),i,o]})}function wU(e,t,r,n,a=.001){return K(()=>{let s=Nm(e,n),i=s.mean,o=s.variance,l=[];for(let c of ga(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[Gp(e,u,d,p,h,a),i,o]})}function kU(e,t,r,n,a=.001){return w.arraysEqual(n.slice().sort(),ga(0,e.rank-1))?vU(e,t,r,n,a):wU(e,t,r,n,a)}var kx=class extends st{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=ir(e.betaConstraint),this.gammaConstraint=ir(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Kt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=je(e),a=n.shape,s=a.length,i=ga(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Mo(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,ga(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return Gp(n,g,y,A,x,this.epsilon)}else return Gp(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=kU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),v=L(ce(b,y),x);g.write(ce(b,v))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:sr(this.betaConstraint),gammaConstraint:sr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};kx.className="BatchNormalization";ue.registerClass(kx);var Ix=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Cs(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=je(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=Nm(r,this.axis,!0),o=Mo(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Bn(s,h),i=Bn(i,h),u=Bn(u,p),d=Bn(d,p),Gp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ix.className="LayerNormalization";ue.registerClass(Ix);function IU(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=ma()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Hn(e,n)})}var Sx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ma():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>IU(je(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Sx.className="ZeroPadding2D";ue.registerClass(Sx);function p0(e,t,r,n,a,s){return K(()=>{Gt(a),U7(s),_n(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=ma()),s==null&&(s="max"),e=jA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=Tm(e,t,r,o):i=Am(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function W4(e,t,r,n,a,s){return K(()=>{Gt(a),U7(s),_n(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=ma()),s==null&&(s="max"),e=F4(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=q2(e,t,r,o):i=$2(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var V4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(fr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);fr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,_n(this.padding),this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=ca(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=Th(je(e),2);let r=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Tx=class extends V4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),_n(n),p0(e,t,r,n,a,"max")}};Tx.className="MaxPooling1D";ue.registerClass(Tx);var Nx=class extends V4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),_n(n),p0(e,t,r,n,a,"avg")}};Nx.className="AveragePooling1D";ue.registerClass(Nx);var U4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];fr(this.poolSize,"poolSize"),fr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),_n(this.padding),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ca(t,this.poolSize[0],this.padding,this.strides[0]),r=ca(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Cx=class extends U4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),_n(n),p0(e,t,r,n,a,"max")}};Cx.className="MaxPooling2D";ue.registerClass(Cx);var Ex=class extends U4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),_n(n),p0(e,t,r,n,a,"avg")}};Ex.className="AveragePooling2D";ue.registerClass(Ex);var G4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];fr(this.poolSize,"poolSize"),fr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),_n(this.padding),this.inputSpec=[new Kt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ca(t,this.poolSize[0],this.padding,this.strides[0]),r=ca(r,this.poolSize[1],this.padding,this.strides[1]),n=ca(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Rx=class extends G4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),_n(n),W4(e,t,r,n,a,"max")}};Rx.className="MaxPooling3D";ue.registerClass(Rx);var Mx=class extends G4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),_n(n),W4(e,t,r,n,a,"avg")}};Mx.className="AveragePooling3D";ue.registerClass(Mx);var j4=class extends st{constructor(e){super(e),this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},Fx=class extends j4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return Bt(r,1)})}};Fx.className="GlobalAveragePooling1D";ue.registerClass(Fx);var $x=class extends j4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return mr(r,1)})}};$x.className="GlobalMaxPooling1D";ue.registerClass($x);var H4=class extends st{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Px=class extends H4{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?Bt(r,[1,2]):Bt(r,[2,3])})}};Px.className="GlobalAveragePooling2D";ue.registerClass(Px);var _x=class extends H4{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?mr(r,[1,2]):mr(r,[2,3])})}};_x.className="GlobalMaxPooling2D";ue.registerClass(_x);var q4=class extends st{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){let n=t.layer,a=ha(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},zx=class extends q4{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=je(e),D4((r,n)=>[je(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};zx.className="TimeDistributed";ue.registerClass(zx);function SU(e){Rl(FW,"BidirectionalMergeMode",e)}var TU="concat",Ox=class extends q4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=ha(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=ha(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?TU:e.mergeMode,SU(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):Qr(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=O4(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new Kt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof ua;for(let l of s)if(l instanceof ua!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=$n(a,1));let i;return this.mergeMode==="concat"?i=vA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){ko(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ko(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let r=It(e),n=!0;for(let s of r)if(!(s instanceof ua)){n=!1;break}let a=!0;for(let s of r)if(s instanceof ua){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return To(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of It(e))s.push(i.shape);this.build(Qr(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=It(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Qr(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=JL(e),i=this.computeOutputShape(s),o,l=QL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new ua(l,u,this,It(e),t,this.name,d)):o=new ua(l,i,this,It(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Ga(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Ga(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new la(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new n0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}TA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${OA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=Ey(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=It(e);let r=new ko;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return vp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=It(e);let r;return t==null?r=Po(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=Sf(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Gc);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(Qr(d)),p=Sf(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ta(o in r),a.push(r[o])}return Qr(a)}runInternalGraph(e,t){t==null&&(t=Po(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Gc);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(d.call(x,f)),A=It(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(d.call(m,f)),A=It(d.computeMask(m,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=y[x],S=A[x];r[b.id]=[v,S]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ta(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof Sa?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=Sa.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=Sa.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=Sa.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=Sa.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=Sa.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=Sa.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],S=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let T=a[b];if(T.inboundNodes.length<=v){i(m,g);return}let E=T.inboundNodes[v];y.push(E.outputTensors[S])}y.length>0&&m.apply(Qr(y),A)}function l(m){let g=m.name,y=ha(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!TL(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ta(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ta(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function rU(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function g4(e,t){return rU(e,t,"classWeight")}async function y4(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Br(e);if(e.shape.length===2){if(e.shape[1]>1)return Cn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),St(i,"float32")}else return null}function nU(e,t){return L(e,t)}var aU=32;function A4(e,t){let r,n,a=t;r=a.xs,n=a.ys,w.assert(r!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=uv("input",e.inputNames,r),i=uv("output",e.outputNames,n),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function uv(e,t,r){if(r instanceof nt)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function sU(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function iU(e,t,r){let n=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=r.validationData!=null,s,i;if(a)if(dv(r.validationData))w.assert(r.validationBatches==null||r.validationBatches>0&&Number.isInteger(r.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${r.validationBatches}`);else{let g=sU(r.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=l4(r.callbacks,r.yieldEvery),h=r.verbose==null?1:r.verbose,{callbackList:p,history:c}=u4(d,h,r.epochs,null,null,oU(t,r),null,a,u);p.setModel(e),e.history=c,await p.onTrainBegin(),e.stopTraining_=!1;let f=r.initialEpoch==null?0:r.initialEpoch,m=await t.iterator();for(;f<r.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(n||(m=await t.iterator());!n||y<r.batchesPerEpoch;){let x=await m.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${r.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${r.batchesPerEpoch*r.epochs} batches). 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t=[],r,n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:n[l]});let i=new ko(s),o=vp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Bt(u(a[l],o[l]));l===0?r=d:r=le(r,d),t.push(r)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],h=Bt(u(a[d],o[d]));t.push(h)}return t})}async fit(e,t,r={}){return pU(this,e,t,r)}async fitDataset(e,t){return iU(this,e,t)}async trainOnBatch(e,t){let r=await this.standardizeUserData(e,t),n=r[0],a=r[1],s=this.makeTrainFunction()(n.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return re(s),oa(r[0],e),oa(r[1],t),Qr(i)}getNamedWeights(e){let t=[],r=e!=null&&e.trainableOnly,n=r?this.trainableWeights:this.weights,a=this.getWeights(r);for(let 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if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=bo(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Tr.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Tr.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:gU,generatedBy:`TensorFlow.js tfjs-layers v${OA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Tr.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Tr.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(lv(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){lv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Xa.className="Model";ue.registerClass(Xa);var b4=class extends Xa{};b4.className="Functional";ue.registerClass(b4);async function yU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let n=Up(r),a=ha(n,t);if(e.weightsManifest!=null){let s=await Tr.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),re(s)}return a}async function AU(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Tr.getLoadHandlers(e,t);if(r.length===0)r.push(Tr.browserHTTPRequest(e,t));else if(r.length>1)throw new q(`Found more than one (${r.length}) load handlers for URL '${e}'`);e=r[0]}return xU(e,void 0,t)}async function xU(e,t,r){if(r==null&&(r={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await e.load(),a=n.modelTopology;a.model_config!=null&&(a=a.model_config);let s=r.strict==null?!0:r.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,o=ha(Up(a),t,i),l=n.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),n.userDefinedMetadata!=null&&o.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=bU(n.weightData,n.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),re(u),re(d.map(h=>h.tensor))}return o}function bU(e,t){let r=Tr.decodeWeights(e,t),n={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:r[s.name]}):n[s.name]=r[s.name]}),{modelWeights:n,optimizerWeights:a}}var $y=class extends Xa{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:qm("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof $y||e instanceof Xa,r;if(t){if(r=e,r.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. 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compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new la("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,r={}){if(!this.built)throw new la("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new la("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof $y))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=ha(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},i0=$y;i0.className="Sequential";ue.registerClass(i0);function vU(e){return new Xa(e)}function wU(e){return new i0(e)}function kU(e,t){return t==null&&(t={}),AU(e,t)}function v4(e){return J7(e)}function IU(e,t){FA.registerCallbackConstructor(e,t)}var on=class extends ue.Serializable{getConfig(){return{}}},w4=class extends on{apply(e,t=1){return WL(e,t)}};w4.className="elu";ue.registerClass(w4);var k4=class extends on{apply(e){return rA(e)}};k4.className="selu";ue.registerClass(k4);var I4=class extends on{apply(e){return _a(e)}};I4.className="relu";ue.registerClass(I4);var S4=class extends on{apply(e){return K(()=>vh(6,_a(e)))}};S4.className="relu6";ue.registerClass(S4);var T4=class extends on{apply(e){return e}};T4.className="linear";ue.registerClass(T4);var N4=class extends on{apply(e){return Nr(e)}};N4.className="sigmoid";ue.registerClass(N4);var C4=class extends on{apply(e){return UL(e)}};C4.className="hardSigmoid";ue.registerClass(C4);var E4=class extends on{apply(e){return pd(e)}};E4.className="softplus";ue.registerClass(E4);var R4=class extends on{apply(e){return VL(e)}};R4.className="softsign";ue.registerClass(R4);var M4=class extends on{apply(e){return vu(e)}};M4.className="tanh";ue.registerClass(M4);var LA=class extends on{apply(e,t=-1){return fd(e,t)}};LA.className="softmax";ue.registerClass(LA);var F4=class extends on{apply(e,t=-1){return j2(e,t)}};F4.className="logSoftmax";ue.registerClass(F4);var $4=class extends on{apply(e,t=1){return K(()=>L(Nr(L(e,t)),e))}};$4.className="swish";ue.registerClass($4);var P4=class extends on{apply(e){return K(()=>L(e,vu(pd(e))))}};P4.className="mish";ue.registerClass(P4);function Vs(e){return e.getClassName()}function ny(e,t={}){return Ih(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Us(e){if(e==null){let t={};return t.className="linear",t.config={},ny(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},ny(t)}else return e instanceof on?e:ny(e)}function BA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var _4=class extends ue.Serializable{},Eh=class extends _4{constructor(e){super(),BA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Wt([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,rr(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,Th(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Eh.className="L1L2";ue.registerClass(Eh);function SU(e){return BA(e),new Eh({l1:e!=null?e.l1:null,l2:0})}function TU(e){return BA(e),new Eh({l2:e!=null?e.l2:null,l1:0})}var fv={l1l2:"L1L2"};function xt(e){return gA(e)}function mv(e,t={}){return Ih(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in fv?fv[e]:e,config:{}};return mv(t)}else return e instanceof _4?e:mv(e)}var WA=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=je(e);let r=_a(e);return this.maxValue!=null&&(r=cn(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};WA.className="ReLU";ue.registerClass(WA);var VA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return Sm(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};VA.className="LeakyReLU";ue.registerClass(VA);var UA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=ir(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new Kt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=je(e),Fm(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:xt(this.alphaRegularizer),alphaConstraint:sr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};UA.className="PReLU";ue.registerClass(UA);var GA=class extends st{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=je(e);return xh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};GA.className="ELU";ue.registerClass(GA);var jA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=je(e);return L(r,me(fn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};jA.className="ThresholdedReLU";ue.registerClass(jA);var HA=class extends st{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new LA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=je(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};HA.className="Softmax";ue.registerClass(HA);function yu(e,t,r){if(typeof e=="number")return Po(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!OL(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ca(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Na(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+Ws([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function qA(e,t){return K(()=>(Gt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function z4(e,t){return K(()=>(Gt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function NU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=ga()),Gt(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=tt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=O2(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=xa(o,r)),o})}function gv(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=ga()),Gt(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=qA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Bs.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function CU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=ga()),Gt(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=z4(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=B2(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=xa(o,r)),s==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var KA=class extends st{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",KA.verifyArgs(t),this.rank=e,fr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=yu(t.kernelSize,e,"kernelSize"),this.strides=yu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,zn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Gt(this.dataFormat),this.activation=Us(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ir(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=yu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ta("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!yA(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Vs(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),biasConstraint:sr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Rh=class extends KA{constructor(e,t){super(e,t),this.kernel=null,Rh.verifyArgs(t),this.filters=t.filters,fr(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ir(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return K(()=>{e=je(e);let r,n=this.bias==null?null:this.bias.read(),a=V7(this.activation.getClassName());if(a!=null&&this.rank===2)r=gv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=NU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=gv(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=CU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=ft(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ca(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:xt(this.kernelRegularizer),kernelConstraint:sr(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},O4=class extends Rh{constructor(e){super(2,e),O4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!yA(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},o0=O4;o0.className="Conv2D";ue.registerClass(o0);var D4=class extends Rh{constructor(e){super(3,e),D4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},l0=D4;l0.className="Conv3D";ue.registerClass(l0);var XA=class extends o0{constructor(e){if(super(e),this.inputSpec=[new Kt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Na(o,h,u,this.padding),f=Na(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,1]));let g=L2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=xa(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Na(t[n],o,s,this.padding),t[a]=Na(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};XA.className="Conv2DTranspose";ue.registerClass(XA);var ZA=class extends l0{constructor(e){if(super(e),this.inputSpec=[new Kt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Kt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=je(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Na(l,f,h,this.padding),A=Na(u,m,p,this.padding),x=Na(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=tt(r,[0,2,3,4,1]));let v=Hk(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=tt(v,[0,4,1,2,3])),this.bias!==null&&(v=xa(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Na(t[n],u,i,this.padding),t[a]=Na(t[a],d,o,this.padding),t[s]=Na(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ZA.className="Conv3DTranspose";ue.registerClass(ZA);var L4=class extends Rh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=ir(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=ir(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Kt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=je(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),r=c7(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=xa(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=tt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.pointwiseRegularizer=xt(this.pointwiseRegularizer),e.depthwiseConstraint=sr(this.depthwiseConstraint),e.pointwiseConstraint=sr(this.pointwiseConstraint),e}};L4.className="SeparableConv";var YA=class extends L4{constructor(e){super(2,e)}};YA.className="SeparableConv2D";ue.registerClass(YA);var B4=class extends Rh{constructor(e){super(1,e),B4.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!yA(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},JA=B4;JA.className="Conv1D";ue.registerClass(JA);var QA=class extends st{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=je(e),this.dataFormat==="channelsLast"){let r=Hc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Hc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Hc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Hc(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};QA.className="Cropping2D";ue.registerClass(QA);var ex=class extends st{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,PL(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=je(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=tt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return tt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ex.className="UpSampling2D";ue.registerClass(ex);function EU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=ga()),Gt(a);let i=qA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Ah(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}var tx=class extends KA{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ir(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=je(e);let r=EU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=xa(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ca(t,this.kernelSize[0],this.padding,this.strides[0]),s=ca(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=xt(this.depthwiseRegularizer),e.depthwiseConstraint=sr(this.depthwiseRegularizer),e}};tx.className="DepthwiseConv2D";ue.registerClass(tx);function W4(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function V4(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ma(2,l));if(t=tt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=qt(a,-1)),a=tt(a,u)),n&&(t=$n(t,0),a!=null&&(a=$n(a,0)));let d=[],h,p=r,c=t.shape[0],f=tn(t),m;a!=null&&(m=tn(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let v=m[y],S=ce(Fn(v),v),T=le(L(x[0],v),L(p[0],S)),E=p.map((R,_)=>le(L(x[1][_],v),L(R,S)));return{output:T,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=or(d,1)),[h,g,p]})}var U4=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new p0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Kt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ma(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Ty(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");Ty(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new Kt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new Kt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_=[Wt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_[0]=Wt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>cr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=W4(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new Kt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof ua){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=je(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=V4((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=Wt(e.shape);return t=ke(t,[1,2]),t=Sh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?Iy(t,[1,r]):t):this.cell.stateSize>1?[Iy(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===U4.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=ha(n,r);return new e(Object.assign(t,{cell:a}))}},as=U4;as.className="RNN";ue.registerClass(as);var Mh=class extends st{},u0=class extends Mh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,fr(this.units,"units"),this.activation=Us(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=Tu([1,Ws([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Tu([1,Ws([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gs({ones:()=>Fn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gs({ones:()=>Fn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ea(L(e,s),this.kernel.read()):a=Ea(e,this.kernel.read()),this.bias!=null&&(a=xa(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Ea(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Vs(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),recurrentConstraint:sr(this.recurrentConstraint),biasConstraint:sr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};u0.className="SimpleRNNCell";ue.registerClass(u0);var rx=class extends as{constructor(e){e.cell=new u0(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};rx.className="SimpleRNN";ue.registerClass(rx);var d0=class extends Mh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,fr(this.units,"units"),this.activation=Us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=Tu([1,Ws([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Tu([1,Ws([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gs({ones:()=>Fn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gs({ones:()=>Fn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Ea(e,this.kernel.read());this.useBias&&(u=xa(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Xt(d,[2*this.units,this.units],d.rank-1),c=Ea(n,h),[f,m,g]=Xt(u,3,u.rank-1),[y,A]=Xt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Ea(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Vs(this.activation),recurrentActivation:Vs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),recurrentConstraint:sr(this.recurrentConstraint),biasConstraint:sr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};d0.className="GRUCell";ue.registerClass(d0);var nx=class extends as{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new d0(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};nx.className="GRU";ue.registerClass(nx);var Fh=class extends Mh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,fr(this.units,"units"),this.activation=Us(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Us(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ir(e.kernelConstraint),this.recurrentConstraint=ir(e.recurrentConstraint),this.biasConstraint=ir(e.biasConstraint),this.dropout=Tu([1,Ws([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Tu([1,Ws([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Kn{apply(i,o){let l=a.apply([s]),u=new Zm().apply([s]),d=a.apply([s*2]);return K3(K3(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gs({ones:()=>Fn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gs({ones:()=>Fn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Ea(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Ea(n,this.recurrentKernel.read())),this.useBias&&(h=xa(h,this.bias.read()));let[p,c,f,m]=Xt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Vs(this.activation),recurrentActivation:Vs(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:xt(this.kernelRegularizer),recurrentRegularizer:xt(this.recurrentRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),recurrentConstraint:sr(this.recurrentConstraint),biasConstraint:sr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Fh.className="LSTMCell";ue.registerClass(Fh);var ax=class extends as{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Fh(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ax.className="LSTM";ue.registerClass(ax);var p0=class extends Mh{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){Ty(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{To(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(ha(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Ny(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}TA(t)}};p0.className="StackedRNNCells";ue.registerClass(p0);function Gs(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):X7(t(),r),o=()=>Nh(i,t,n);return!a||a<=1?cr(o().clone()):Array(a).fill(void 0).map(o).map(l=>cr(l.clone()))}var G4=class extends as{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Kt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Wt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new Ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_=[Wt(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_[0]=Wt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!w.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>cr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ca(l,n[0],a,s[0],i[0]),h=ca(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};G4.className="ConvRNN2D";var h0=class extends Fh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t}),this.filters=t,fr(this.filters,"filters"),this.kernelSize=yu(r,2,"kernelSize"),this.kernelSize.forEach(o=>fr(o,"kernelSize")),this.strides=yu(n||1,2,"strides"),this.strides.forEach(o=>fr(o,"strides")),this.padding=a||"valid",zn(this.padding),this.dataFormat=s||"channelsLast",Gt(this.dataFormat),this.dilationRate=yu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>fr(o,"dilationRate"))}build(e){var t;e=ft(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Kn{apply(d,h){let p=l.apply([u]),c=hn([u]),f=l.apply([u*2]);return AA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Gs({ones:()=>Fn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,J)=>!ee||!ee[J]?V:L(ee[J],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Gs({ones:()=>Fn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,v,S]=Xt(this.kernel.read(),i,A),[T,E,R,_]=this.useBias?Xt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,T,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,v,R,this.padding),p=this.inputConv(p,S,_,this.padding);let[M,I,z,O]=Xt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,z),y=this.recurrentConv(y,O);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Os(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?xa(a,r,this.dataFormat):a}recurrentConv(e,t){return Os(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};h0.className="ConvLSTM2DCell";ue.registerClass(h0);var sx=class extends G4{constructor(e){let t=new h0(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};sx.className="ConvLSTM2D";ue.registerClass(sx);var c0=class extends st{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return Nh(()=>X7(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};c0.className="Dropout";ue.registerClass(c0);var ix=class extends c0{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};ix.className="SpatialDropout1D";ue.registerClass(ix);var ox=class extends st{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,fr(this.units,"units"),this.activation=Us(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ir(e.kernelConstraint),this.biasConstraint=ir(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=V7(this.activation.getClassName()),a;return n!=null?a=Ea(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Ea(r,this.kernel.read()),this.bias!=null&&(a=xa(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Vs(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:xt(this.kernelRegularizer),biasRegularizer:xt(this.biasRegularizer),activityRegularizer:xt(this.activityRegularizer),kernelConstraint:sr(this.kernelConstraint),biasConstraint:sr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ox.className="Dense";ue.registerClass(ox);var lx=class extends st{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Rs(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=tt(r,n)}return BL(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};lx.className="Flatten";ue.registerClass(lx);var ux=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.activation=Us(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.activation.apply(r)})}getConfig(){let e={activation:Vs(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};ux.className="Activation";ue.registerClass(ux);var dx=class extends st{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=je(e),DL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};dx.className="RepeatVector";ue.registerClass(dx);var px=class extends st{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Rs(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};px.className="Reshape";ue.registerClass(px);var hx=class extends st{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ma(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Kt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return tt(je(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};hx.className="Permute";ue.registerClass(hx);var cx=class extends st{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=je(e),n=-1;return wf(Iu(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e),n=-1,a=!0,s=wf(Iu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};cx.className="Masking";ue.registerClass(cx);var fx=class extends st{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,fr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,fr(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=ir(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=je(e),Iu(e,at(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);r.dtype!=="int32"&&(r=Km(r,"int32"));let n=K7(this.embeddings.read(),G(r,[r.size]));return G(n,ft(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:xt(this.embeddingsRegularizer),activityRegularizer:xt(this.activityRegularizer),embeddingsConstraint:sr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};fx.className="Embedding";ue.registerClass(fx);var Pl=class extends st{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Es(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Es(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=Ws(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=Sh(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Rs(u.slice(1))));p=tt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=ma(1,l).concat([0]);r.push(tt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(tt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(ma(0,i-1));s=tt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Es(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:qt(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=fa(r,t[n]);return r})}},mx=class extends Pl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};mx.className="Add";ue.registerClass(mx);var gx=class extends Pl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};gx.className="Multiply";ue.registerClass(gx);var yx=class extends Pl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};yx.className="Average";ue.registerClass(yx);var Ax=class extends Pl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=ts(t,e[r]);return t})}};Ax.className="Maximum";ue.registerClass(Ax);var xx=class extends Pl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=vh(t,e[r]);return t})}};xx.className="Minimum";ue.registerClass(xx);var bx=class extends Pl{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>AA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Fn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(qt(t[s],-1)):n.push(t[s]);let a=kt(n,this.axis);return $2(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};bx.className="Concatenate";ue.registerClass(bx);function mp(e,t){for(;e<0;)e+=t;return e}function RU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(tt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=rt(o,u)}return o.shape.length===1&&(o=qt(o,1)),o})}var vx=class extends Pl{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>mp(a,e[s].shape.length)):n=[mp(this.axes,t.shape.length),mp(this.axes,r.shape.length)],this.normalize&&(t=Ef(t,n[0]),r=Ef(r,n[1])),RU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[mp(this.axes,e.length),mp(this.axes,t.length)],r}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};vx.className="Dot";ue.registerClass(vx);var wx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return Nh(()=>le(Xm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};wx.className="GaussianNoise";ue.registerClass(wx);var kx=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=je(e);return this.rate>0&&this.rate<1?Nh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,Xm(r.shape,1,n))},()=>r,t.training||!1):r})}};kx.className="GaussianDropout";ue.registerClass(kx);var Ix=class extends st{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||je(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Nh(()=>{let n=je(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=El(cd(r),this.rate);o=Km(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>je(e),t.training||!1)}return e})}};Ix.className="AlphaDropout";ue.registerClass(Ix);function Gp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=Ok(e,t,r,n,a,s);else if(e.rank===3)i=Dk(e,t,r,n,a,s);else if(e.rank===4)i=Lk(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function MU(e,t,r,n,a=.001){return K(()=>{let s=Rm(e,n),i=s.mean,o=s.variance;return[Gp(e,i,o,r,t,a),i,o]})}function FU(e,t,r,n,a=.001){return K(()=>{let s=Rm(e,n),i=s.mean,o=s.variance,l=[];for(let c of ma(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[Gp(e,u,d,p,h,a),i,o]})}function $U(e,t,r,n,a=.001){return w.arraysEqual(n.slice().sort(),ma(0,e.rank-1))?MU(e,t,r,n,a):FU(e,t,r,n,a)}var Sx=class extends st{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=ir(e.betaConstraint),this.gammaConstraint=ir(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Kt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=je(e),a=n.shape,s=a.length,i=ma(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=Po(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,ma(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return Gp(n,g,y,A,x,this.epsilon)}else return Gp(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=$U(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),v=L(ce(b,y),x);g.write(ce(b,v))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer),betaConstraint:sr(this.betaConstraint),gammaConstraint:sr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Sx.className="BatchNormalization";ue.registerClass(Sx);var Tx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Es(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=je(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=Rm(r,this.axis,!0),o=Po(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Wn(s,h),i=Wn(i,h),u=Wn(u,p),d=Wn(d,p),Gp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:xt(this.betaRegularizer),gammaRegularizer:xt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Tx.className="LayerNormalization";ue.registerClass(Tx);function PU(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=ga()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Hn(e,n)})}var Nx=class extends st{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ga():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>PU(je(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Nx.className="ZeroPadding2D";ue.registerClass(Nx);function f0(e,t,r,n,a,s){return K(()=>{Gt(a),G7(s),zn(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=ga()),s==null&&(s="max"),e=qA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=Em(e,t,r,o):i=vm(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,3,1,2])),i})}function j4(e,t,r,n,a,s){return K(()=>{Gt(a),G7(s),zn(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=ga()),s==null&&(s="max"),e=z4(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=X2(e,t,r,o):i=_2(e,t,r,o),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var H4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(fr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);fr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,zn(this.padding),this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=ca(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=Sh(je(e),2);let r=this.poolingFunction(je(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return rt(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Cx=class extends H4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),zn(n),f0(e,t,r,n,a,"max")}};Cx.className="MaxPooling1D";ue.registerClass(Cx);var Ex=class extends H4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),zn(n),f0(e,t,r,n,a,"avg")}};Ex.className="AveragePooling1D";ue.registerClass(Ex);var q4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];fr(this.poolSize,"poolSize"),fr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),zn(this.padding),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ca(t,this.poolSize[0],this.padding,this.strides[0]),r=ca(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Rx=class extends q4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),zn(n),f0(e,t,r,n,a,"max")}};Rx.className="MaxPooling2D";ue.registerClass(Rx);var Mx=class extends q4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),zn(n),f0(e,t,r,n,a,"avg")}};Mx.className="AveragePooling2D";ue.registerClass(Mx);var K4=class extends st{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];fr(this.poolSize,"poolSize"),fr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),zn(this.padding),this.inputSpec=[new Kt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ca(t,this.poolSize[0],this.padding,this.strides[0]),r=ca(r,this.poolSize[1],this.padding,this.strides[1]),n=ca(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(je(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Fx=class extends K4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),zn(n),j4(e,t,r,n,a,"max")}};Fx.className="MaxPooling3D";ue.registerClass(Fx);var $x=class extends K4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Gt(a),zn(n),j4(e,t,r,n,a,"avg")}};$x.className="AveragePooling3D";ue.registerClass($x);var X4=class extends st{constructor(e){super(e),this.inputSpec=[new Kt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},Px=class extends X4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return Bt(r,1)})}};Px.className="GlobalAveragePooling1D";ue.registerClass(Px);var _x=class extends X4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=je(e);return mr(r,1)})}};_x.className="GlobalMaxPooling1D";ue.registerClass(_x);var Z4=class extends st{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Gt(this.dataFormat),this.inputSpec=[new Kt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zx=class extends Z4{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?Bt(r,[1,2]):Bt(r,[2,3])})}};zx.className="GlobalAveragePooling2D";ue.registerClass(zx);var Ox=class extends Z4{call(e,t){return K(()=>{let r=je(e);return this.dataFormat==="channelsLast"?mr(r,[1,2]):mr(r,[2,3])})}};Ox.className="GlobalMaxPooling2D";ue.registerClass(Ox);var Y4=class extends st{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){let n=t.layer,a=ha(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Dx=class extends Y4{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=je(e),V4((r,n)=>[je(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Dx.className="TimeDistributed";ue.registerClass(Dx);function _U(e){Fl($L,"BidirectionalMergeMode",e)}var zU="concat",Lx=class extends Y4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=ha(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=ha(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?zU:e.mergeMode,_U(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):Qr(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=W4(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new Kt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof ua;for(let l of s)if(l instanceof ua!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=$n(a,1));let i;return this.mergeMode==="concat"?i=AA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){To(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),To(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r);return[r.getTensorList(n.id).popBack(a,s)]}case"TensorListSplit":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("lengths",e,t,r),i=Pj(n,s,a);return r.addTensorList(i),[i.idTensor]}case"TensorListLength":{let n=k("tensorListId",e,t,r),a=r.getTensorList(n.id);return[Se(a.size(),"int32")]}case"TensorListResize":{let n=k("tensorListId",e,t,r),a=k("size",e,t,r),s=r.getTensorList(n.id).resize(a);return r.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function vv(e,t,r){let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=!s,o=a==="prelu",l=n==="fusedbatchnorm",u=k("numArgs",e,t,r);if(s){if(o&&u!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&s&&u!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(l)throw new Error("FusedConv2d 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n=k("strides",e,t,r),a=nf(e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilations",e,t,r);return[zs(k("x",e,t,r),k("filter",e,t,r),[n[1],n[2]],a,s,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=vv(e,t,r);return[Ls.conv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{let{stride:n,pad:a,dataFormat:s,dilations:i,biasArg:o,preluArg:l,activationFunc:u,leakyreluAlpha:d}=vv(e,t,r);return[Ls.depthwiseConv2d({x:k("x",e,t,r),filter:k("filter",e,t,r),strides:[n[1],n[2]],pad:a,dataFormat:s,dilations:[i[1],i[2]],bias:o,activation:u,preluActivationWeights:l,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r),i=k("includeBatchInIndex",e,t,r),{result:o,indexes:l}=s7(k("x",e,t,r),[s[1],s[2]],[n[1],n[2]],a,i);return[o,l]}case"AvgPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[$2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"MaxPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[q2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"Dilation2D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("dilations",e,t,r),i=n[1],o=n[2],l=s[1],u=s[2];return[Hk(k("x",e,t,r),k("filter",e,t,r),[i,o],a,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Oj=(e,t,r)=>{switch(e.op){case"Fill":{let n=k("shape",e,t,r),a=k("dtype",e,t,r),s=k("value",e,t,r);return[dd(n,s,a)]}case"LinSpace":{let n=k("start",e,t,r),a=k("stop",e,t,r),s=k("num",e,t,r);return[Jk(n,a,s)]}case"Multinomial":{let 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n=k("boxes",e,t,r),a=k("scores",e,t,r),s=k("maxOutputSize",e,t,r),i=k("iouThreshold",e,t,r),o=k("scoreThreshold",e,t,r),l=k("softNmsSigma",e,t,r);return{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}}var Dj=async(e,t,r)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o,softNmsSigma:l}=ry(e,t,r),u=await Ie.nonMaxSuppressionWithScoreAsync(n,a,s,i,o,l);return[u.selectedIndices,u.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=ry(e,t,r),l=k("padToMaxOutputSize",e,t,r),u=await Ie.nonMaxSuppressionPaddedAsync(n,a,s,i,o,l);return[u.selectedIndices,u.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:n,scores:a,maxOutputSize:s,iouThreshold:i,scoreThreshold:o}=ry(e,t,r);return[await Ie.nonMaxSuppressionAsync(n,a,s,i,o)]}case"Where":{let n=me(k("condition",e,t,r),"bool"),a=[await iA(n)];return 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k("x",e,t,r).map(u=>St(u.shape));case"Size":return[Se(k("x",e,t,r).size,"int32")];case"Rank":return[Se(k("x",e,t,r).rank,"int32")];case"NoOp":return[Se(1)];case"Print":let s=k("x",e,t,r),i=k("data",e,t,r),o=k("message",e,t,r),l=k("summarize",e,t,r);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;u<i.length;u++)console.log(Array.prototype.slice.call(i[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Wj=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Se(0),this.tensorMap=new Map,cr(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Se(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),K(()=>{let n=tn(t),a=r.length,s=n.length;w.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=r[i],l=n[i];cr(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return K(()=>{let n=[];for(let a=0;a<r.length;a++){let s=r[a],i=this.findWithDefault(s,t);n.push(i)}return or(n)})}findWithDefault(e,t){let r=this.tensorMap.get(e);return r!=null?r:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},Vj=async(e,t,r,n)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=k("keyDType",e,t,r),s=k("valueDType",e,t,r),i=new Wj(a,s);return n.addHashTable(e.name,i),[i.handle]}case"LookupTableImport":case"LookupTableImportV2":{let a=k("tableHandle",e,t,r,n),s=k("keys",e,t,r),i=k("values",e,t,r);return[await n.getHashTableById(a.id).import(s,i)]}case"LookupTableFind":case"LookupTableFindV2":{let a=k("tableHandle",e,t,r,n),s=k("keys",e,t,r),i=k("defaultValue",e,t,r);return[await n.getHashTableById(a.id).find(s,i)]}case"LookupTableSize":case"LookupTableSizeV2":{let a=k("tableHandle",e,t,r,n);return[n.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Uj=(e,t,r)=>{switch(e.op){case"ResizeBilinear":{let n=k("images",e,t,r),a=k("size",e,t,r),s=k("alignCorners",e,t,r),i=k("halfPixelCenters",e,t,r);return[Ie.resizeBilinear(n,[a[0],a[1]],s,i)]}case"ResizeNearestNeighbor":{let n=k("images",e,t,r),a=k("size",e,t,r),s=k("alignCorners",e,t,r),i=k("halfPixelCenters",e,t,r);return[Ie.resizeNearestNeighbor(n,[a[0],a[1]],s,i)]}case"CropAndResize":{let n=k("image",e,t,r),a=k("boxes",e,t,r),s=k("boxInd",e,t,r),i=k("cropSize",e,t,r),o=k("method",e,t,r),l=k("extrapolationValue",e,t,r);return[Ie.cropAndResize(n,a,s,i,o,l)]}case"ImageProjectiveTransformV3":{let n=k("images",e,t,r),a=k("transforms",e,t,r),s=k("outputShape",e,t,r),i=k("fillValue",e,t,r),o=k("interpolation",e,t,r),l=k("fillMode",e,t,r);return[Ie.transform(n,a,o.toLowerCase(),l.toLowerCase(),i,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Gj=(e,t,r)=>{switch(e.op){case"Equal":return[En(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[ku(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[fn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[Nl(k("a",e,t,r),k("b",e,t,r))];case"Less":return[V2(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[Cl(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[fa(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[Sm(k("a",e,t,r))];case"LogicalOr":return[H2(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Wr(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},jj=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[Kk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[nt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=a==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,d]=k("args",e,t,r);return[Ls.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:u,activation:a,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Hj=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[vu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[vu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[Qk(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[fd(k("x",e,t,r))];case"LogSoftmax":return[U2(k("x",e,t,r))];case"SparseToDense":return[lA(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},qj=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[mr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Bt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Os(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[M2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[wf(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Cn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[Ik(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[K2(k("x",e,t,r),i,o)]}case"Cumprod":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[Uk(k("x",e,t,r),i,o,l)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[B2(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[P2(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[Gk(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Kj=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[kt(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[wu(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[wu(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[$n(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[$n(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[c7(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=et(a[0]).shape,o=a.map(l=>{let u=w.arraysEqual(l.shape,s);if(!u&&!w.arraysEqual(et(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[or(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return tn(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Bn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Xt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[b7(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[v7(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[lA(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Xj=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=bp.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=bp.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[bp.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[bp.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Zj=(e,t,r)=>{switch(e.op){case"FFT":return[Mm(k("x",e,t,r))];case"IFFT":return[Wp(k("x",e,t,r))];case"RFFT":return[Fm(k("x",e,t,r))];case"IRFFT":return[aA(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Yj=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:a}=rf.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[n,a]}case"StringSplit":{let{indices:n,values:a,shape:s}=rf.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[n,a,s]}case"StringToHashBucketFast":return[rf.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Jj=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let n=k("axis",e,t,r);return[qt(k("x",e,t,r),n)]}case"Squeeze":{let n=k("axis",e,t,r);return[et(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[i7(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Hn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[Cm(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[xm(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[jk(k("x",e,t,r),n,a)]}case"BroadcastTo":return[Ep(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[zk(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function wv(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>Cj(s,i,o));case"basic_math":return K(()=>Ej(s,i,o));case"control":return _j(s,i,o);case"convolution":return K(()=>zj(s,i,o));case"creation":return K(()=>Oj(s,i,o));case"dynamic":return Dj(s,i,o);case"evaluation":return K(()=>Lj(s,i,o));case"image":return K(()=>Uj(s,i,o));case"graph":return K(()=>Bj(s,i,o));case"logical":return K(()=>Gj(s,i,o));case"matrices":return K(()=>jj(s,i,o));case"normalization":return K(()=>Hj(s,i,o));case"reduction":return K(()=>qj(s,i,o));case"slice_join":return K(()=>Kj(s,i,o));case"sparse":return K(()=>Xj(s,i,o));case"spectral":return K(()=>Zj(s,i,o));case"string":return K(()=>Yj(s,i,o));case"transformation":return K(()=>Jj(s,i,o));case"hash_table":return Vj(s,i,o,n);case"custom":let l=a6(s.op);if(l&&l.customExecutor)return l.customExecutor(new Nj(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Iv(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>dn(p)[0]),d=[];n!=null&&(d=n.map(p=>dn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((T6(p)||nH(p)||aH(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Qj(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>dn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var eH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],tH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],rH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function T6(e){return eH.indexOf(e.op)>=0}function nH(e){return tH.indexOf(e.op)>=0}function aH(e){return rH.indexOf(e.op)>=0}var Uy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new Uy(e.functions[r],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(r=>e[r].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=Iv(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},NH=class extends yr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=da.getTensorsInContainer(e.value),r=this.transform(e.value),n=da.getTensorsInContainer(r);for(let a of t)da.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},CH=class extends yr{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Sv=class extends yr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=da.getTensorsInContainer(e.value),r=await this.transform(e.value),n=da.getTensorsInContainer(r);for(let a of t)da.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Gx=class extends yr{constructor(){super(),this.outputQueue=new $6,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},EH=class extends Gx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=da.getTensorsInContainer(e.value),r=this.transform(e.value),n=da.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)da.isTensorInList(a,n)||a.dispose();return!0}},_6=class extends yr{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},z6=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(z6||{}),RH=class extends yr{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof yr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await R6(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},O6=class extends yr{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new M6(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},MH=class extends O6{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=pH.alea(r||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Ad=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),un(async()=>(await r.iterator()).columnMajorBatch(e,t,PH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,un(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,un(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return un(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,un(async()=>{let n=Ux(async()=>({value:await t.iterator(),done:!1}));return AH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,un(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=dH.alea(t||w.now().toString());return un(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,un(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ad.MAX_BUFFER_SIZE=1e4;function un(e,t=null){return new class extends Ad{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function FH(e){return un(async()=>P6(e),e.length)}function $H(e){if(!Cu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return un(async()=>{let r=await R6(e,n=>{if(n instanceof Ad)return{value:n.iterator(),recurse:!1};if(Cu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return xH(r,1)},t)}function PH(e){if(e===null)return null;let t=e[0];return fH(t)?{value:_H(e),recurse:!1}:{value:null,recurse:!0}}function _H(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof rt?or(e):ct(e)}var D6=class extends Ad{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Xc='"',yp=Symbol("out"),Tv=Symbol("field"),Zc=Symbol("quote"),ny=Symbol("quoteafterquote"),Nv=Symbol("quoteinquote"),L6=class extends Ad{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new D6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=yp;for(let i=0;i<a;i++)switch(s){case yp:switch(e.charAt(i)){case Xc:n=i+1,s=Zc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=yp;break;default:s=Tv,n=i;break}break;case Tv:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=yp,n=i+1;break;default:}break;case Zc:switch(e.charAt(i)){case Xc:s=ny;break;default:}break;case ny:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=yp,n=i+1;break;case Xc:s=Zc;break;default:s=Nv;break}break;case Nv:switch(e.charAt(i)){case Xc:s=Zc;break;default:}break;default:}if(s===ny?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},B6=class extends yr{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Y().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new B6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),ct(r,t)}},W6=class extends yr{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=pa([s,a,o,i],[1,4])}else this.cropBox=pa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Y().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new W6(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Pn.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=qt(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},V6=class{},U6=class extends yr{split(e){return new zH(this,e)}},zH=class extends U6{constructor(e,t){super(),this.upstream=e,this.impl=new OH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},OH=class extends Gx{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},DH=class extends yr{decodeUTF8(){return new LH(this)}},LH=class extends U6{constructor(e){super(),this.upstream=e,this.impl=new BH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},BH=class extends Gx{constructor(e){if(super(),this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=fw();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return Y().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},G6=class extends DH{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof 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n=k("image",e,t,r),a=k("boxes",e,t,r),s=k("boxInd",e,t,r),i=k("cropSize",e,t,r),o=k("method",e,t,r),l=k("extrapolationValue",e,t,r);return[Ie.cropAndResize(n,a,s,i,o,l)]}case"ImageProjectiveTransformV3":{let n=k("images",e,t,r),a=k("transforms",e,t,r),s=k("outputShape",e,t,r),i=k("fillValue",e,t,r),o=k("interpolation",e,t,r),l=k("fillMode",e,t,r);return[Ie.transform(n,a,o.toLowerCase(),l.toLowerCase(),i,s)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},eH=(e,t,r)=>{switch(e.op){case"Equal":return[En(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[Iu(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[fn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[El(k("a",e,t,r),k("b",e,t,r))];case"Less":return[G2(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[Rl(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[fa(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[Cm(k("a",e,t,r))];case"LogicalOr":return[K2(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Wr(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},tH=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[Yk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[tt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=a==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,d]=k("args",e,t,r);return[Bs.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:u,activation:a,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},rH=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[wu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[wu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[r7(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[fd(k("x",e,t,r))];case"LogSoftmax":return[j2(k("x",e,t,r))];case"SparseToDense":return[dA(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},nH=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[mr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Bt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Ds(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[$2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[wf(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Cn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[Ck(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Z2(k("x",e,t,r),i,o)]}case"Cumprod":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[If(k("x",e,t,r),i,o,l)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[V2(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[z2(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[qk(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},aH=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[kt(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[ku(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[ku(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[$n(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[$n(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[g7(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=rt(a[0]).shape,o=a.map(l=>{let u=w.arraysEqual(l.shape,s);if(!u&&!w.arraysEqual(rt(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[or(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return tn(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Wn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Xt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[k7(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[I7(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[dA(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},sH=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=bp.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=bp.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[bp.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[bp.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},iH=(e,t,r)=>{switch(e.op){case"FFT":return[Pm(k("x",e,t,r))];case"IFFT":return[Wp(k("x",e,t,r))];case"RFFT":return[_m(k("x",e,t,r))];case"IRFFT":return[iA(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},oH=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:a}=rf.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[n,a]}case"StringSplit":{let{indices:n,values:a,shape:s}=rf.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[n,a,s]}case"StringToHashBucketFast":return[rf.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},lH=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let n=k("axis",e,t,r);return[qt(k("x",e,t,r),n)]}case"Squeeze":{let n=k("axis",e,t,r);return[rt(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[u7(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Hn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[Mm(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[wm(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[Kk(k("x",e,t,r),n,a)]}case"BroadcastTo":return[Ep(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[Bk(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function kv(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>Dj(s,i,o));case"basic_math":return K(()=>Lj(s,i,o));case"control":return jj(s,i,o);case"convolution":return K(()=>Hj(s,i,o));case"creation":return K(()=>qj(s,i,o));case"dynamic":return Kj(s,i,o);case"evaluation":return K(()=>Xj(s,i,o));case"image":return K(()=>Qj(s,i,o));case"graph":return K(()=>Zj(s,i,o));case"logical":return K(()=>eH(s,i,o));case"matrices":return K(()=>tH(s,i,o));case"normalization":return K(()=>rH(s,i,o));case"reduction":return K(()=>nH(s,i,o));case"slice_join":return K(()=>aH(s,i,o));case"sparse":return K(()=>sH(s,i,o));case"spectral":return K(()=>iH(s,i,o));case"string":return K(()=>oH(s,i,o));case"transformation":return K(()=>lH(s,i,o));case"hash_table":return Jj(s,i,o,n);case"custom":let l=l6(s.op);if(l&&l.customExecutor)return l.customExecutor(new Oj(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. 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e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Sv(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>dn(p)[0]),d=[];n!=null&&(d=n.map(p=>dn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((R6(p)||cH(p)||fH(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function uH(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>dn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var dH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],pH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],hH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function R6(e){return dH.indexOf(e.op)>=0}function cH(e){return pH.indexOf(e.op)>=0}function fH(e){return hH.indexOf(e.op)>=0}var jy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new jy(e.functions[r],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(r=>e[r].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=Sv(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. 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t=Tr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Tr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Tr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},OH=class extends yr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=da.getTensorsInContainer(e.value),r=this.transform(e.value),n=da.getTensorsInContainer(r);for(let a of t)da.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},DH=class extends yr{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Tv=class extends yr{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=da.getTensorsInContainer(e.value),r=await this.transform(e.value),n=da.getTensorsInContainer(r);for(let a of t)da.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Hx=class extends yr{constructor(){super(),this.outputQueue=new O6,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},LH=class extends Hx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=da.getTensorsInContainer(e.value),r=this.transform(e.value),n=da.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)da.isTensorInList(a,n)||a.dispose();return!0}},L6=class extends yr{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},B6=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(B6||{}),BH=class extends yr{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof yr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await P6(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},W6=class extends yr{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new _6(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},WH=class extends W6{constructor(e,t,r){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=vH.alea(r||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Ad=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),un(async()=>(await r.iterator()).columnMajorBatch(e,t,GH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,un(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,un(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return un(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return un(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return un(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,un(async()=>{let n=jx(async()=>({value:await t.iterator(),done:!1}));return CH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,un(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=bH.alea(t||w.now().toString());return un(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,un(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ad.MAX_BUFFER_SIZE=1e4;function un(e,t=null){return new class extends Ad{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function VH(e){return un(async()=>D6(e),e.length)}function UH(e){if(!Eu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return un(async()=>{let r=await P6(e,n=>{if(n instanceof Ad)return{value:n.iterator(),recurse:!1};if(Eu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return EH(r,1)},t)}function GH(e){if(e===null)return null;let t=e[0];return IH(t)?{value:jH(e),recurse:!1}:{value:null,recurse:!0}}function jH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof nt?or(e):ct(e)}var V6=class extends Ad{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Xc='"',yp=Symbol("out"),Nv=Symbol("field"),Zc=Symbol("quote"),sy=Symbol("quoteafterquote"),Cv=Symbol("quoteinquote"),U6=class extends Ad{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new V6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=yp;for(let i=0;i<a;i++)switch(s){case yp:switch(e.charAt(i)){case Xc:n=i+1,s=Zc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=yp;break;default:s=Nv,n=i;break}break;case Nv:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=yp,n=i+1;break;default:}break;case Zc:switch(e.charAt(i)){case Xc:s=sy;break;default:}break;case sy:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=yp,n=i+1;break;case Xc:s=Zc;break;default:s=Cv;break}break;case Cv:switch(e.charAt(i)){case Xc:s=Zc;break;default:}break;default:}if(s===sy?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},G6=class extends yr{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Y().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new G6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),ct(r,t)}},j6=class extends yr{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=pa([s,a,o,i],[1,4])}else this.cropBox=pa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Y().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new j6(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=_n.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=qt(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},H6=class{},q6=class extends yr{split(e){return new HH(this,e)}},HH=class extends q6{constructor(e,t){super(),this.upstream=e,this.impl=new qH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},qH=class extends Hx{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},KH=class extends yr{decodeUTF8(){return new XH(this)}},XH=class extends q6{constructor(e){super(),this.upstream=e,this.impl=new ZH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},ZH=class extends Hx{constructor(e){if(super(),this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Aw();this.decoder=new 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r.makeTensorInfo(a.shape,a.dtype,m)}var YK={kernelName:pi,backendName:"cpu",kernelFunc:ZK};function JK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Te([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Mt({inputs:{x:a},backend:r,attrs:{shape:l}}),f=nn({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:d}}),g=zo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var QK={kernelName:Uo,backendName:"cpu",kernelFunc:JK};function eX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Xx(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var 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r.makeTensorInfo(A.shape,A.dtype,A.values)}var hX={kernelName:Jf,backendName:"cpu",kernelFunc:pX};function cX(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Te([a,s],"conv2dBackpropInput");let h=w.computeStrides(s.shape),p=w.computeStrides(a.shape),c=N.convertConv2DDataFormat(u),f=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),m=new ar(f.inShape,"float32"),g=m.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,v]=h,{batchSize:S,filterHeight:T,filterWidth:E,inChannels:R,inHeight:_,inWidth:M,outChannels:I,outHeight:z,outWidth:O,strideHeight:j,strideWidth:X}=f;c=f.dataFormat;let D=T-1-f.padInfo.top,Q=E-1-f.padInfo.left,V=c==="channelsLast",ee=m.strides[0],J=V?m.strides[1]:m.strides[2],se=V?m.strides[2]:1,Z=V?1:m.strides[1],ae=p[0],de=V?p[1]:p[2],Ae=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<S;++Ee)for(let Me=0;Me<R;++Me)for(let De=0;De<_;++De){let 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u=w.computeStrides(a.shape),d=w.computeStrides(s.shape),h=N.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,f=h.strideWidth,m=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new ar(h.filterShape,"float32"),x=A.values,[b,v,S,T]=A.strides,E=r.data.get(s.dataId).values,[R,_,M,I]=d,z=r.data.get(a.dataId).values,[O,j,X,D]=u,Q=h.padInfo.front,V=h.padInfo.left,ee=h.padInfo.top;for(let J=0;J<m;++J){let se=Math.max(0,Math.ceil((Q-J)/p)),Z=Math.min(h.outDepth,(h.inDepth+Q-J)/p),ae=J*b;for(let de=0;de<g;++de){let Ae=Math.max(0,Math.ceil((ee-de)/c)),be=Math.min(h.outHeight,(h.inHeight+ee-de)/c),Ee=de*v+ae;for(let Me=0;Me<y;++Me){let De=Math.max(0,Math.ceil((V-Me)/f)),Be=Math.min(h.outWidth,(h.inWidth+V-Me)/f),Ze=Me*S+Ee;for(let ot=0;ot<h.inChannels;++ot){let dt=ot*T+Ze;for(let pt=0;pt<h.outChannels;++pt){let $e=0;for(let vt=0;vt<h.batchSize;++vt){let yt=vt*O,$r=vt*R;for(let dr=se;dr<Z;++dr){let Zr=(J+dr*p-Q)*j+yt,er=dr*_+$r;for(let pr=Ae;pr<be;++pr){let 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h=Cr(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?0:c[x];else{let b=m(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=nn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var RX={kernelName:ai,backendName:"cpu",kernelFunc:EX};function MX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Xx(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=tI(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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zX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"depthwiseConv2dNativeBackpropFilter");let h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),{strideHeight:p,strideWidth:c,filterHeight:f,filterWidth:m}=h,g=new ar(h.filterShape,"float32"),y=h.padInfo.left,A=h.padInfo.top,x=h.outChannels/h.inChannels,b=r.data.get(a.dataId).values,v=new ar(a.shape,a.dtype,b),S=r.data.get(s.dataId).values,T=new ar(s.shape,s.dtype,S);for(let E=0;E<f;++E){let R=Math.max(0,Math.ceil((A-E)/p)),_=Math.min(h.outHeight,(h.inHeight+A-E)/p);for(let M=0;M<m;++M){let I=Math.max(0,Math.ceil((y-M)/c)),z=Math.min(h.outWidth,(h.inWidth+y-M)/c);for(let O=0;O<h.outChannels;++O){let j=Math.trunc(O/x),X=O%x,D=0;for(let Q=0;Q<h.batchSize;++Q)for(let V=R;V<_;++V){let ee=E+V*p-A;for(let J=I;J<z;++J){let se=M+J*c-y;D+=v.get(Q,ee,se,j)*T.get(Q,V,J,O)}}g.set(D,E,M,j,X)}}}return r.makeTensorInfo(g.shape,g.dtype,g.values)}var OX={kernelName:rm,backendName:"cpu",kernelFunc:zX};function DX(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n;Te([a,s],"depthwiseConv2DNativeBackpropInput");let h=w.computeStrides(a.shape),p=w.computeStrides(s.shape),c=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),f=new ar(c.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=r.data.get(a.dataId).values,[b,v,S]=h,T=r.data.get(s.dataId).values,[E,R,_]=p,{batchSize:M,filterHeight:I,filterWidth:z,inChannels:O,inHeight:j,inWidth:X,outChannels:D,outHeight:Q,outWidth:V,strideHeight:ee,strideWidth:J}=c,se=I-1-c.padInfo.top,Z=z-1-c.padInfo.left,ae=D/O;for(let de=0;de<M;++de)for(let Ae=0;Ae<O;++Ae)for(let be=0;be<j;++be){let Ee=be-se,Me=Math.max(0,Math.ceil(Ee/ee)),De=Math.min(Q,(I+Ee)/ee);for(let Be=0;Be<X;++Be){let Ze=Be-Z,ot=Math.max(0,Math.ceil(Ze/J)),dt=Math.min(V,(z+Ze)/J),pt=0;for(let $e=Me;$e<De;++$e){let vt=$e*ee-Ee;for(let yt=ot;yt<dt;++yt){let 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         uint floatToUint = floatBitsToUint(val);
         return (floatToUint & 0x7fffffffu) > 0x7f800000u;
@@ -106,15 +106,15 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
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   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function ob(){return`
+`}function ub(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
-`}var vS=`
+`}var SS=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -153,22 +153,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,{getBroadcastDims:wS}=N;function yQ(e,t,r){let n=[];if(e.forEach(p=>{let c=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=lb(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
-`),s=e.map(p=>AQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
-`),i=t.texShape,o=Gr(),l=vQ(o),u,d,h=IQ(o);return t.isPacked?(u=xQ(t.logicalShape,i,r.enableShapeUniforms),d=kQ(o)):(u=bQ(t.logicalShape,i,r.enableShapeUniforms),d=wQ(o)),r.packedInputs&&(h+=CQ),[h,l,d,a,u,s,r.userCode].join(`
-`)}function wd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return BQ(e,t);case 1:return VQ(e,t);case 2:return GQ(e,t);case 3:return HQ(e,t);case 4:return KQ(e,t);case 5:return XQ(e);case 6:return ZQ(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function kS(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return LQ(e);case 1:return WQ(e,t);case 2:return UQ(e,t);case 3:return jQ(e,t);default:return qQ(e,t)}}function AQ(e,t,r=!1,n){let a="";r?a+=kS(e,n):a+=wd(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=YQ(e,t):a+=JQ(e,t)),a}function xQ(e,t,r){switch(e.length){case 0:return IS();case 1:return EQ(e,t,r);case 2:return OQ(e,t,r);case 3:return MQ(e,t,r);default:return $Q(e,t,r)}}function bQ(e,t,r){switch(e.length){case 0:return IS();case 1:return RQ(e,t,r);case 2:return DQ(e,t,r);case 3:return FQ(e,t,r);case 4:return PQ(e,t,r);case 5:return _Q(e,t);case 6:return zQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function vQ(e){return`
+`,{getBroadcastDims:TS}=N;function NQ(e,t,r){let n=[];if(e.forEach(p=>{let c=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=db(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
+`),s=e.map(p=>CQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
+`),i=t.texShape,o=Gr(),l=MQ(o),u,d,h=PQ(o);return t.isPacked?(u=EQ(t.logicalShape,i,r.enableShapeUniforms),d=$Q(o)):(u=RQ(t.logicalShape,i,r.enableShapeUniforms),d=FQ(o)),r.packedInputs&&(h+=DQ),[h,l,d,a,u,s,r.userCode].join(`
+`)}function wd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return ZQ(e,t);case 1:return JQ(e,t);case 2:return eee(e,t);case 3:return ree(e,t);case 4:return aee(e,t);case 5:return see(e);case 6:return iee(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function NS(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return XQ(e);case 1:return YQ(e,t);case 2:return QQ(e,t);case 3:return tee(e,t);default:return nee(e,t)}}function CQ(e,t,r=!1,n){let a="";r?a+=NS(e,n):a+=wd(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=oee(e,t):a+=lee(e,t)),a}function EQ(e,t,r){switch(e.length){case 0:return CS();case 1:return LQ(e,t,r);case 2:return qQ(e,t,r);case 3:return WQ(e,t,r);default:return UQ(e,t,r)}}function RQ(e,t,r){switch(e.length){case 0:return CS();case 1:return BQ(e,t,r);case 2:return KQ(e,t,r);case 3:return VQ(e,t,r);case 4:return GQ(e,t,r);case 5:return jQ(e,t);case 6:return HQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function MQ(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function wQ(e){return`
+  `}function FQ(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function kQ(e){return`
+  `}function $Q(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function IQ(e){return`${e.version}
+  `}function PQ(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -223,10 +223,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${SQ}
-    ${TQ}
-    ${NQ}
-  `}var SQ=`
+    ${_Q}
+    ${zQ}
+    ${OQ}
+  `}var _Q=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -238,7 +238,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,TQ=`
+`,zQ=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -246,7 +246,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,NQ=`
+`,OQ=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -255,7 +255,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,CQ=`
+`,DQ=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -266,11 +266,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function IS(){return`
+`;function CS(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function EQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?r?`
+  `}function LQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?r?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
       }
@@ -299,7 +299,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${n[0]}, ${n[1]}));
       return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
     }
-  `}function RQ(e,t,r){return t[0]===1?r?`
+  `}function BQ(e,t,r){return t[0]===1?r?`
       int getOutputCoords() {
         return int(resultUV.x * float(outTexShape[1]));
       }
@@ -327,7 +327,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function MQ(e,t,r){if(r)return`
+  `}function WQ(e,t,r){if(r)return`
     ivec3 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
@@ -358,15 +358,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function FQ(e,t,r){if(r)return`
+  `}function VQ(e,t,r){if(r)return`
   ivec3 getOutputCoords() {
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${g0(["r","c","d"],e)}
+    ${x0(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
-`;let n=$l(["r","c","d"],e);return`
+`;let n=_l(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -374,7 +374,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec3(r, c, d);
     }
-  `}function $Q(e,t,r){if(r)return`
+  `}function UQ(e,t,r){if(r)return`
     ivec4 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -415,15 +415,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${l});
     }
-  `}function PQ(e,t,r){if(r)return`
+  `}function GQ(e,t,r){if(r)return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${g0(["r","c","d","d2"],e)}
+      ${x0(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
-  `;let n=$l(["r","c","d","d2"],e);return`
+  `;let n=_l(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -431,7 +431,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${n}
       return ivec4(r, c, d, d2);
     }
-  `}function _Q(e,t){let r=$l(["r","c","d","d2","d3"],e);return`
+  `}function jQ(e,t){let r=_l(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function zQ(e,t){let r=$l(["r","c","d","d2","d3","d4"],e);return`
+  `}function HQ(e,t){let r=_l(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -454,7 +454,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function OQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return r?`
+  `}function qQ(e,t,r){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return r?`
       ivec2 getOutputCoords() {
         ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
         return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
@@ -487,7 +487,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function DQ(e,t,r){return w.arraysEqual(e,t)?r?`
+  `}function KQ(e,t,r){return w.arraysEqual(e,t)?r?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
       }
@@ -541,15 +541,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function Pl(e){return`offset${e}`}function LQ(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Gr();return`
+  `}function zl(e){return`offset${e}`}function XQ(e){let t=e.name,r="get"+t.charAt(0).toUpperCase()+t.slice(1),n=Gr();return`
     vec4 ${r}() {
       return ${n.texture2D}(${t}, halfCR);
     }
-  `}function BQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${r};}`;let[a,s]=e.shapeInfo.texShape;if(a===1&&s===1)return`
+  `}function ZQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${r};}`;let[a,s]=e.shapeInfo.texShape;if(a===1&&s===1)return`
       float ${n}() {
         return sampleTexture(${r}, halfCR);
       }
-    `;let i=Pl(r);if(t)return`
+    `;let i=zl(r);if(t)return`
     float ${n}() {
       vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], ${i});
       return sampleTexture(${r}, uv);
@@ -559,7 +559,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${o}, ${l}, ${i});
       return sampleTexture(${r}, uv);
     }
-  `}function WQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,s=Gr();if(t)return`
+  `}function YQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,s=Gr();if(t)return`
     vec4 ${n}(int index) {
       ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));
       vec2 uv = packedUVfrom1D(
@@ -572,7 +572,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${i[0]}, ${i[1]}, index);
       return ${s.texture2D}(${r}, uv);
     }
-  `}function VQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function JQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
         ${kd(e)}
       }
@@ -580,7 +580,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       float ${n}(int index) {
         return sampleTexture(${r}, halfCR);
       }
-    `;let o=Pl(r);return i===1?t?`
+    `;let o=zl(r);return i===1?t?`
       float ${n}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${r}TexShape[0]));
         return sampleTexture(${r}, uv);
@@ -610,7 +610,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${s}, ${i}, index + ${o});
       return sampleTexture(${r}, uv);
     }
-  `}function UQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Gr();if(s!=null&&w.arraysEqual(r,s))return t?`
+  `}function QQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=s[0],o=s[1],l=Gr();if(s!=null&&w.arraysEqual(r,s))return t?`
       vec4 ${a}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
 
@@ -634,7 +634,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${d}, ${u[0]}, ${u[1]}, row, col);
       return ${l.texture2D}(${n}, uv);
     }
-  `}function GQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(r,s)){if(t)return`
+  `}function eee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape;if(s!=null&&w.arraysEqual(r,s)){if(t)return`
       float ${a}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${n}TexShape[1], ${n}TexShape[0]);
         return sampleTexture(${n}, uv);
@@ -654,7 +654,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
         ${kd(e)}
       }
-    `;let u=s[0],d=s[1],h=Pl(n);return d===1?t?`
+    `;let u=s[0],d=s[1],h=zl(n);return d===1?t?`
       float ${a}(int row, int col) {
         float index = dot(vec3(row, col, ${h}), vec3(${n}Shape[1], 1, 1));
         vec2 uv = vec2(0.5, (index + 0.5) / float(${n}TexShape[0]));
@@ -692,8 +692,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${u}, ${d}, index);
     return sampleTexture(${n}, uv);
   }
-`}function jQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=Id(e,p),m=["b","row","col"];return`
-        ${kS(f,t)}
+`}function tee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=Id(e,p),m=["b","row","col"];return`
+        ${NS(f,t)}
         vec4 ${a}(int b, int row, int col) {
           return ${a}(${Sd(m,c)});
         }
@@ -712,7 +712,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${l}, ${u}, ${h}, ${d}, b, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function HQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),u=o;if(u.length<r.length){let m=Id(e,u),g=["row","col","depth"];return`
+  `}function ree(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),u=o;if(u.length<r.length){let m=Id(e,u),g=["row","col","depth"];return`
         ${wd(m,t)}
         float ${a}(int row, int col, int depth) {
           return ${a}(${Sd(g,l)});
@@ -754,7 +754,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${h}.0);
       return sampleTexture(${n}, uv);
     }
-  `;let f=Pl(n);return t?`
+  `;let f=zl(n);return t?`
     float ${a}(int row, int col, int depth) {
       // Explicitly use integer operations as dot() only works on floats.
       int stride0 = ${n}Shape[1] * ${n}Shape[2];
@@ -770,7 +770,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         vec2 uv = uvFromFlat(${h}, ${p}, index);
         return sampleTexture(${n}, uv);
       }
-  `}function qQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=Gr();if(t)return`
+  `}function nee(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=Gr();if(t)return`
     vec4 ${n}(int b2, int b, int row, int col) {
       int valuesPerRow = int(ceil(float(${r}Shape[3]) / 2.0));
       int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[2]) / 2.0));
@@ -790,7 +790,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
       return ${a.texture2D}(${r}, uv);
     }
-  `}function KQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(r);if(l.length<r.length){let A=Id(e,l),x=["row","col","depth","depth2"];return`
+  `}function aee(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(r);if(l.length<r.length){let A=Id(e,l),x=["row","col","depth","depth2"];return`
       ${wd(A,t)}
       float ${a}(int row, int col, int depth, int depth2) {
         return ${a}(${Sd(x,u)});
@@ -841,7 +841,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${c}.0, ${p}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let y=Pl(n);return t?`
+    `;let y=zl(n);return t?`
     float ${a}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       ${f}
@@ -860,7 +860,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index + ${y});
       return sampleTexture(${n}, uv);
     }
-  `}function XQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=Id(e,l),g=["row","col","depth","depth2","depth3"];return`
+  `}function see(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=Id(e,l),g=["row","col","depth","depth2","depth3"];return`
       ${wd(m)}
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
         return ${n}(${Sd(g,u)});
@@ -893,7 +893,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${c}.0, ${p}.0);
         return sampleTexture(${r}, uv);
       }
-    `;let f=Pl(r);return`
+    `;let f=zl(r);return`
     float ${n}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${o} + col * ${i} + depth * ${s} +
@@ -901,7 +901,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index);
       return sampleTexture(${r}, uv);
     }
-  `}function ZQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=w.squeezeShape(t);if(a.length<t.length){let g=Id(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
+  `}function iee(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=w.squeezeShape(t);if(a.length<t.length){let g=Id(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
       ${wd(g)}
       float ${n}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
@@ -942,7 +942,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${c}.0);
         return sampleTexture(${r}, uv);
       }
-    `;let m=Pl(r);return`
+    `;let m=zl(r);return`
     float ${n}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -957,7 +957,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         return ${t}[i];
       }
     }
-  `}function YQ(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=wS(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
+  `}function oee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=TS(e.shapeInfo.logicalShape,t.logicalShape),l=gt(i),u=i-s,d,h=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(g=>`coords.${h[g+u]} = 0;`).join(`
 `);let p="";i<2&&s>0?p="coords":p=e.shapeInfo.logicalShape.map((g,y)=>`coords.${h[y+u]}`).join(", ");let c="return outputValue;",f=w.sizeFromShape(e.shapeInfo.logicalShape)===1,m=w.sizeFromShape(t.logicalShape)===1;if(s===1&&!f&&!m)c=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(f&&!m)i===1?c=`
@@ -971,20 +971,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec4 outputValue = get${n}(${p});
       ${c}
     }
-  `}function JQ(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
+  `}function lee(e,t){let r=e.name,n=r.charAt(0).toUpperCase()+r.slice(1),a="get"+n+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===l&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,s))return`
       float ${a}() {
         return sampleTexture(${r}, resultUV);
       }
-    `;let u=gt(l),d=wS(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(m=>`coords.${c[m+h]} = 0;`).join(`
+    `;let u=gt(l),d=TS(e.shapeInfo.logicalShape,t.logicalShape),h=l-o,p,c=["x","y","z","w","u","v"];o===0?p="":l<2&&d.length>=1?p="coords = 0;":p=d.map(m=>`coords.${c[m+h]} = 0;`).join(`
 `);let f="";return l<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${c[g+h]}`).join(", "),`
     float ${a}() {
       ${u} coords = getOutputCoords();
       ${p}
       return get${n}(${f});
     }
-  `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function lb(e,t,r){let{newShape:n,keptDims:a}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!w.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function Id(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Sd(e,t){return t.map(r=>e[r]).join(", ")}function QQ(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=yQ(a,i,t),l=eS(e.gl,o),u=e.createProgram(l);return Y().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...SS(e,t,u)}}function SS(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Y().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let f=t.variableNames[c];n[f]=e.getUniformLocation(r,f,p),n[`offset${f}`]=e.getUniformLocation(r,`offset${f}`,p),t.enableShapeUniforms&&(a[`${f}Shape`]=e.getUniformLocation(r,`${f}Shape`,p),s[`${f}TexShape`]=e.getUniformLocation(r,`${f}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,f)=>{i[f]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function Rv(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!w.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function eee(e,t,r,n,a){t.program.enableShapeUniforms||(Rv(t.inShapeInfos,r),Rv([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=lb(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function tee(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=lb(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let v=w.computeStrides(d);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Y().getNumber("WEBGL_VERSION")}`,s}function ln(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var ree=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
+  `}function gt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function db(e,t,r){let{newShape:n,keptDims:a}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!w.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function Id(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Sd(e,t){return t.map(r=>e[r]).join(", ")}function uee(e,t,r,n){let a=r.map((d,h)=>{let p={logicalShape:d.shape,texShape:d.isUniform?null:d.texData.texShape,isUniform:d.isUniform,isPacked:d.isUniform?!1:d.texData.isPacked,flatOffset:null};return d.texData!=null&&d.texData.slice!=null&&d.texData.slice.flatOffset>0&&(p.flatOffset=d.texData.slice.flatOffset),{name:t.variableNames[h],shapeInfo:p}}),s=a.map(d=>d.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=NQ(a,i,t),l=aS(e.gl,o),u=e.createProgram(l);return Y().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:l,source:o,webGLProgram:u,inShapeInfos:s,outShapeInfo:i,...ES(e,t,u)}}function ES(e,t,r){let n={},a={},s={},i=[],o,l,u,d=null,h=null;h=e.getUniformLocation(r,"NAN",!1),Y().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(r,"INFINITY",!1));let p=!1;for(let c=0;c<t.variableNames.length;c++){let f=t.variableNames[c];n[f]=e.getUniformLocation(r,f,p),n[`offset${f}`]=e.getUniformLocation(r,`offset${f}`,p),t.enableShapeUniforms&&(a[`${f}Shape`]=e.getUniformLocation(r,`${f}Shape`,p),s[`${f}TexShape`]=e.getUniformLocation(r,`${f}TexShape`,p))}return t.enableShapeUniforms&&(o=e.getUniformLocation(r,"outShape",p),u=e.getUniformLocation(r,"outShapeStrides",p),l=e.getUniformLocation(r,"outTexShape",p)),t.customUniforms&&t.customUniforms.forEach((c,f)=>{i[f]=e.getUniformLocation(r,c.name,p)}),{uniformLocations:n,customUniformLocations:i,infLoc:d,nanLoc:h,inShapesLocations:a,inTexShapesLocations:s,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}}function Mv(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!w.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function dee(e,t,r,n,a){t.program.enableShapeUniforms||(Mv(t.inShapeInfos,r),Mv([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=db(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function pee(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=db(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let v=w.computeStrides(d);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${Y().getNumber("WEBGL_VERSION")}`,s}function ln(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var hee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?g0(["r","c","d"],e):$l(["r","c","d"],e)}
+        ${this.enableShapeUniforms?x0(["r","c","d"],e):_l(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1002,9 +1002,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},nee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
+    `}},cee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?g0(["r","c","d"],e):$l(["r","c","d"],e)}
+        ${this.enableShapeUniforms?x0(["r","c","d"],e):_l(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1022,23 +1022,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},aee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Gr();this.outputShape=e,this.userCode=`
-      ${vS}
+    `}},fee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=Gr();this.outputShape=e,this.userCode=`
+      ${SS}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},see=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Gr();this.outputShape=e,this.userCode=`
-      ${vS}
+    `}},mee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=Gr();this.outputShape=e,this.userCode=`
+      ${SS}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},iee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?ob():ib(e)}
+    `}},gee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
+      ${this.enableShapeUniforms?ub():lb(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1067,7 +1067,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${r.output} = vec4(${n}, 0., 0., 0.);
       }
-    `}},oee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let n="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
+    `}},yee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Gr();this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let n="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let i=0;i<=1;i++){let o=s*2+i;n+=`
           localCoords = coords;
           if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
           localCoords[2] += ${i};
@@ -1096,7 +1096,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?ob():ib(e)}
+        ${this.enableShapeUniforms?ub():lb(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1111,7 +1111,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${r.output} = ${a};
         }
-    `}},TS={};Le(TS,{bindVertexProgramAttributeStreams:()=>_S,createBufferFromOutputTexture:()=>DS,createFloat16MatrixTexture:()=>MS,createFloat16PackedMatrixTexture:()=>PS,createFloat32MatrixTexture:()=>RS,createIndexBuffer:()=>ES,createPackedMatrixTexture:()=>$S,createUnsignedBytesMatrixTexture:()=>FS,createVertexBuffer:()=>CS,createVertexShader:()=>NS,downloadByteEncodedFloatMatrixFromOutputTexture:()=>BS,downloadFloat32MatrixFromBuffer:()=>LS,downloadMatrixFromPackedOutputTexture:()=>VS,downloadPackedMatrixFromBuffer:()=>WS,getInternalFormatForFloat16MatrixTexture:()=>db,getInternalFormatForFloat16PackedMatrixTexture:()=>cb,getInternalFormatForFloat32MatrixTexture:()=>ub,getInternalFormatForPackedMatrixTexture:()=>hb,getInternalFormatForUnsignedBytesMatrixTexture:()=>pb,uploadDenseMatrixToTexture:()=>zS,uploadPixelDataToTexture:()=>OS});function NS(e){let t=Gr(),r=`${t.version}
+    `}},RS={};Le(RS,{bindVertexProgramAttributeStreams:()=>LS,createBufferFromOutputTexture:()=>VS,createFloat16MatrixTexture:()=>_S,createFloat16PackedMatrixTexture:()=>DS,createFloat32MatrixTexture:()=>PS,createIndexBuffer:()=>$S,createPackedMatrixTexture:()=>OS,createUnsignedBytesMatrixTexture:()=>zS,createVertexBuffer:()=>FS,createVertexShader:()=>MS,downloadByteEncodedFloatMatrixFromOutputTexture:()=>GS,downloadFloat32MatrixFromBuffer:()=>US,downloadMatrixFromPackedOutputTexture:()=>HS,downloadPackedMatrixFromBuffer:()=>jS,getInternalFormatForFloat16MatrixTexture:()=>hb,getInternalFormatForFloat16PackedMatrixTexture:()=>mb,getInternalFormatForFloat32MatrixTexture:()=>pb,getInternalFormatForPackedMatrixTexture:()=>fb,getInternalFormatForUnsignedBytesMatrixTexture:()=>cb,uploadDenseMatrixToTexture:()=>BS,uploadPixelDataToTexture:()=>WS});function MS(e){let t=Gr(),r=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1120,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return QI(e,r)}function CS(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return nS(e,t)}function ES(e){let t=new Uint16Array([0,1,2,2,1,3]);return aS(e,t)}function zh(e,t,r,n,a,s){iS(t,r);let i=sS(e),o=e.TEXTURE_2D;return we(e,()=>e.bindTexture(o,i)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),we(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),we(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),Y().getNumber("WEBGL_VERSION")===1?we(e,()=>e.texImage2D(o,0,n,t,r,0,a,s,null)):we(e,()=>e.texStorage2D(o,1,n,t,r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[r,t]}}function ub(e){return e.internalFormatFloat}function RS(e,t,r,n){let[a,s]=_h(t,r);return zh(e,a,s,ub(n),n.textureFormatFloat,e.FLOAT)}function db(e){return e.internalFormatHalfFloat}function MS(e,t,r,n){let[a,s]=_h(t,r);return zh(e,a,s,db(n),n.textureFormatFloat,n.textureTypeHalfFloat)}function pb(e){return e.downloadTextureFormat}function FS(e,t,r,n){let[a,s]=_h(t,r);return zh(e,a,s,pb(n),e.RGBA,e.UNSIGNED_BYTE)}function hb(e){return e.internalFormatPackedFloat}function $S(e,t,r,n){let[a,s]=bd(t,r);return zh(e,a,s,hb(n),e.RGBA,e.FLOAT)}function cb(e){return e.internalFormatPackedHalfFloat}function PS(e,t,r,n){let[a,s]=bd(t,r);return zh(e,a,s,cb(n),e.RGBA,n.textureTypeHalfFloat)}function _S(e,t,r){return we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,r)),Hy(e,t,"clipSpacePos",r,3,20,0)&&Hy(e,t,"uv",r,2,20,12)}function zS(e,t,r,n,a,s){we(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,l;a instanceof Uint8Array?(i=new Uint8Array(r*n*4),o=e.UNSIGNED_BYTE,l=e.RGBA):(i=new Float32Array(r*n*4),o=e.FLOAT,l=s.internalFormatPackedFloat),i.set(a),Y().getNumber("WEBGL_VERSION")===2?we(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,r,n,e.RGBA,o,i)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,r,n,0,e.RGBA,o,i)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function OS(e,t,r){we(e,()=>e.bindTexture(e.TEXTURE_2D,t)),r.data instanceof Uint8Array?Y().getNumber("WEBGL_VERSION")===2?we(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,r.width,r.height,e.RGBA,e.UNSIGNED_BYTE,r.data)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,r.width,r.height,0,e.RGBA,e.UNSIGNED_BYTE,r.data)):Y().getNumber("WEBGL_VERSION")===2?we(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,r)):we(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,r)),we(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function DS(e,t,r,n){let a=e.createBuffer();we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,a));let s=4*4*t*r;return we(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),we(e,()=>e.readPixels(0,0,r,t,e.RGBA,e.FLOAT,0)),we(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),a}function LS(e,t,r){let n=e,a=new Float32Array(r);return n.bindBuffer(n.PIXEL_PACK_BUFFER,t),n.getBufferSubData(n.PIXEL_PACK_BUFFER,0,a),n.bindBuffer(n.PIXEL_PACK_BUFFER,null),a}function BS(e,t,r,n){let[a,s]=_h(t,r),i=4,o=new Uint8Array(nQ(t*r,i));return we(e,()=>e.readPixels(0,0,a,s,n.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function WS(e,t,r,n,a,s,i,o){let l=e,u=new Float32Array(aQ(s,i));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function VS(e,t,r){let n=new Float32Array(t*r*4);return we(e,()=>e.readPixels(0,0,r,t,e.RGBA,e.FLOAT,n)),n}var yu=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,m0(t,e)):this.gl=ya(t);let r="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),Y().getNumber("WEBGL_VERSION")===1){let a="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Sp(this.gl,a),Nn(this.gl,s))this.textureHalfFloatExtension=Sp(this.gl,s);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(r),Nn(this.gl,n))this.colorBufferHalfFloatExtension=Sp(this.gl,n);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(r="EXT_color_buffer_float",Nn(this.gl,r))this.colorBufferFloatExtension=this.gl.getExtension(r);else if(Nn(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=CS(this.gl),this.indexBuffer=ES(this.gl),this.framebuffer=oS(this.gl),this.textureConfig=ab(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Sp(this.gl,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.createQuery();return r.beginQuery(n.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,r=this.getQueryTimerExtensionWebGL2();t.endQuery(r.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let r=this.gl;return r.getQueryParameter(e,r.QUERY_RESULT)/1e6}else{let r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),n=r.getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=Aee(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:r}=this.itemsToPoll[t];r()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),sf(this.gl,e,this.framebuffer),this.debug&&Tp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(sf(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tp(this.gl)):Xy(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let r=t();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(e,t,r){this.throwIfDisposed();let n=this.gl;sf(n,e,this.framebuffer),this.debug&&Tp(n),this.outputTexture=e,we(n,()=>n.viewport(0,0,t,r)),we(n,()=>n.scissor(0,0,t,r))}setOutputMatrixWriteRegionDriver(e,t,r,n){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,r,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Aee(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:xee,bincountImpl:qS,bincountReduceImpl:bee,ceilImpl:vee,concatImpl:wee,equalImpl:kee,expImpl:Iee,expm1Impl:See,floorImpl:Tee,gatherNdImpl:Nee,gatherV2Impl:Cee,greaterImpl:Eee,greaterEqualImpl:Ree,lessImpl:Mee,lessEqualImpl:Fee,linSpaceImpl:$ee,logImpl:Pee,maxImpl:_ee,maximumImpl:zee,minimumImpl:Oee,multiplyImpl:Dee,negImpl:Lee,notEqualImpl:Bee,prodImpl:Wee,rangeImpl:Vee,rsqrtImpl:Uee,sigmoidImpl:Gee,simpleAbsImpl:KS,sliceImpl:jee,sparseFillEmptyRowsImpl:Hee,sparseReshapeImpl:qee,sparseSegmentReductionImpl:XS,sqrtImpl:Kee,stridedSliceImpl:Xee,stringNGramsImpl:Zee,stringSplitImpl:Yee,stringToHashBucketFastImpl:Jee,subImpl:Qee,tileImpl:ete,topKImpl:tte,transposeImpl:gb,uniqueImpl:rte}=g0;function ZS(e,t){return["x","y","z","w","u","v"].slice(0,t).map(r=>`${e}.${r}`)}function Lr(e,t){return t===1?[e]:ZS(e,t)}function nte(e,t){if(e===1)return"rc";let r="";for(let n=0;n<e;n++)r+=t[n],n<e-1&&(r+=",");return r}var ate=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=ln(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
             cEdge ? 0. : getA(${t[1]}),
             rEdge ? 0. : getA(${t[2]}),
-            rEdge || cEdge ? 0. : getA(${t[3]})`}},qS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},YS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
         ${a}
         ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1159,8 +1159,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${n>0?"}":""}
       `}this.userCode=`
-      ${Xee(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?ob():ib(e)}
+      ${ste(t,this.enableShapeUniforms)}
+      ${this.enableShapeUniforms?ub():lb(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1175,12 +1175,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Xee(e,t){return`
+    `}};function ste(e,t){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t?gQ(["r","c","d"],"inputShape"):$l(["r","c","d"],e)}
+      ${t?TQ(["r","c","d"],"inputShape"):_l(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var Zee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=Fv(t,r),a=$v(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Mv(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=Fv(r,n),s=$v(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Mv(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Yee(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Mv(e,t,r,n,a){let s=Jee(t,n),i;if(a){let[l,u]=bd(e[0],e[1]);i=l*u}else{let[l,u]=_h(e[0],e[1]);i=l*u}let o=Yee(r,s);return i*o}function Jee(e,t){switch(e){case 3:return hb(t);case 4:return cb(t);case 1:return ub(t);case 0:return db(t);case 2:return pb(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Qee(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function Fv(e,t){if(e===1)return 3;if(e===0||e==null)return Qee(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function $v(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Ka=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
+  `}var ite=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=$v(t,r),a=Pv(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=Fv(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=$v(r,n),s=Pv(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=Fv(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function ote(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Fv(e,t,r,n,a){let s=lte(t,n),i;if(a){let[l,u]=bd(e[0],e[1]);i=l*u}else{let[l,u]=_h(e[0],e[1]);i=l*u}let o=ote(r,s);return i*o}function lte(e,t){switch(e){case 3:return fb(t);case 4:return mb(t);case 1:return pb(t);case 0:return hb(t);case 2:return cb(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function ute(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function $v(e,t){if(e===1)return 3;if(e===0||e==null)return ute(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function Pv(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Ka=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1191,11 +1191,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Xn="if (isnan(x)) return x;",ete="return x;",Pv="return abs(x);",tte="return (x >= 0.0) ? x : (exp(x) - 1.0);",rte=Xn+`
+    `}},Xn="if (isnan(x)) return x;",dte="return x;",_v="return abs(x);",pte="return (x >= 0.0) ? x : (exp(x) - 1.0);",hte=Xn+`
   return (x < 0.0) ? 0.0 : x;
-`,nte=Xn+`
+`,cte=Xn+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,iu="return x;",ate="return 1.0 / (1.0 + exp(-1.0 * x));",ste="return x;",ite=`
+`,ou="return x;",fte="return 1.0 / (1.0 + exp(-1.0 * x));",mte="return x;",gte=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -1204,7 +1204,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,ote=`
+`,yte=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1214,7 +1214,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,lte=`
+`,Ate=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1224,7 +1224,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,ute="return 1.0 / (1.0 + exp(-1.0 * x));",vo=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
+`,xte="return 1.0 / (1.0 + exp(-1.0 * x));",Io=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -1235,17 +1235,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},dte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let t=e.length,r=Lr("rc",t),n=gt(t),a=qee(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
+    `}},bte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let t=e.length,r=Lr("rc",t),n=gt(t),a=nte(t,r),s=r.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
       void main() {
         ${n} rc = getOutputCoords();
         vec4 packedInput = getA(${a});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},pte=qn.whereImpl,hte=1e-7,cte=1e-4,sy={};function fte(e){return e in sy||(sy[e]={}),sy[e]}var mte=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),gte=600;function yte(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*gte/1024/1024}var KS=class extends Fu{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof yu)t=e;else{let r=ya(Y().getNumber("WEBGL_VERSION"),e);t=new yu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=ya(Y().getNumber("WEBGL_VERSION"));t=new yu(r),this.binaryCache=fte(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Zee(this.gpgpu),this.numMBBeforeWarning=yte(),this.texData=new qp(this,br())}nextDataId(){return KS.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new vo(i,iu):h=new Ka(i,iu);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=w.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new vo(n,iu):c=new Ka(n,iu);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Yc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=w.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&br().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new vo(a,iu):p=new Ka(a,iu);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=br().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!YI(r))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Yc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Y().getBool("WEBGL_PACK")&&n===!0,i=s?of(t):t,o=s?new see(i):new aee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=mte){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return pte(e.shape,t)}packedUnaryOp(e,t,r){let n=new vo(e.shape,t),a=this.compileAndRun(n,[e],r);return br().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=GS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Pv,e.dtype);let t=new Ka(e.shape,Pv),r=this.compileAndRun(t,[e]);return br().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return br().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new dte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Kee(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Po(e.shape),..._o(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Po(t),..._o(t)],s=new qS(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=w.sizeFromShape(a),p=t[0]*t[1]*4;w.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=of(a),o;n?o=new nee(i):o=new ree(i);let l=!0,u=[t!=null?t:Yc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Yc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!jp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=tee(e,u,d),p=this.getAndSaveBinary(h,()=>QQ(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),Y().get("ENGINE_COMPILE_ONLY")||eee(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=Y().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?hte:cte}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=fS(r,o),t.texShape=d),a!=null){let h=of(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=bd(d[0],d[1])),o?p=new oee(h,m):p=new iee(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,v=this.runWebGLProgram(p,[y],n,x,b),S=this.texData.get(v.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,Y().get("ENGINE_COMPILE_ONLY")?this.disposeData(v.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(v.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=w.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=Ate(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await hA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(sb(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=SS(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Oh=KS;Oh.nextDataId=0;function Ate(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var xte="0.0.0";function XS(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}fh.isBrowser()&&Tl("webgl",()=>new Oh,2);var bte={forceHalfFloat:XS},ZS=`
+    `}},vte=qn.whereImpl,wte=1e-7,kte=1e-4,oy={};function Ite(e){return e in oy||(oy[e]={}),oy[e]}var Ste=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Tte=600;function Nte(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*Tte/1024/1024}var JS=class extends $u{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Au)t=e;else{let r=ya(Y().getNumber("WEBGL_VERSION"),e);t=new Au(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=ya(Y().getNumber("WEBGL_VERSION"));t=new Au(r),this.binaryCache=Ite(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new ite(this.gpgpu),this.numMBBeforeWarning=Nte(),this.texData=new qp(this,br())}nextDataId(){return JS.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new Io(i,ou):h=new Ka(i,ou);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=w.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new Io(n,ou):c=new Ka(n,ou);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Yc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=w.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&br().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new Io(a,ou):p=new Ka(a,ou);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=br().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!tS(r))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=w.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Yc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=Y().getBool("WEBGL_PACK")&&n===!0,i=s?of(t):t,o=s?new mee(i):new fee(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Ste){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return vte(e.shape,t)}packedUnaryOp(e,t,r){let n=new Io(e.shape,t),a=this.compileAndRun(n,[e],r);return br().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=KS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,_v,e.dtype);let t=new Ka(e.shape,_v),r=this.compileAndRun(t,[e]);return br().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return br().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new bte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new ate(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Oo(e.shape),...Do(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Oo(t),...Do(t)],s=new YS(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=w.sizeFromShape(a),p=t[0]*t[1]*4;w.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=of(a),o;n?o=new cee(i):o=new hee(i);let l=!0,u=[t!=null?t:Yc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Yc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!jp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=pee(e,u,d),p=this.getAndSaveBinary(h,()=>uee(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),Y().get("ENGINE_COMPILE_ONLY")||dee(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=Y().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?wte:kte}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=AS(r,o),t.texShape=d),a!=null){let h=of(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=bd(d[0],d[1])),o?p=new yee(h,m):p=new gee(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,v=this.runWebGLProgram(p,[y],n,x,b),S=this.texData.get(v.dataId);t.texShape=S.texShape,t.isPacked=S.isPacked,t.usage=S.usage,Y().get("ENGINE_COMPILE_ONLY")?this.disposeData(v.dataId):(t.texture=S.texture,t.values=null,this.texData.delete(v.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=w.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=Cte(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let r=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(a){throw a}});e.push(r)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await fA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ob(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:r,infLoc:n,nanLoc:a,inShapesLocations:s,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}=ES(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=r,e.infLoc=n,e.nanLoc=a,e.inShapesLocations=s,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}},Oh=JS;Oh.nextDataId=0;function Cte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var Ete="0.0.0";function QS(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}fh.isBrowser()&&Cl("webgl",()=>new Oh,2);var Rte={forceHalfFloat:QS},e8=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,Mu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
+`,Fu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=ln(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1255,7 +1255,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},y0=`
+    `}},b0=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
@@ -1304,21 +1304,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function an(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var vte={kernelName:pi,backendName:"webgl",kernelFunc:an};function Vi(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=an({inputs:{x:n},backend:r}),l=an({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var wte={kernelName:Xp,backendName:"webgl",kernelFunc:Vi},YS="return (a < 0.) ? b * a : a;",JS=`
+    `}};function an(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Mte={kernelName:ci,backendName:"webgl",kernelFunc:an};function ji(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=an({inputs:{x:n},backend:r}),l=an({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Fte={kernelName:Xp,backendName:"webgl",kernelFunc:ji},t8="return (a < 0.) ? b * a : a;",r8=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function kte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dh(JS,a.shape,i.shape):new Mu(YS,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var Ite={kernelName:hi,backendName:"webgl",kernelFunc:kte},QS="return (a < 0.) ? b * a : a;",e8=`
+`;function $te(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dh(r8,a.shape,i.shape):new Fu(t8,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var Pte={kernelName:fi,backendName:"webgl",kernelFunc:$te},n8="return (a < 0.) ? b * a : a;",a8=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function Ste(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dh(e8,n.shape,a.shape):new Mu(QS,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var Tte={kernelName:Ii,backendName:"webgl",kernelFunc:Ste},Td="if (isnan(x)) return x;",Nte=`
+`;function _te(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dh(a8,n.shape,a.shape):new Fu(n8,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var zte={kernelName:Ti,backendName:"webgl",kernelFunc:_te},Td="if (isnan(x)) return x;",Ote=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,Cte=`
+`,Dte=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new vo(i.shape,t):d=new Ka(i.shape,e),o.runWebGLProgram(d,[i],l)}}function wr({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new Mu(e,l.shape,u.shape);return d.runWebGLProgram(E,[S,T],Cr(b.dtype,v.dtype))}),A=Vi({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Cr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),v=d.texData.get(b.dataId);return v.values=A,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Dh(t,l.shape,u.shape,r):c=new Mu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function A0(e,t=!1){if(e==="linear")return t?ste:ete;if(e==="relu")return t?ote:rte;if(e==="elu")return t?ite:tte;if(e==="relu6")return t?lte:nte;if(e==="prelu")return t?e8:QS;if(e==="leakyrelu")return t?JS:YS;if(e==="sigmoid")return t?ute:ate;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var t8=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=ln(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new Io(i.shape,t):d=new Ka(i.shape,e),o.runWebGLProgram(d,[i],l)}}function wr({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,S={dataId:b.dataId,dtype:b.dtype,shape:l.shape},T={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new Fu(e,l.shape,u.shape);return d.runWebGLProgram(E,[S,T],Cr(b.dtype,v.dtype))}),A=ji({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Cr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),v=d.texData.get(b.dataId);return v.values=A,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Dh(t,l.shape,u.shape,r):c=new Fu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function v0(e,t=!1){if(e==="linear")return t?mte:dte;if(e==="relu")return t?yte:hte;if(e==="elu")return t?gte:pte;if(e==="relu6")return t?Ate:cte;if(e==="prelu")return t?a8:n8;if(e==="leakyrelu")return t?r8:t8;if(e==="sigmoid")return t?xte:fte;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var s8=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=ln(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1357,7 +1357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},_v={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},zv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
+    `}},zv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Ov=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1370,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},Ov="return a * b;";function mb(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new zv(_v.REAL,n.shape,a.shape),d=new zv(_v.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Vi({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=Cee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Dh(Ov,n.shape,a.shape):i=new Mu(Ov,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var Ete={kernelName:vi,backendName:"webgl",kernelFunc:mb};function Rte(e,t,r){let n=[Po(e.shape),..._o(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Po(t),..._o(t)],i=new qS(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=w.sizeFromShape(a.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!jp(a.shape,l)&&!(d.texture!==null&&jp(d.shape,l))?Rte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var Mte={kernelName:ul,backendName:"webgl",kernelFunc:ve},Dv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
+    `}},Dv="return a * b;";function yb(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new Ov(zv.REAL,n.shape,a.shape),d=new Ov(zv.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=ji({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=Dee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Dh(Dv,n.shape,a.shape):i=new Fu(Dv,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var Lte={kernelName:ki,backendName:"webgl",kernelFunc:yb};function Bte(e,t,r){let n=[Oo(e.shape),...Do(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Oo(t),...Do(t)],i=new YS(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=w.sizeFromShape(a.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!jp(a.shape,l)&&!(d.texture!==null&&jp(d.shape,l))?Bte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var Wte={kernelName:pl,backendName:"webgl",kernelFunc:ve},Lv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1423,7 +1423,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},Fte=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(r/4)*4,d=r%4,h=`
+    `}},Vte=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(r/4)*4,d=r%4,h=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1515,12 +1515,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function $te(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function _l(e,t,r,n){let a=$te(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new Dv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Dv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new Fte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Pte=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=gt(this.rank),a=_te(t);this.userCode=`
+    `}};function Ute(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Ol(e,t,r,n){let a=Ute(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new Lv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Lv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new Vte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Gte=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=gt(this.rank),a=jte(t);this.userCode=`
     void main() {
       ${n} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function _te(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var zte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),a=HS("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function jte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var Hte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=gt(this.rank),a=ZS("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${n} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1537,14 +1537,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function x0(e,t,r){let n=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zte(e.shape,t):new Pte(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function Ote(e,t,r,n){let a=t,s=e.shape.length,i=w.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=x0(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=w.sizeFromShape(p),m=w.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=ch(e.dtype),A=_l(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function b0(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Ote(a,s,i,r)}var Dte={kernelName:Fi,backendName:"webgl",kernelFunc:b0};function vr(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=fb(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=x0(a,s,i);return u}var Lte={kernelName:Oi,backendName:"webgl",kernelFunc:vr},r8=1e3;function Of({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=Sl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],S=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),T=ve({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,T],R=Math.max(y,A),_=r?S.shape[1]:S.shape[2],M=s!=null,I=i!=null,z=l==="leakyrelu",O=l!=null?A0(l,!0):null,j=M||I||z||O!=null,X;if((c===1||f===1)&&_>r8&&j===!1){let Q=S,V=T;r&&(Q=vr({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=vr({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,J=f===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(se));let Z=f===1?2:1,ae=V;J&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=mb({inputs:{a:se,b:ae},backend:a});X=b0({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Cr(e.dtype,t.dtype),V=new t8(b,v,[R,c,f],r,n,M,O,I,z),ee=[S,T];if(s!=null&&ee.push(s),I&&ee.push(i),z){let J=a.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(J),E.push(J)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function Bte(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Of({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Wte={kernelName:Ms,backendName:"webgl",kernelFunc:Bte},Lv="return abs(x);";function Vte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=GS(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new vo(n.shape,Lv):a=new Ka(n.shape,Lv),r.runWebGLProgram(a,[n],n.dtype)}var Ute={kernelName:Lo,backendName:"webgl",kernelFunc:Vte},Gte=Xn+`
+    `}};function w0(e,t,r){let n=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hte(e.shape,t):new Gte(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function qte(e,t,r,n){let a=t,s=e.shape.length,i=w.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=w0(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=w.sizeFromShape(p),m=w.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=ch(e.dtype),A=Ol(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function k0(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return qte(a,s,i,r)}var Kte={kernelName:_i,backendName:"webgl",kernelFunc:k0};function vr(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=gb(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=w0(a,s,i);return u}var Xte={kernelName:Bi,backendName:"webgl",kernelFunc:vr},i8=1e3;function Bf({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=Nl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],S=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),T=ve({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,T],R=Math.max(y,A),_=r?S.shape[1]:S.shape[2],M=s!=null,I=i!=null,z=l==="leakyrelu",O=l!=null?v0(l,!0):null,j=M||I||z||O!=null,X;if((c===1||f===1)&&_>i8&&j===!1){let Q=S,V=T;r&&(Q=vr({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=vr({inputs:{x:T},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,J=f===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(se));let Z=f===1?2:1,ae=V;J&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=yb({inputs:{a:se,b:ae},backend:a});X=k0({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Cr(e.dtype,t.dtype),V=new s8(b,v,[R,c,f],r,n,M,O,I,z),ee=[S,T];if(s!=null&&ee.push(s),I&&ee.push(i),z){let J=a.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(J),E.push(J)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function Zte(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Bf({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Yte={kernelName:Fs,backendName:"webgl",kernelFunc:Zte},Bv="return abs(x);";function Jte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=KS(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Io(n.shape,Bv):a=new Ka(n.shape,Bv),r.runWebGLProgram(a,[n],n.dtype)}var Qte={kernelName:Vo,backendName:"webgl",kernelFunc:Jte},ere=Xn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,jte=it({opSnippet:Gte}),Hte={kernelName:Pu,backendName:"webgl",kernelFunc:jte},qte=Xn+`
+`,tre=it({opSnippet:ere}),rre={kernelName:_u,backendName:"webgl",kernelFunc:tre},nre=Xn+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,Kte=it({opSnippet:qte}),Xte={kernelName:_u,backendName:"webgl",kernelFunc:Kte},Bv="return a + b;",Zte=wr({opSnippet:Bv,packedOpSnippet:Bv,supportsComplex:!0,cpuKernelImpl:uee}),Yte={kernelName:Ya,backendName:"webgl",kernelFunc:Zte},Jte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,are=it({opSnippet:nre}),sre={kernelName:zu,backendName:"webgl",kernelFunc:are},Wv="return a + b;",ire=wr({opSnippet:Wv,packedOpSnippet:Wv,supportsComplex:!0,cpuKernelImpl:xee}),ore={kernelName:Ja,backendName:"webgl",kernelFunc:ire},lre=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${r.join(`
         `)}
@@ -1552,7 +1552,7 @@ return log(x + sqrt(x * x - 1.0));`,Kte=it({opSnippet:qte}),Xte={kernelName:_u,b
         float result = ${n};
         setOutput(result);
       }
-    `}},Qte=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`vec4 v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+    `}},ure=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`vec4 v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${r.join(`
         `)}
@@ -1560,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,Kte=it({opSnippet:qte}),Xte={kernelName:_u,b
         vec4 result = ${n};
         setOutput(result);
       }
-    `}};function df(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return an({inputs:{x:n[0]},backend:r});if(n.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=df({inputs:n.slice(0,o),backend:r}),u=df({inputs:n.slice(o),backend:r});return df({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Cr(o,l)),s=n.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new Qte(n[0].shape,s):new Jte(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var ere={kernelName:qs,backendName:"webgl",kernelFunc:df};function tre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=_l(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var rre={kernelName:zu,backendName:"webgl",kernelFunc:tre};function nre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=_l(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var are={kernelName:Ou,backendName:"webgl",kernelFunc:nre},sre=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function df(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return an({inputs:{x:n[0]},backend:r});if(n.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=df({inputs:n.slice(0,o),backend:r}),u=df({inputs:n.slice(o),backend:r});return df({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Cr(o,l)),s=n.map(o=>o.shape),i=Y().getBool("WEBGL_PACK")?new ure(n[0].shape,s):new lre(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var dre={kernelName:Ks,backendName:"webgl",kernelFunc:df};function pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Ol(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var hre={kernelName:Ou,backendName:"webgl",kernelFunc:pre};function cre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Ol(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var fre={kernelName:Du,backendName:"webgl",kernelFunc:cre},mre=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1580,7 +1580,7 @@ return log(x + sqrt(x * x - 1.0));`,Kte=it({opSnippet:qte}),Xte={kernelName:_u,b
         }
         setOutput(float(bestIndex));
       }
-    `}},ire=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=gt(o),u=Lr("coords",o),d,h;if(s===1){h=o+1;let T=gt(h);d=`
+    `}},gre=class{constructor(e,t,r,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${r.charAt(0).toUpperCase()+r.slice(1)} supports only inputs with rank above 2.`);let a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,l=gt(o),u=Lr("coords",o),d,h;if(s===1){h=o+1;let T=gt(h);d=`
         ${T} sourceLocR = ${T}(${u.join()}, 0);
         ++${u[o-1]};
         ${T} sourceLocG = ${T}(${u.join()}, 0);
@@ -1642,23 +1642,23 @@ return log(x + sqrt(x * x - 1.0));`,Kte=it({opSnippet:qte}),Xte={kernelName:_u,b
         }
         setOutput(bestIndex);
       }
-    `}};function n8(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new sre(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=n8(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function a8(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new ire(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=a8(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function s8(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=w.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=n8(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return a8(e,t,n)}function ore(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=s8(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var lre={kernelName:Ks,backendName:"webgl",kernelFunc:ore};function ure(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=s8(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var dre={kernelName:Du,backendName:"webgl",kernelFunc:ure},pre=Xn+`
+    `}};function o8(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new mre(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=o8(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function l8(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new gre(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=l8(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function u8(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=w.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=o8(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return l8(e,t,n)}function yre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=u8(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Are={kernelName:Xs,backendName:"webgl",kernelFunc:yre};function xre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=vr({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=u8(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var bre={kernelName:Lu,backendName:"webgl",kernelFunc:xre},vre=Xn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,hre=it({opSnippet:pre}),cre={kernelName:Lu,backendName:"webgl",kernelFunc:hre},fre=Xn+"return log(x + sqrt(x * x + 1.0));",mre=it({opSnippet:fre}),gre={kernelName:Bu,backendName:"webgl",kernelFunc:mre},yre=Xn+`
+`,wre=it({opSnippet:vre}),kre={kernelName:Bu,backendName:"webgl",kernelFunc:wre},Ire=Xn+"return log(x + sqrt(x * x + 1.0));",Sre=it({opSnippet:Ire}),Tre={kernelName:Wu,backendName:"webgl",kernelFunc:Sre},Nre=Xn+`
   return atan(x);
-`,Are=it({opSnippet:yre}),xre={kernelName:Wu,backendName:"webgl",kernelFunc:Are},bre=Nte+`
+`,Cre=it({opSnippet:Nre}),Ere={kernelName:Vu,backendName:"webgl",kernelFunc:Cre},Rre=Ote+`
   return atan(a, b);
-`,vre=`
+`,Mre=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+Cte+`
+  `+Dte+`
   return result;
-`,wre=wr({opSnippet:bre,packedOpSnippet:vre}),kre={kernelName:Uu,backendName:"webgl",kernelFunc:wre},Ire=Xn+`
+`,Fre=wr({opSnippet:Rre,packedOpSnippet:Mre}),$re={kernelName:Gu,backendName:"webgl",kernelFunc:Fre},Pre=Xn+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernelName:Vu,backendName:"webgl",kernelFunc:Sre},Hp=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let T=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,_re=it({opSnippet:Pre}),zre={kernelName:Uu,backendName:"webgl",kernelFunc:_re},Hp=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let T=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${p}, ${c});
 
@@ -1799,7 +1799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(${x});
       }
-    `}},gb=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
+    `}},Ab=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${m}, ${g}, ${y});
@@ -1962,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
           setOutput(${v});
         }
       }
-    `}};function Nre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;vd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return an({inputs:{x:a},backend:r});let h=new Hp(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var Cre={kernelName:Xs,backendName:"webgl",kernelFunc:Nre};function Ere(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new gb(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var Rre={kernelName:Kp,backendName:"webgl",kernelFunc:Ere},Mre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
+    `}};function Ore(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;vd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return an({inputs:{x:a},backend:r});let h=new Hp(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var Dre={kernelName:Zs,backendName:"webgl",kernelFunc:Ore};function Lre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new Ab(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var Bre={kernelName:Kp,backendName:"webgl",kernelFunc:Lre},Wre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
       const ivec2 pads = ivec2(${u}, ${d});
       const float avgMultiplier = float(${h});
 
@@ -2004,7 +2004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}},Fre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
+    `}},Vre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=d-1-e.padInfo.front,f=h-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*r*n);this.userCode=`
       const ivec3 pads = ivec3(${c}, ${f}, ${m});
       const float avgMultiplier = float(${g});
 
@@ -2060,7 +2060,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function $re(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Fre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Pre={kernelName:Hf,backendName:"webgl",kernelFunc:$re};function _re(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;vd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new Mre(d);return r.runWebGLProgram(h,[a],i.dtype)}var zre={kernelName:jf,backendName:"webgl",kernelFunc:_re};function Ore(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Of({a,b:s,transposeA:i,transposeB:o,backend:r})}var Dre={kernelName:Zs,backendName:"webgl",kernelFunc:Ore},Lre=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function Ure(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Vre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Gre={kernelName:Xf,backendName:"webgl",kernelFunc:Ure};function jre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;vd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new Wre(d);return r.runWebGLProgram(h,[a],i.dtype)}var Hre={kernelName:Kf,backendName:"webgl",kernelFunc:jre};function qre(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Bf({a,b:s,transposeA:i,transposeB:o,backend:r})}var Kre={kernelName:Ys,backendName:"webgl",kernelFunc:qre},Xre=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2070,7 +2070,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         float inv = scale * inversesqrt(variance + float(${s}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},Bre=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},Zre=class{constructor(e,t,r,n,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -2083,7 +2083,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},Wre=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;w.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Bre(n.shape,a.shape,s.shape,d,h,l):new Lre(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Vre={kernelName:ui,backendName:"webgl",kernelFunc:Wre},Ure=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Gre(this.rank),n,a=e.map((s,i)=>`sourceLoc.${Zy[i]} = start[${i}] + coords.${Zy[i]};`);n=`
+    `}},Yre=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;w.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Zre(n.shape,a.shape,s.shape,d,h,l):new Xre(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Jre={kernelName:pi,backendName:"webgl",kernelFunc:Yre},Qre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=gt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=ene(this.rank),n,a=e.map((s,i)=>`sourceLoc.${Jy[i]} = start[${i}] + coords.${Jy[i]};`);n=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${a.join(`
@@ -2093,7 +2093,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${n}
         setOutput(getSource(${r}));
       }
-    `}},Zy=["x","y","z","w","u","v"];function Gre(e){if(e===1)return"sourceLoc";if(e<=6)return Zy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var jre=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),r=Lr("coords",this.rank),n=Lr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
+    `}},Jy=["x","y","z","w","u","v"];function ene(e){if(e===1)return"sourceLoc";if(e<=6)return Jy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var tne=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=gt(this.rank),r=Lr("coords",this.rank),n=Lr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
         ++${n[this.rank-1]};
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${o}
         setOutput(result);
       }
-    `}};function Hre(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=_t.computeFlatOffset(t,w.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Nd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=_ee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=_t.isSliceContinous(a.shape,o,l);if(u||!d){let h=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new jre(l):new Ure(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Hre(a,o,l,r)}var qre={kernelName:fl,backendName:"webgl",kernelFunc:Nd},Kre=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=vr({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=Nd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},Xre={kernelName:Bo,backendName:"webgl",kernelFunc:Kre};function Zre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=US(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var Yre={kernelName:qf,backendName:"webgl",kernelFunc:Zre};function Jre(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Qre={kernelName:Kf,backendName:"webgl",kernelFunc:Jre},ene="return float(a != b);",i8=wr({opSnippet:ene,cpuKernelImpl:Ree,dtype:"bool"}),tne={kernelName:rl,backendName:"webgl",kernelFunc:i8};function Lh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return an({inputs:{x:a.complexTensorInfos.real},backend:r})}var rne={kernelName:ah,backendName:"webgl",kernelFunc:Lh},nne="return float(int(x));";function ane(e,t){let r=new Ka(e.shape,nne),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Yy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return an({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Yy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Vi({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Lh({inputs:{input:a},backend:r}),o=Yy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=an({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return ane(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=i8({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var sne={kernelName:Ys,backendName:"webgl",kernelFunc:Yy},Wv="return ceil(x);",ine=it({opSnippet:Wv,packedOpSnippet:Wv,cpuKernelImpl:pee}),one={kernelName:Js,backendName:"webgl",kernelFunc:ine},lne=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function rne(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=_t.computeFlatOffset(t,w.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function Nd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=jee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=_t.isSliceContinous(a.shape,o,l);if(u||!d){let h=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tne(l):new Qre(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),rne(a,o,l,r)}var nne={kernelName:gl,backendName:"webgl",kernelFunc:Nd},ane=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=vr({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=Nd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},sne={kernelName:Uo,backendName:"webgl",kernelFunc:ane};function ine(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=qS(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var one={kernelName:Zf,backendName:"webgl",kernelFunc:ine};function lne(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var une={kernelName:Yf,backendName:"webgl",kernelFunc:lne},dne="return float(a != b);",d8=wr({opSnippet:dne,cpuKernelImpl:Bee,dtype:"bool"}),pne={kernelName:sl,backendName:"webgl",kernelFunc:d8};function Lh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return an({inputs:{x:a.complexTensorInfos.real},backend:r})}var hne={kernelName:ah,backendName:"webgl",kernelFunc:Lh},cne="return float(int(x));";function fne(e,t){let r=new Ka(e.shape,cne),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Qy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return an({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Qy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=ji({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Lh({inputs:{input:a},backend:r}),o=Qy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=an({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return fne(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=d8({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var mne={kernelName:Js,backendName:"webgl",kernelFunc:Qy},Vv="return ceil(x);",gne=it({opSnippet:Vv,packedOpSnippet:Vv,cpuKernelImpl:vee}),yne={kernelName:Qs,backendName:"webgl",kernelFunc:gne},Ane=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
 
       void main() {
         float value = getAAtOutCoords();
@@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}},une=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}},xne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
       void main() {
         vec4 value = getAAtOutCoords();
 
@@ -2144,7 +2144,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}};function dne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;Y().getBool("WEBGL_PACK_CLIP")?o=new une(a.shape):o=new lne(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var pne={kernelName:Ja,backendName:"webgl",kernelFunc:dne},hne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}};function bne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o;Y().getBool("WEBGL_PACK_CLIP")?o=new xne(a.shape):o=new Ane(a.shape);let l=[[s],[i]];return r.runWebGLProgram(o,[a],a.dtype,l)}var vne={kernelName:Qa,backendName:"webgl",kernelFunc:bne},wne=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -2157,7 +2157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function Vv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function cne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new hne(n.shape),i=[Vv(n,a.complexTensorInfos.real),Vv(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var fne={kernelName:Zp,backendName:"webgl",kernelFunc:cne},mne=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
+    `}};function Uv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function kne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new wne(n.shape),i=[Uv(n,a.complexTensorInfos.real),Uv(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Ine={kernelName:Zp,backendName:"webgl",kernelFunc:kne},Sne=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${r.join(`
         `)}
       }
-    `}},gne=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=gt(n),s=Lr("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
+    `}},Tne=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let r=this.outputShape,n=r.length,a=gt(n),s=Lr("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let l=i[t],u=i.slice(-2),d=i.join(),h=`if (${l} < ${o[0]}) {
         return getChannel(
             getT0(${d}), vec2(${u.join()}));
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
@@ -2203,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(result);
       }
-    `}};function Qc(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function v0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return an({inputs:{x:a.complexTensorInfos.imag},backend:r})}var yne={kernelName:eh,backendName:"webgl",kernelFunc:v0};function hu(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Lh({inputs:{input:m},backend:r})),h=e.map(m=>v0({inputs:{input:m},backend:r})),p=hu(d,t,r),c=hu(h,t,r),f=Vi({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=hee(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=hu(e.slice(0,d),t,r),p=hu(e.slice(d),t,r),c=hu([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new gne(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=Ane(e,t,r),o=new mne(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function Ane(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function o8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return an({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),hu(o,s,r)}var xne={kernelName:Wo,backendName:"webgl",kernelFunc:o8},l8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(n?x=`float activation(float a) {
+    `}};function Qc(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function I0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return an({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Nne={kernelName:eh,backendName:"webgl",kernelFunc:I0};function cu(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Lh({inputs:{input:m},backend:r})),h=e.map(m=>I0({inputs:{input:m},backend:r})),p=cu(d,t,r),c=cu(h,t,r),f=ji({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=wee(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=cu(e.slice(0,d),t,r),p=cu(e.slice(d),t,r),c=cu([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new Tne(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=Cne(e,t,r),o=new Sne(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function Cne(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function p8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return an({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),cu(o,s,r)}var Ene={kernelName:Go,backendName:"webgl",kernelFunc:p8},h8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(n?x=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?x=`float activation(float a) {
@@ -2337,7 +2337,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${b}
         setOutput(result);
       }
-    `}},bne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},Rne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,r=e.padInfo.top,n=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,d=e.filterDepth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${a}, ${s}, ${i});
       const ivec3 pads = ivec3(${t}, ${r}, ${n});
 
@@ -2425,7 +2425,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}},vne=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let{dataFormat:r}=t,n=Gr(),a=r==="channelsLast",s=a?0:1,i=a?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
+    `}},Mne=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=ln(this.outputShape.length);let{dataFormat:r}=t,n=Gr(),a=r==="channelsLast",s=a?0:1,i=a?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let d=0;d<=1;d++)l+=`
           blockIndex = rc.y + ${d};
           pos = rc.x + ${u};
 
@@ -2472,7 +2472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
 
         ${n.output} = result;
       }
-    `}};function u8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>r8)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(jp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let S=Of({a:x,b:v,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(S.dataId);w.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=r.outShape,g=an({inputs:{x:S},backend:n}),g.shape=r.outShape,y.push(S)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=Of({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function d8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),S=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(S);let T=new vne(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(T,[v],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,z=o==="leakyrelu",O=o?A0(o,!0):null,j=new t8(_.shape,S.shape,[1,g,r.outChannels],A,x,M,O,I,z),X=[_,S];if(a&&X.push(a),I&&X.push(s),z){let ee=n.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function wne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=u8({x:a,filter:s,convInfo:p,backend:r});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=d8({x:a,filter:s,convInfo:p,backend:r});else{let m=new l8(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var kne={kernelName:Qs,backendName:"webgl",kernelFunc:wne},Ine=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function c8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>i8)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(jp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let S=Bf({a:x,b:v,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),T=n.texData.get(S.dataId);w.assert(T.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,T.shape=r.outShape,g=an({inputs:{x:S},backend:n}),g.shape=r.outShape,y.push(S)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=Bf({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function f8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),S=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(S);let T=new Mne(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(T,[v],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,z=o==="leakyrelu",O=o?v0(o,!0):null,j=new s8(_.shape,S.shape,[1,g,r.outChannels],A,x,M,O,I,z),X=[_,S];if(a&&X.push(a),I&&X.push(s),z){let ee=n.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function Fne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=c8({x:a,filter:s,convInfo:p,backend:r});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=f8({x:a,filter:s,convInfo:p,backend:r});else{let m=new h8(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var $ne={kernelName:ei,backendName:"webgl",kernelFunc:Fne},Pne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2514,7 +2514,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}},Sne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
+    `}},_ne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=r-1-e.padInfo.left,l=s?1:2,u=s?2:3,d=s?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -2567,7 +2567,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}},Tne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},zne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2609,7 +2609,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}},Nne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
+    `}},One=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,r=e.filterHeight,n=e.filterWidth,a=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,l=r-1-e.padInfo.top,u=n-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${o}, ${l}, ${u});
 
       void main() {
@@ -2666,12 +2666,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function Cne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new Ine(p);return r.runWebGLProgram(c,[a,s],"float32")}var Ene={kernelName:Xf,backendName:"webgl",kernelFunc:Cne};function Rne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new Sne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Mne={kernelName:ei,backendName:"webgl",kernelFunc:Rne};function Fne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new bne(u);return r.runWebGLProgram(d,[a,s],"float32")}var $ne={kernelName:Yp,backendName:"webgl",kernelFunc:Fne};function Pne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new Tne(u);return r.runWebGLProgram(d,[a,s],"float32")}var _ne={kernelName:Zf,backendName:"webgl",kernelFunc:Pne};function zne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new Nne(u);return r.runWebGLProgram(d,[a,s],"float32")}var One={kernelName:Yf,backendName:"webgl",kernelFunc:zne},Dne=Td+`
+    `}};function Dne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new Pne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Lne={kernelName:Jf,backendName:"webgl",kernelFunc:Dne};function Bne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new _ne(p);return r.runWebGLProgram(c,[a,s],"float32")}var Wne={kernelName:ti,backendName:"webgl",kernelFunc:Bne};function Vne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new Rne(u);return r.runWebGLProgram(d,[a,s],"float32")}var Une={kernelName:Yp,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new zne(u);return r.runWebGLProgram(d,[a,s],"float32")}var jne={kernelName:Qf,backendName:"webgl",kernelFunc:Gne};function Hne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new One(u);return r.runWebGLProgram(d,[a,s],"float32")}var qne={kernelName:em,backendName:"webgl",kernelFunc:Hne},Kne=Td+`
   return cos(x);
-`,Lne=it({opSnippet:Dne}),Bne={kernelName:ti,backendName:"webgl",kernelFunc:Lne},Wne=`
+`,Xne=it({opSnippet:Kne}),Zne={kernelName:ri,backendName:"webgl",kernelFunc:Xne},Yne=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,Vne=it({opSnippet:Wne}),Une={kernelName:ri,backendName:"webgl",kernelFunc:Vne},Gne=class{constructor(e,t,r,n,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=r;this.outputShape=[u,d,h,l];let p=n==="bilinear"?1:0,[c,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,Jne=it({opSnippet:Yne}),Qne={kernelName:ni,backendName:"webgl",kernelFunc:Jne},eae=class{constructor(e,t,r,n,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,l]=e,[u]=t,[d,h]=r;this.outputShape=[u,d,h,l];let p=n==="bilinear"?1:0,[c,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${c} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${c}`],[A,x,b]=h>1?[`${(o-1)/(h-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${A});
       void main() {
@@ -2732,33 +2732,33 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
           setOutput(newValue);
         }
       }
-    `}},jne=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Gne(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Hne={kernelName:Uo,backendName:"webgl",kernelFunc:jne},Uv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"1.0":`getX(${Gv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
+    `}},tae=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new eae(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},rae={kernelName:Ho,backendName:"webgl",kernelFunc:tae},Gv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"1.0":`getX(${jv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${gt(n)} coords = getOutputCoords();
-        int end = ${jv(n,"coords")};
+        int end = ${Hv(n,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${jv(n,"coords")} = idx;
-          val *= getX(${Gv(n,"coords")});
+          ${Hv(n,"coords")} = idx;
+          val *= getX(${jv(n,"coords")});
         }
         setOutput(val);
       }
-    `}};function Gv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function jv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function qne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=vr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=an({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new Uv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new Uv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=vr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Kne={kernelName:Gu,backendName:"webgl",kernelFunc:qne},Hv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${qv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
+    `}};function jv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function Hv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function nae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=vr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=an({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new Gv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new Gv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=vr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var aae={kernelName:jo,backendName:"webgl",kernelFunc:nae},qv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${Kv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${gt(n)} coords = getOutputCoords();
-        int end = ${Kv(n,"coords")};
+        int end = ${Xv(n,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${Kv(n,"coords")} = idx;
-          val += getX(${qv(n,"coords")});
+          ${Xv(n,"coords")} = idx;
+          val += getX(${Kv(n,"coords")});
         }
         setOutput(val);
       }
-    `}};function qv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Kv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Xne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=vr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=an({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new Hv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new Hv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=vr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Zne={kernelName:Vo,backendName:"webgl",kernelFunc:Xne};function Yne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=US(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=dee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Jne={kernelName:Jf,backendName:"webgl",kernelFunc:Yne},Qne=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
+    `}};function Kv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Xv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function sae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=vr({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=an({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new qv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new qv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=vr({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var iae={kernelName:ai,backendName:"webgl",kernelFunc:sae};function oae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=qS(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=bee(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var lae={kernelName:tm,backendName:"webgl",kernelFunc:oae},uae=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2777,7 +2777,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function eae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new Qne(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var tae={kernelName:Go,backendName:"webgl",kernelFunc:eae},p8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ln(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function dae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new uae(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var pae={kernelName:qo,backendName:"webgl",kernelFunc:dae},m8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ln(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?l=`float activation(float a) {
@@ -2830,7 +2830,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${u}
         setOutput(result);
       }
-    `}},h8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ln(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
+    `}},g8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ln(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
           vec4 xTexelC${g*2};
@@ -3017,7 +3017,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${f}
         setOutput(result);
       }
-    `}};function rae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new h8(h):p=new p8(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var nae={kernelName:ni,backendName:"webgl",kernelFunc:rae},aae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function hae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new g8(h):p=new m8(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var cae={kernelName:si,backendName:"webgl",kernelFunc:hae},fae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3052,7 +3052,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}},sae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},mae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,i=r-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${s}, ${i});
 
       void main() {
@@ -3097,13 +3097,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function iae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new aae(h);return r.runWebGLProgram(p,[a,s],"float32")}var oae={kernelName:Qf,backendName:"webgl",kernelFunc:iae};function lae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new sae(h);return r.runWebGLProgram(p,[a,s],"float32")}var uae={kernelName:em,backendName:"webgl",kernelFunc:lae},dae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function gae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new fae(h);return r.runWebGLProgram(p,[a,s],"float32")}var yae={kernelName:rm,backendName:"webgl",kernelFunc:gae};function Aae(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new mae(h);return r.runWebGLProgram(p,[a,s],"float32")}var xae={kernelName:nm,backendName:"webgl",kernelFunc:Aae},bae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function pae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=w.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new dae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var hae={kernelName:tm,backendName:"webgl",kernelFunc:pae},cae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
+    `}};function vae(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=w.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new bae(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var wae={kernelName:am,backendName:"webgl",kernelFunc:vae},kae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
       const ivec2 pads = ivec2(${d}, ${h});
       const float neg_infinity = -3.4e38;
@@ -3141,7 +3141,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         float result = curVal;
         setOutput(result);
       }
-    `}};function fae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new cae(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var mae={kernelName:Jp,backendName:"webgl",kernelFunc:fae};function gae(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=vr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=mb({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=b0({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var yae={kernelName:Qp,backendName:"webgl",kernelFunc:gae},Aae="return (x >= 0.0) ? x : (exp(x) - 1.0);",xae=`
+    `}};function Iae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new kae(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var Sae={kernelName:Jp,backendName:"webgl",kernelFunc:Iae};function Tae(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=vr({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=yb({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=k0({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var Nae={kernelName:Qp,backendName:"webgl",kernelFunc:Tae},Cae="return (x >= 0.0) ? x : (exp(x) - 1.0);",Eae=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3150,12 +3150,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,bae=it({opSnippet:Aae,packedOpSnippet:xae}),vae={kernelName:si,backendName:"webgl",kernelFunc:bae},wae="return (b >= 1.0) ? a : a * (b + 1.0);",kae=`
+`,Rae=it({opSnippet:Cae,packedOpSnippet:Eae}),Mae={kernelName:oi,backendName:"webgl",kernelFunc:Rae},Fae="return (b >= 1.0) ? a : a * (b + 1.0);",$ae=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,Iae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dh(kae,n.shape,a.shape):new Mu(wae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},Sae={kernelName:rm,backendName:"webgl",kernelFunc:Iae},Tae=`
+`,Pae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Dh($ae,n.shape,a.shape):new Fu(Fae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},_ae={kernelName:sm,backendName:"webgl",kernelFunc:Pae},zae=`
   return vec4(equal(a, b));
-`,Nae="return float(a == b);",Cae=wr({opSnippet:Nae,packedOpSnippet:Tae,dtype:"bool",cpuKernelImpl:cee}),Eae={kernelName:jo,backendName:"webgl",kernelFunc:Cae},Rae=`
+`,Oae="return float(a == b);",Dae=wr({opSnippet:Oae,packedOpSnippet:zae,dtype:"bool",cpuKernelImpl:kee}),Lae={kernelName:Ko,backendName:"webgl",kernelFunc:Dae},Bae=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3170,9 +3170,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,Mae=it({opSnippet:Rae}),Fae={kernelName:ju,backendName:"webgl",kernelFunc:Mae},$ae=Td+`
+`,Wae=it({opSnippet:Bae}),Vae={kernelName:ju,backendName:"webgl",kernelFunc:Wae},Uae=Td+`
   return exp(x);
-`,Pae=`
+`,Gae=`
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -3181,7 +3181,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,c8=it({opSnippet:$ae,packedOpSnippet:Pae,cpuKernelImpl:fee,dtype:"float32"}),_ae={kernelName:ii,backendName:"webgl",kernelFunc:c8};function Jy(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var zae={kernelName:Ho,backendName:"webgl",kernelFunc:Jy},Xv="return exp(x) - 1.0;",Oae=it({opSnippet:Xv,packedOpSnippet:Xv,cpuKernelImpl:mee}),Dae={kernelName:qo,backendName:"webgl",kernelFunc:Oae},Zv=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,y8=it({opSnippet:Uae,packedOpSnippet:Gae,cpuKernelImpl:Iee,dtype:"float32"}),jae={kernelName:li,backendName:"webgl",kernelFunc:y8};function e2(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var Hae={kernelName:Xo,backendName:"webgl",kernelFunc:e2},Zv="return exp(x) - 1.0;",qae=it({opSnippet:Zv,packedOpSnippet:Zv,cpuKernelImpl:See}),Kae={kernelName:Zo,backendName:"webgl",kernelFunc:qae},Yv=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3214,12 +3214,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function f8(e,t,r){let n=r.texData.get(e.dataId),a=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Zv("real",l,t),d=new Zv("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Vi({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function Lae(e){let{inputs:t,backend:r}=e,{input:n}=t;return f8(n,!1,r)}var Bae={kernelName:nm,backendName:"webgl",kernelFunc:Lae},Wae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function A8(e,t,r){let n=r.texData.get(e.dataId),a=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Yv("real",l,t),d=new Yv("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=ji({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function Xae(e){let{inputs:t,backend:r}=e,{input:n}=t;return A8(n,!1,r)}var Zae={kernelName:im,backendName:"webgl",kernelFunc:Xae},Yae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}};function Bh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Wae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Vae={kernelName:Hu,backendName:"webgl",kernelFunc:Bh},Uae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function Bh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Yae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Jae={kernelName:Hu,backendName:"webgl",kernelFunc:Bh},Qae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3233,7 +3233,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
           }
           setOutput(outputValue);
         }
-    `}},Gae={kernelName:Ko,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Uae(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},Yv="return floor(x);",jae=it({opSnippet:Yv,packedOpSnippet:Yv,cpuKernelImpl:gee}),Hae={kernelName:oi,backendName:"webgl",kernelFunc:jae},qae=`
+    `}},ese={kernelName:Yo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Qae(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},Jv="return floor(x);",tse=it({opSnippet:Jv,packedOpSnippet:Jv,cpuKernelImpl:Tee}),rse={kernelName:ui,backendName:"webgl",kernelFunc:tse},nse=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3243,7 +3243,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
   } else {
     return NAN;
   }
-`,Kae=`
+`,ase=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -3264,7 +3264,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,Xae=wr({opSnippet:qae,packedOpSnippet:Kae,dtype:"int32"}),Zae={kernelName:li,backendName:"webgl",kernelFunc:Xae},Yae=class{constructor(e){this.variableNames=["A"];let t=Gr(),[r,n]=e;this.outputShape=e,this.userCode=`
+`,sse=wr({opSnippet:nse,packedOpSnippet:ase,dtype:"int32"}),ise={kernelName:di,backendName:"webgl",kernelFunc:sse},ose=class{constructor(e){this.variableNames=["A"];let t=Gr(),[r,n]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3286,7 +3286,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},Jae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gr(),[r,n]=e;this.outputShape=e,this.userCode=`
+    `}},lse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gr(),[r,n]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3320,7 +3320,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
 
         ${t.output} = result;
       }
-    `}},Qae={kernelName:Pp,backendName:"webgl",kernelFunc:ese},ou;function ese(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(ou==null&&(ou=document.createElement("canvas").getContext("2d")),ou.canvas.width=l,ou.canvas.height=u,ou.drawImage(a,0,0,l,u),a=ou.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Y().getBool("WEBGL_PACK")?new Jae(h):new Yae(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function tse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=u8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=d8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,S=c==="leakyrelu",T=c?A0(c,!1):null,E=new l8(g,b,T,v,S),R=[a,s];if(i&&R.push(i),o&&R.push(o),S){let _=r.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var rse={kernelName:Fs,backendName:"webgl",kernelFunc:tse};function nse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?A0(p,y):null,x=[a,s],b=i!=null,v=o!=null,S=p==="leakyrelu";if(b&&x.push(i),v&&x.push(o),S){let _=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(_),f.push(_)}let T;y?T=new h8(g,b,A,v,S):T=new p8(g,b,A,v,S);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(T,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var ase={kernelName:$s,backendName:"webgl",kernelFunc:nse},sse=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=gt(t.length),a=gt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},use={kernelName:Pp,backendName:"webgl",kernelFunc:dse},lu;function dse(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(lu==null&&(lu=document.createElement("canvas").getContext("2d")),lu.canvas.width=l,lu.canvas.height=u,lu.drawImage(a,0,0,l,u),a=lu.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=Y().getBool("WEBGL_PACK")?new lse(h):new ose(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function pse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=c8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=f8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,S=c==="leakyrelu",T=c?v0(c,!1):null,E=new h8(g,b,T,v,S),R=[a,s];if(i&&R.push(i),o&&R.push(o),S){let _=r.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var hse={kernelName:$s,backendName:"webgl",kernelFunc:pse};function cse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?v0(p,y):null,x=[a,s],b=i!=null,v=o!=null,S=p==="leakyrelu";if(b&&x.push(i),v&&x.push(o),S){let _=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(_),f.push(_)}let T;y?T=new g8(g,b,A,v,S):T=new m8(g,b,A,v,S);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(T,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var fse={kernelName:Ps,backendName:"webgl",kernelFunc:cse},mse=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=gt(t.length),a=gt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${n} strides = ${n}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -3331,24 +3331,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function ise(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=yee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let f=new sse(i,h,[u,d]),m=r.runWebGLProgram(f,[c,p],c.dtype),g=ve({inputs:{x:m},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),g}var ose={kernelName:Zo,backendName:"webgl",kernelFunc:ise},lse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=gt(this.rank),n=use(e,2);this.userCode=`
+      `}};function gse(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=ve({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=ve({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let y=r.readSync(a.dataId),A=r.bufferSync(n),x=Nee(y,A,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,x.values)}let f=new mse(i,h,[u,d]),m=r.runWebGLProgram(f,[c,p],c.dtype),g=ve({inputs:{x:m},backend:r,attrs:{shape:l}});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(m),g}var yse={kernelName:Qo,backendName:"webgl",kernelFunc:gse},Ase=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let r=gt(this.rank),n=xse(e,2);this.userCode=`
       void main() {
         ${r} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${n}));
       }
-    `}};function use(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function m8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0];if(Y().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=Aee(x,A,f);return h.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new lse(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var dse={kernelName:Xo,backendName:"webgl",kernelFunc:m8},pse="return float(a > b);",hse=`
+    `}};function xse(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function x8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0];if(Y().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=Cee(x,A,f);return h.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new Ase(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var bse={kernelName:Jo,backendName:"webgl",kernelFunc:x8},vse="return float(a > b);",wse=`
   return vec4(greaterThan(a, b));
-`,cse=wr({opSnippet:pse,packedOpSnippet:hse,cpuKernelImpl:xee,dtype:"bool"}),fse={kernelName:Yo,backendName:"webgl",kernelFunc:cse},mse="return float(a >= b);",gse=`
+`,kse=wr({opSnippet:vse,packedOpSnippet:wse,cpuKernelImpl:Eee,dtype:"bool"}),Ise={kernelName:el,backendName:"webgl",kernelFunc:kse},Sse="return float(a >= b);",Tse=`
   return vec4(greaterThanEqual(a, b));
-`,yse=wr({opSnippet:mse,packedOpSnippet:gse,dtype:"bool",cpuKernelImpl:bee}),Ase={kernelName:di,backendName:"webgl",kernelFunc:yse};function xse(e){let{inputs:t,backend:r}=e,{input:n}=t;return f8(n,!0,r)}var bse={kernelName:am,backendName:"webgl",kernelFunc:xse},vse="return float(!isnan(x) && !isinf(x));",wse=it({opSnippet:vse,dtype:"bool"}),kse={kernelName:qu,backendName:"webgl",kernelFunc:wse},Ise="return float(isinf(x));",Sse=it({opSnippet:Ise,dtype:"bool"}),Tse={kernelName:Ku,backendName:"webgl",kernelFunc:Sse},Nse="return float(isnan(x));",Cse=it({opSnippet:Nse,dtype:"bool"}),Ese={kernelName:Xu,backendName:"webgl",kernelFunc:Cse},Rse="return float(a < b);",Mse=`
+`,Nse=wr({opSnippet:Sse,packedOpSnippet:Tse,dtype:"bool",cpuKernelImpl:Ree}),Cse={kernelName:hi,backendName:"webgl",kernelFunc:Nse};function Ese(e){let{inputs:t,backend:r}=e,{input:n}=t;return A8(n,!0,r)}var Rse={kernelName:om,backendName:"webgl",kernelFunc:Ese},Mse="return float(!isnan(x) && !isinf(x));",Fse=it({opSnippet:Mse,dtype:"bool"}),$se={kernelName:qu,backendName:"webgl",kernelFunc:Fse},Pse="return float(isinf(x));",_se=it({opSnippet:Pse,dtype:"bool"}),zse={kernelName:Ku,backendName:"webgl",kernelFunc:_se},Ose="return float(isnan(x));",Dse=it({opSnippet:Ose,dtype:"bool"}),Lse={kernelName:Xu,backendName:"webgl",kernelFunc:Dse},Bse="return float(a < b);",Wse=`
   return vec4(lessThan(a, b));
-`,Fse=wr({opSnippet:Rse,packedOpSnippet:Mse,cpuKernelImpl:vee,dtype:"bool"}),$se={kernelName:Jo,backendName:"webgl",kernelFunc:Fse},Pse="return float(a <= b);",_se=`
+`,Vse=wr({opSnippet:Bse,packedOpSnippet:Wse,cpuKernelImpl:Mee,dtype:"bool"}),Use={kernelName:tl,backendName:"webgl",kernelFunc:Vse},Gse="return float(a <= b);",jse=`
   return vec4(lessThanEqual(a, b));
-`,zse=wr({opSnippet:Pse,packedOpSnippet:_se,cpuKernelImpl:wee,dtype:"bool"}),Ose={kernelName:Qo,backendName:"webgl",kernelFunc:zse};function Dse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=kee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var Lse={kernelName:sm,backendName:"webgl",kernelFunc:Dse},Bse=Td+`
+`,Hse=wr({opSnippet:Gse,packedOpSnippet:jse,cpuKernelImpl:Fee,dtype:"bool"}),qse={kernelName:rl,backendName:"webgl",kernelFunc:Hse};function Kse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=$ee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var Xse={kernelName:lm,backendName:"webgl",kernelFunc:Kse},Zse=Td+`
   return x < 0.0 ? 0./0. : log(x);
-`,Wse=`
+`,Yse=`
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -3356,18 +3356,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
   result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
-`,Vse=it({opSnippet:Bse,packedOpSnippet:Wse,cpuKernelImpl:Iee}),Use={kernelName:ci,backendName:"webgl",kernelFunc:Vse},Gse=Td+`
+`,Jse=it({opSnippet:Zse,packedOpSnippet:Yse,cpuKernelImpl:Pee}),Qse={kernelName:mi,backendName:"webgl",kernelFunc:Jse},eie=Td+`
   return log(1.0 + x);
-`,jse=it({opSnippet:Gse}),Hse={kernelName:Zu,backendName:"webgl",kernelFunc:jse},qse="return float(a >= 1.0 && b >= 1.0);",Kse=`
+`,tie=it({opSnippet:eie}),rie={kernelName:Zu,backendName:"webgl",kernelFunc:tie},nie="return float(a >= 1.0 && b >= 1.0);",aie=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,Xse=wr({opSnippet:qse,packedOpSnippet:Kse,dtype:"bool"}),Zse={kernelName:el,backendName:"webgl",kernelFunc:Xse},Yse="return float(!(x >= 1.0));",Jse=it({opSnippet:Yse}),Qse={kernelName:Yu,backendName:"webgl",kernelFunc:Jse},eie="return float(a >= 1.0 || b >= 1.0);",tie=`
+`,sie=wr({opSnippet:nie,packedOpSnippet:aie,dtype:"bool"}),iie={kernelName:nl,backendName:"webgl",kernelFunc:sie},oie="return float(!(x >= 1.0));",lie=it({opSnippet:oie}),uie={kernelName:Yu,backendName:"webgl",kernelFunc:lie},die="return float(a >= 1.0 || b >= 1.0);",pie=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,rie=wr({opSnippet:eie,packedOpSnippet:tie,dtype:"bool"}),nie={kernelName:th,backendName:"webgl",kernelFunc:rie},aie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,hie=wr({opSnippet:die,packedOpSnippet:pie,dtype:"bool"}),cie={kernelName:th,backendName:"webgl",kernelFunc:hie},fie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3386,7 +3386,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         float val = x * ${o};
         setOutput(val);
       }
-    `}},sie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+    `}},mie=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3448,7 +3448,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},iie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new sie(a.shape,s,i,o,l):new aie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},oie={kernelName:rh,backendName:"webgl",kernelFunc:iie},lie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
+    `}},gie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new mie(a.shape,s,i,o,l):new fie(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},yie={kernelName:rh,backendName:"webgl",kernelFunc:gie},Aie=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3503,14 +3503,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
       }
       setOutput(result);
       }
-    `}},uie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new lie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},die={kernelName:im,backendName:"webgl",kernelFunc:uie};function pie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=_l(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function g8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let S=0;S<x.length;S++)x[S]=a.shape[d[S]];let b=fb(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=x0(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=See(A,w.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=pie(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var hie={kernelName:fi,backendName:"webgl",kernelFunc:g8},cie=ZS+`
+    `}},xie=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new Aie(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},bie={kernelName:um,backendName:"webgl",kernelFunc:xie};function vie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ol(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function b8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let S=0;S<x.length;S++)x[S]=a.shape[d[S]];let b=gb(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=w0(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=_ee(A,w.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=vie(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var wie={kernelName:gi,backendName:"webgl",kernelFunc:b8},kie=e8+`
   return max(a, b);
-`,fie=`
+`,Iie=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+y0+`
+  `+b0+`
   return result;
-`,mie=wr({opSnippet:cie,packedOpSnippet:fie,cpuKernelImpl:Tee}),gie={kernelName:mi,backendName:"webgl",kernelFunc:mie};function yie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;vd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return an({inputs:{x:a},backend:r});let h=new Hp(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var Aie={kernelName:gi,backendName:"webgl",kernelFunc:yie};function xie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new gb(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var bie={kernelName:nh,backendName:"webgl",kernelFunc:xie},vie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,Sie=wr({opSnippet:kie,packedOpSnippet:Iie,cpuKernelImpl:zee}),Tie={kernelName:yi,backendName:"webgl",kernelFunc:Sie};function Nie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;vd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return an({inputs:{x:a},backend:r});let h=new Hp(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var Cie={kernelName:Ai,backendName:"webgl",kernelFunc:Nie};function Eie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new Ab(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var Rie={kernelName:nh,backendName:"webgl",kernelFunc:Eie},Mie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3556,7 +3556,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}},wie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
+    `}},Fie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,r=e.strideHeight,n=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,d=o-1-e.padInfo.front,h=l-1-e.padInfo.top,p=u-1-e.padInfo.left,c=o*l*u-1;this.userCode=`
       const ivec3 pads = ivec3(${d}, ${h}, ${p});
 
       void main() {
@@ -3620,14 +3620,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function kie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new gb(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new wie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var Iie={kernelName:lm,backendName:"webgl",kernelFunc:kie};function Sie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;vd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new Hp(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new vie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var Tie={kernelName:om,backendName:"webgl",kernelFunc:Sie};function Nie(e,t,r,n){let a=new Hp(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new Hp(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var Cie={kernelName:um,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=Nie(n,o,d,l);return[h,p]}};function Eie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=_l(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var Rie={kernelName:yi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=w.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let T=0;T<b.length;T++)b[T]=n.shape[d[T]];let v=fb(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let S=i.texData.get(f.dataId);S.values=v}else f=x0(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=Eie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function Mie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=_l(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Fie={kernelName:Ai,backendName:"webgl",kernelFunc:Mie},$ie=ZS+`
+    `}};function $ie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Ab(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new Fie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var Pie={kernelName:pm,backendName:"webgl",kernelFunc:$ie};function _ie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;vd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new Hp(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new Mie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var zie={kernelName:dm,backendName:"webgl",kernelFunc:_ie};function Oie(e,t,r,n){let a=new Hp(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new Hp(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var Die={kernelName:hm,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=Oie(n,o,d,l);return[h,p]}};function Lie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Ol(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var Bie={kernelName:xi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=w.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let T=0;T<b.length;T++)b[T]=n.shape[d[T]];let v=gb(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let S=i.texData.get(f.dataId);S.values=v}else f=w0(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=Lie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function Wie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Ol(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Vie={kernelName:bi,backendName:"webgl",kernelFunc:Wie},Uie=e8+`
   return min(a, b);
-`,Pie=`
+`,Gie=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+y0+`
+  `+b0+`
   return result;
-`,_ie=wr({opSnippet:$ie,packedOpSnippet:Pie,cpuKernelImpl:Nee}),zie={kernelName:xi,backendName:"webgl",kernelFunc:_ie},Oie=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=gt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
+`,jie=wr({opSnippet:Uie,packedOpSnippet:Gie,cpuKernelImpl:Oee}),Hie={kernelName:vi,backendName:"webgl",kernelFunc:jie},qie=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=gt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3656,7 +3656,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${a} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},Die=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,f)=>c[0]+e[f]+c[1]);let n=e.length,a=gt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,f)=>c[0]+e[f]).join(","),o=Lr("rc",n),l=Lr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=r==="reflect"?0:1,p="";if(n===1){let c=`
+    `}},Kie=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((c,f)=>c[0]+e[f]+c[1]);let n=e.length,a=gt(n),s=t.map(c=>c[0]).join(","),i=t.map((c,f)=>c[0]+e[f]).join(","),o=Lr("rc",n),l=Lr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=r==="reflect"?0:1,p="";if(n===1){let c=`
         ${a} source = rc;
         if (source < start) {
           source = start * 2 - source - ${h};
@@ -3712,13 +3712,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         ${p}
         setOutput(result);
       }
-    `}},Lie=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Die(n.shape,a,s):new Oie(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},Bie={kernelName:bi,backendName:"webgl",kernelFunc:Lie},Wie=`if (b == 0.0) return NAN;
-  return mod(a, b);`,Vie=`
+    `}},Xie=({inputs:e,backend:t,attrs:r})=>{let{x:n}=e,{paddings:a,mode:s}=r,i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Kie(n.shape,a,s):new qie(n.shape,a,s);return t.runWebGLProgram(i,[n],n.dtype)},Zie={kernelName:wi,backendName:"webgl",kernelFunc:Xie},Yie=`if (b == 0.0) return NAN;
+  return mod(a, b);`,Jie=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+y0+`
+  `+b0+`
   return result;
-`,Uie=wr({opSnippet:Wie,packedOpSnippet:Vie}),Gie={kernelName:Ju,backendName:"webgl",kernelFunc:Uie},jie=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
+`,Qie=wr({opSnippet:Yie,packedOpSnippet:Jie}),eoe={kernelName:Ju,backendName:"webgl",kernelFunc:Qie},toe=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3738,11 +3738,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Sre=it({opSnippet:Ire}),Tre={kernel
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}},Hie=`
+    `}},roe=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,qie=`
+return a / b;`,noe=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3760,9 +3760,9 @@ return a / b;`,qie=`
   }
 
   return result;
-`,y8=wr({opSnippet:Hie,packedOpSnippet:qie,checkOutOfBounds:!0}),Kie={kernelName:ai,backendName:"webgl",kernelFunc:y8},Jv="return a - b;",A8=wr({opSnippet:Jv,packedOpSnippet:Jv,supportsComplex:!0,cpuKernelImpl:Uee}),Xie={kernelName:_i,backendName:"webgl",kernelFunc:A8};function x8(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=g8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=A8({inputs:{a,b:u},backend:r}),h=c8({inputs:{x:d},backend:r}),p=b0({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=y8({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var Zie={kernelName:$i,backendName:"webgl",kernelFunc:x8};function Yie(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:x8({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new jie(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Jie={kernelName:dm,backendName:"webgl",kernelFunc:Yie},Qie=Xn+`
+`,v8=wr({opSnippet:roe,packedOpSnippet:noe,checkOutOfBounds:!0}),aoe={kernelName:ii,backendName:"webgl",kernelFunc:v8},Qv="return a - b;",w8=wr({opSnippet:Qv,packedOpSnippet:Qv,supportsComplex:!0,cpuKernelImpl:Qee}),soe={kernelName:Di,backendName:"webgl",kernelFunc:w8};function k8(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=b8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=w8({inputs:{a,b:u},backend:r}),h=y8({inputs:{x:d},backend:r}),p=k0({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=v8({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var ioe={kernelName:zi,backendName:"webgl",kernelFunc:k8};function ooe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:k8({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new toe(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var loe={kernelName:cm,backendName:"webgl",kernelFunc:ooe},uoe=Xn+`
   return -x;
-`,eoe=`
+`,doe=`
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -3772,14 +3772,14 @@ return a / b;`,qie=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function toe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=Eee(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new vo(n.shape,eoe):a=new Ka(n.shape,Qie),r.runWebGLProgram(a,[n],n.dtype)}var roe={kernelName:tl,backendName:"webgl",kernelFunc:toe},noe=qn.nonMaxSuppressionV3Impl;function aoe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=noe(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var soe={kernelName:nl,backendName:"webgl",kernelFunc:aoe},ioe=qn.nonMaxSuppressionV4Impl;function ooe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=ioe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var loe={kernelName:Qu,backendName:"webgl",kernelFunc:ooe},uoe=qn.nonMaxSuppressionV5Impl;function doe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=uoe(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var poe={kernelName:al,backendName:"webgl",kernelFunc:doe},hoe=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function poe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=Lee(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new Io(n.shape,doe):a=new Ka(n.shape,uoe),r.runWebGLProgram(a,[n],n.dtype)}var hoe={kernelName:al,backendName:"webgl",kernelFunc:poe},coe=qn.nonMaxSuppressionV3Impl;function foe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=coe(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var moe={kernelName:il,backendName:"webgl",kernelFunc:foe},goe=qn.nonMaxSuppressionV4Impl;function yoe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=goe(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var Aoe={kernelName:Qu,backendName:"webgl",kernelFunc:yoe},xoe=qn.nonMaxSuppressionV5Impl;function boe(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=xoe(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var voe={kernelName:ol,backendName:"webgl",kernelFunc:boe},woe=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${n}), float(${r}),
                       float(index == coords.y)));
       }
-    `}},coe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=w.sizeFromShape(a.shape),u=new hoe(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},foe={kernelName:il,backendName:"webgl",kernelFunc:coe};function Df(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Lh({inputs:{input:n},backend:r}),s=Df({inputs:{x:a},backend:r}),i=v0({inputs:{input:n},backend:r}),o=Df({inputs:{x:i},backend:r}),l=Vi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Bh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var moe={kernelName:kl,backendName:"webgl",kernelFunc:Df};function b8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Lh({inputs:{input:n},backend:r}),s=b8({inputs:{x:a},backend:r}),i=v0({inputs:{input:n},backend:r}),o=Df({inputs:{x:i},backend:r}),l=Vi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Bh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var goe={kernelName:sl,backendName:"webgl",kernelFunc:b8};function yoe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Jy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Jy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=o8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var Aoe={kernelName:ol,backendName:"webgl",kernelFunc:yoe},xoe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=gt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
+    `}},koe=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=w.sizeFromShape(a.shape),u=new woe(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},Ioe={kernelName:ul,backendName:"webgl",kernelFunc:koe};function Wf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Lh({inputs:{input:n},backend:r}),s=Wf({inputs:{x:a},backend:r}),i=I0({inputs:{input:n},backend:r}),o=Wf({inputs:{x:i},backend:r}),l=ji({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Bh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Soe={kernelName:Sl,backendName:"webgl",kernelFunc:Wf};function I8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Lh({inputs:{input:n},backend:r}),s=I8({inputs:{x:a},backend:r}),i=I0({inputs:{input:n},backend:r}),o=Wf({inputs:{x:i},backend:r}),l=ji({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Bh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var Toe={kernelName:ll,backendName:"webgl",kernelFunc:I8};function Noe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return e2({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=e2({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=p8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var Coe={kernelName:dl,backendName:"webgl",kernelFunc:Noe},Eoe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=gt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3804,7 +3804,7 @@ return a / b;`,qie=`
           setOutput(getX(${o}));
         }
       }
-    `}},boe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let n=e.length,a=gt(n),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=Lr("rc",n),l=Lr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[n-1]} += 1;
+    `}},Roe=class{constructor(e,t,r){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let n=e.length,a=gt(n),s=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=Lr("rc",n),l=Lr("source",n),u=`${o[n-1]} < ${this.outputShape[n-1]}`,d=n===1?"source":`vec2(${l.slice(-2).join()})`,h=[`${a} rc = outputLoc;`,`${o[n-1]} += 1;
        if(${u}) {
       `,n===1?"":`}
        rc = outputLoc;
@@ -3828,7 +3828,7 @@ return a / b;`,qie=`
         ${c}
         setOutput(result);
       }
-    `}},v8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Bh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new boe(a.shape,s,i):new xoe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},voe={kernelName:wi,backendName:"webgl",kernelFunc:v8},woe=`
+    `}},S8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Bh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Roe(a.shape,s,i):new Eoe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},Moe={kernelName:Ii,backendName:"webgl",kernelFunc:S8},Foe=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3837,7 +3837,7 @@ return a / b;`,qie=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,koe=`
+`,$oe=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3851,11 +3851,11 @@ return a / b;`,qie=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+y0+`
+  `+b0+`
   return result;
-`,Ioe=wr({opSnippet:woe,packedOpSnippet:koe}),Soe={kernelName:ki,backendName:"webgl",kernelFunc:Ioe};function Toe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=w.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=vr({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=Mee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=w.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=ch(a.dtype),x=_l(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var Noe={kernelName:ll,backendName:"webgl",kernelFunc:Toe},w8=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Fee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},Coe={kernelName:ed,backendName:"webgl",kernelFunc:w8},Eoe="return 1.0 / x;",Roe=it({opSnippet:Eoe}),Moe={kernelName:td,backendName:"webgl",kernelFunc:Roe},Foe=Xn+`
+`,Poe=wr({opSnippet:Foe,packedOpSnippet:$oe}),_oe={kernelName:Si,backendName:"webgl",kernelFunc:Poe};function zoe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=w.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=vr({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=Wee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=w.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=ch(a.dtype),x=Ol(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var Ooe={kernelName:Ni,backendName:"webgl",kernelFunc:zoe},T8=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Vee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},Doe={kernelName:ed,backendName:"webgl",kernelFunc:T8},Loe="return 1.0 / x;",Boe=it({opSnippet:Loe}),Woe={kernelName:td,backendName:"webgl",kernelFunc:Boe},Voe=Xn+`
   return (x < 0.0) ? 0.0 : x;
-`,$oe=`
+`,Uoe=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3865,9 +3865,9 @@ return a / b;`,qie=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Poe=it({opSnippet:Foe,packedOpSnippet:$oe}),_oe={kernelName:Si,backendName:"webgl",kernelFunc:Poe},zoe=Xn+`
+`,Goe=it({opSnippet:Voe,packedOpSnippet:Uoe}),joe={kernelName:Ci,backendName:"webgl",kernelFunc:Goe},Hoe=Xn+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,Ooe=`
+`,qoe=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3877,7 +3877,7 @@ return a / b;`,qie=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Doe=it({opSnippet:zoe,packedOpSnippet:Ooe}),Loe={kernelName:Ni,backendName:"webgl",kernelFunc:Doe},Boe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,Koe=it({opSnippet:Hoe,packedOpSnippet:qoe}),Xoe={kernelName:Ri,backendName:"webgl",kernelFunc:Koe},Zoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":h="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/d[0]},
           ${u[1]/d[1]});
@@ -3910,7 +3910,7 @@ return a / b;`,qie=`
 
         setOutput(newValue);
       }
-    `}},Woe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},Yoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h;a?h="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":h="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${u[0]/d[0]},
           ${u[1]/d[1]},
@@ -3987,7 +3987,7 @@ return a / b;`,qie=`
 
         setOutput(newValue);
       }
-    `}};function Voe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Woe(a.shape,l,u,s,i):new Boe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var Uoe={kernelName:Ti,backendName:"webgl",kernelFunc:Voe},Goe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
+    `}};function Joe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Yoe(a.shape,l,u,s,i):new Zoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],"float32")}var Qoe={kernelName:Ei,backendName:"webgl",kernelFunc:Joe},ele=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4068,7 +4068,7 @@ return a / b;`,qie=`
 
         setOutput(accumulator);
       }
-    `}};function joe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Goe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Hoe={kernelName:hm,backendName:"webgl",kernelFunc:joe},qoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function tle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new ele(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var rle={kernelName:mm,backendName:"webgl",kernelFunc:tle},nle=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/d[0]},
           ${u[1]/d[1]});
@@ -4090,7 +4090,7 @@ return a / b;`,qie=`
 
         setOutput(newValue);
       }
-    `}},Koe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},ale=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${u[0]/d[0]},
           ${u[1]/d[1]},
@@ -4131,7 +4131,7 @@ return a / b;`,qie=`
 
         setOutput(newValue);
       }
-    `}};function Xoe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Koe(a.shape,l,u,s,i):new qoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var Zoe={kernelName:rd,backendName:"webgl",kernelFunc:Xoe},Yoe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
+    `}};function sle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ale(a.shape,l,u,s,i):new nle(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var ile={kernelName:rd,backendName:"webgl",kernelFunc:sle},ole=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4201,7 +4201,7 @@ return a / b;`,qie=`
 
         setOutput(accumulator);
       }
-    `}};function Joe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Yoe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Qoe={kernelName:pm,backendName:"webgl",kernelFunc:Joe},ele=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
+    `}};function lle(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new ole(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var ule={kernelName:fm,backendName:"webgl",kernelFunc:lle},dle=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -4211,7 +4211,7 @@ return a / b;`,qie=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${a}));
       }
-    `}},tle=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let n=Lr("rc",r),a=`${n[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${n[r-2]} + 1 < ${this.outputShape[r-2]}`,i=gt(r);r===1?this.userCode=`
+    `}},ple=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);this.outputShape=e;let n=Lr("rc",r),a=`${n[r-1]} + 1 < ${this.outputShape[r-1]}`,s=`${n[r-2]} + 1 < ${this.outputShape[r-2]}`,i=gt(r);r===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -4239,7 +4239,7 @@ return a / b;`,qie=`
           }
           setOutput(result);
         }
-    `;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let f=e.map((y,A)=>p(A,c)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(c,f){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${f[c]} - 1`:`${f[c]}`}}};function rle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=w.parseAxisParam(s,a.shape);if(i===0)return an({inputs:{x:a},backend:r});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tle(a.shape,o):new ele(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var nle={kernelName:dl,backendName:"webgl",kernelFunc:rle},ale=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
+    `;function o(c){return h(c)}function l(c){return c[r-1]="("+c[r-1]+" + 1)",h(c)}function u(c){return c[r-2]="("+c[r-2]+" + 1)",h(c)}function d(c){return c[r-1]="("+c[r-1]+" + 1)",c[r-2]="("+c[r-2]+" + 1)",h(c)}function h(c){let f=e.map((y,A)=>p(A,c)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(c,f){return t.indexOf(c)!==-1&&e[c]!==1?`${e[c]} - ${f[c]} - 1`:`${f[c]}`}}};function hle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=a.shape.length,o=w.parseAxisParam(s,a.shape);if(i===0)return an({inputs:{x:a},backend:r});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new ple(a.shape,o):new dle(a.shape,o);return r.runWebGLProgram(l,[a],a.dtype)}var cle={kernelName:hl,backendName:"webgl",kernelFunc:hle},fle=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let r=e[1],n=e[2];this.outputShape=e;let a="";typeof t=="number"?a=`float outputValue = ${t.toFixed(2)};`:a=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4258,7 +4258,7 @@ return a / b;`,qie=`
           }
           setOutput(outputValue);
         }
-    `}},sle={kernelName:Il,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new ale(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},ile=`
+    `}},mle={kernelName:Tl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new fle(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[[u,d,Math.sin(a),Math.cos(a)]];return o.runWebGLProgram(l,[n],n.dtype,h)}},gle=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4273,7 +4273,7 @@ return a / b;`,qie=`
       return base + 1.0;
     }
   }
-`,ole=it({opSnippet:ile}),lle={kernelName:pl,backendName:"webgl",kernelFunc:ole},ule="return inversesqrt(x);",dle=it({opSnippet:ule,cpuKernelImpl:$ee}),ple={kernelName:Ci,backendName:"webgl",kernelFunc:dle},k8=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(a.length),l=gt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
+`,yle=it({opSnippet:gle}),Ale={kernelName:cl,backendName:"webgl",kernelFunc:yle},xle="return inversesqrt(x);",ble=it({opSnippet:xle,cpuKernelImpl:Uee}),vle={kernelName:Mi,backendName:"webgl",kernelFunc:ble},N8=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=gt(a.length),l=gt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -4293,7 +4293,7 @@ return a / b;`,qie=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function hle(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new k8(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var cle={kernelName:hl,backendName:"webgl",kernelFunc:hle},fle=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=gt(r);this.userCode=`
+      `}};function wle(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new N8(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var kle={kernelName:fl,backendName:"webgl",kernelFunc:wle},Ile=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=gt(r);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${n});
@@ -4303,15 +4303,15 @@ return a / b;`,qie=`
           setOutput(getB(${a}));
         }
       }
-    `}};function mle(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new fle(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Cr(a.dtype,s.dtype))}var gle={kernelName:cl,backendName:"webgl",kernelFunc:mle},yle=`
+    `}};function Sle(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Ile(n.shape.length,a.shape,a.shape.length);return r.runWebGLProgram(i,[n,a,s],Cr(a.dtype,s.dtype))}var Tle={kernelName:ml,backendName:"webgl",kernelFunc:Sle},Nle=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${N.SELU_SCALEALPHA};
   float scale = ${N.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,Ale=it({opSnippet:yle}),xle={kernelName:nd,backendName:"webgl",kernelFunc:Ale},ble=Td+`
+`,Cle=it({opSnippet:Nle}),Ele={kernelName:nd,backendName:"webgl",kernelFunc:Cle},Rle=Td+`
   return 1.0 / (1.0 + exp(-1.0 * x));
-`,vle=`
+`,Mle=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
   bvec4 isNaN = isnan(x);
 
@@ -4321,15 +4321,15 @@ return a / b;`,qie=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,wle=it({opSnippet:ble,packedOpSnippet:vle,cpuKernelImpl:Pee}),kle={kernelName:Ri,backendName:"webgl",kernelFunc:wle},Ile=`
+`,Fle=it({opSnippet:Rle,packedOpSnippet:Mle,cpuKernelImpl:Gee}),$le={kernelName:$i,backendName:"webgl",kernelFunc:Fle},Ple=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,Sle=it({opSnippet:Ile}),Tle={kernelName:ad,backendName:"webgl",kernelFunc:Sle},Nle=Td+`
+`,_le=it({opSnippet:Ple}),zle={kernelName:ad,backendName:"webgl",kernelFunc:_le},Ole=Td+`
   return sin(x);
-`,Cle=it({opSnippet:Nle}),Ele={kernelName:Ei,backendName:"webgl",kernelFunc:Cle},Rle=`
+`,Dle=it({opSnippet:Ole}),Lle={kernelName:Fi,backendName:"webgl",kernelFunc:Dle},Ble=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,Mle=it({opSnippet:Rle}),Fle={kernelName:ml,backendName:"webgl",kernelFunc:Mle},$le=`
+`,Wle=it({opSnippet:Ble}),Vle={kernelName:yl,backendName:"webgl",kernelFunc:Wle},Ule=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4349,17 +4349,17 @@ return a / b;`,qie=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,Ple=it({opSnippet:$le}),_le={kernelName:sd,backendName:"webgl",kernelFunc:Ple},zle=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=v8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=vr({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},Ole={kernelName:gl,backendName:"webgl",kernelFunc:zle};function Dle(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,Gle=it({opSnippet:Ule}),jle={kernelName:sd,backendName:"webgl",kernelFunc:Gle},Hle=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=S8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=vr({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},qle={kernelName:Al,backendName:"webgl",kernelFunc:Hle};function Kle(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
          ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
          ${n.shape}`);if(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
          ${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=zee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var Lle={kernelName:sh,backendName:"webgl",kernelFunc:Dle};function Ble(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=Oee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Wle={kernelName:id,backendName:"webgl",kernelFunc:Ble};function Vle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=Hee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var Xle={kernelName:sh,backendName:"webgl",kernelFunc:Kle};function Zle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=qee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var Yle={kernelName:id,backendName:"webgl",kernelFunc:Zle};function Jle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=jS(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Ule={kernelName:ih,backendName:"webgl",kernelFunc:Vle};function Gle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=XS(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Qle={kernelName:ih,backendName:"webgl",kernelFunc:Jle};function eue(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=jS(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var jle={kernelName:oh,backendName:"webgl",kernelFunc:Gle};function Hle(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new k8(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var qle={kernelName:lh,backendName:"webgl",kernelFunc:Hle};function Kle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=Nd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Xle={kernelName:yl,backendName:"webgl",kernelFunc:Kle},Qv="return sqrt(x);",Zle=it({opSnippet:Qv,packedOpSnippet:Qv,cpuKernelImpl:Dee}),Yle={kernelName:Mi,backendName:"webgl",kernelFunc:Zle},Jle="return x * x;",Qle=it({opSnippet:Jle}),eue={kernelName:od,backendName:"webgl",kernelFunc:Qle},ew="return (a - b) * (a - b);",tue=wr({opSnippet:ew,packedOpSnippet:ew}),rue={kernelName:Pi,backendName:"webgl",kernelFunc:tue};function nue({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=Xn+`
+             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=XS(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var tue={kernelName:oh,backendName:"webgl",kernelFunc:eue};function rue(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new N8(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var nue={kernelName:lh,backendName:"webgl",kernelFunc:rue};function aue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=Nd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var sue={kernelName:xl,backendName:"webgl",kernelFunc:aue},ew="return sqrt(x);",iue=it({opSnippet:ew,packedOpSnippet:ew,cpuKernelImpl:Kee}),oue={kernelName:Pi,backendName:"webgl",kernelFunc:iue},lue="return x * x;",uue=it({opSnippet:lue}),due={kernelName:od,backendName:"webgl",kernelFunc:uue},tw="return (a - b) * (a - b);",pue=wr({opSnippet:tw,packedOpSnippet:tw}),hue={kernelName:Oi,backendName:"webgl",kernelFunc:pue};function cue({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=Xn+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,s=new Ka(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var aue={kernelName:Di,backendName:"webgl",kernelFunc:nue},sue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=gt(r.length),s=gt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
+  `,s=new Ka(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var fue={kernelName:Wi,backendName:"webgl",kernelFunc:cue},mue=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=gt(r.length),s=gt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
       ${a} strides = ${a}(${t});
 
@@ -4367,15 +4367,15 @@ return a / b;`,qie=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function iue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let T=_t.computeOutShape(A,x,b),E=Nd({inputs:{x:a},backend:r,attrs:{begin:A,size:T}});v=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let T=r.readSync(a.dataId),E=We(a.shape,a.dtype,T),R=Lee(c,E,b,A);v=r.makeTensorInfo(f,a.dtype,R.values)}else{let T=new sue(A,b,c);v=r.runWebGLProgram(T,[a],a.dtype)}let S=ve({inputs:{x:v},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(v),S}var oue={kernelName:Al,backendName:"webgl",kernelFunc:iue};function lue(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Bee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var uue={kernelName:uh,backendName:"webgl",kernelFunc:lue};function due(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Wee(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var pue={kernelName:cm,backendName:"webgl",kernelFunc:due};function hue(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Vee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var cue={kernelName:fm,backendName:"webgl",kernelFunc:hue},fue="return tan(x);",mue=it({opSnippet:fue}),gue={kernelName:xl,backendName:"webgl",kernelFunc:mue},yue=`
+    `}};function gue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let T=_t.computeOutShape(A,x,b),E=Nd({inputs:{x:a},backend:r,attrs:{begin:A,size:T}});v=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let T=r.readSync(a.dataId),E=We(a.shape,a.dtype,T),R=Xee(c,E,b,A);v=r.makeTensorInfo(f,a.dtype,R.values)}else{let T=new mue(A,b,c);v=r.runWebGLProgram(T,[a],a.dtype)}let S=ve({inputs:{x:v},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(v),S}var yue={kernelName:bl,backendName:"webgl",kernelFunc:gue};function Aue(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Zee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var xue={kernelName:uh,backendName:"webgl",kernelFunc:Aue};function bue(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=Yee(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var vue={kernelName:gm,backendName:"webgl",kernelFunc:bue};function wue(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Jee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var kue={kernelName:ym,backendName:"webgl",kernelFunc:wue},Iue="return tan(x);",Sue=it({opSnippet:Iue}),Tue={kernelName:vl,backendName:"webgl",kernelFunc:Sue},Nue=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,Aue=it({opSnippet:yue}),xue={kernelName:zi,backendName:"webgl",kernelFunc:Aue},bue=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=gt(this.rank),a=vue(e);this.userCode=`
+`,Cue=it({opSnippet:Nue}),Eue={kernelName:Li,backendName:"webgl",kernelFunc:Cue},Rue=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=gt(this.rank),a=Mue(e);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function vue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function I8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Gee(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new bue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var wue={kernelName:Qa,backendName:"webgl",kernelFunc:I8},kue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
+    `}};function Mue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function C8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=ete(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Rue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var Fue={kernelName:es,backendName:"webgl",kernelFunc:C8},$ue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
        void main() {
          ivec2 coords = getOutputCoords();
          int batch = coords[0];
@@ -4415,7 +4415,7 @@ return a / b;`,qie=`
             setOutput(float(i1));
          }
        }
-     `}},Iue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
+     `}},Pue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
     void main() {
          // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
          ivec2 coords = getOutputCoords();
@@ -4449,7 +4449,7 @@ return a / b;`,qie=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function po(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function tw(e){let t=1;for(;t<e;)t*=2;return t}function Sue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=jee(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Bh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=w.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&po(r,c);let g=tw(s),y=tw(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),z=new kue(M),O=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(z,I,"int32",O),po(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new Iue([f,R/2]),I=[[d],[A===null?1:0],[g]],z=A;A=r.runWebGLProgram(M,_,"int32",I),po(r,z);let O=g/2,j=O*2;for(let X=O;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=Nd({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),po(r,v);let S=m8({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});po(r,m);let T=u.slice(0,-1);T.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:T},backend:r}),po(r,v);let E=S;return S=ve({inputs:{x:S},attrs:{shape:T},backend:r}),po(r,E),[S,A]}var Tue={kernelName:bl,backendName:"webgl",kernelFunc:Sue},Nue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function fo(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function rw(e){let t=1;for(;t<e;)t*=2;return t}function _ue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=tte(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Bh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=w.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&fo(r,c);let g=rw(s),y=rw(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),z=new $ue(M),O=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(z,I,"int32",O),fo(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new Pue([f,R/2]),I=[[d],[A===null?1:0],[g]],z=A;A=r.runWebGLProgram(M,_,"int32",I),fo(r,z);let O=g/2,j=O*2;for(let X=O;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=Nd({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),fo(r,v);let S=x8({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});fo(r,m);let T=u.slice(0,-1);T.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:T},backend:r}),fo(r,v);let E=S;return S=ve({inputs:{x:S},attrs:{shape:T},backend:r}),fo(r,E),[S,A]}var zue={kernelName:wl,backendName:"webgl",kernelFunc:_ue},Oue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4561,7 +4561,7 @@ return a / b;`,qie=`
               }
               setOutput(outputValue);
             }
-        `}};function Cue(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new Nue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var Eue={kernelName:vl,backendName:"webgl",kernelFunc:Cue};function Rue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;vd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=Hee(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Mue={kernelName:mm,backendName:"webgl",kernelFunc:Rue};function Fue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=Nd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var $ue={kernelName:wl,backendName:"webgl",kernelFunc:Fue},Pue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
+        `}};function Due(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new Oue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var Lue={kernelName:kl,backendName:"webgl",kernelFunc:Due};function Bue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;vd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=rte(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var Wue={kernelName:Am,backendName:"webgl",kernelFunc:Bue};function Vue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=Nd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var Uue={kernelName:Il,backendName:"webgl",kernelFunc:Vue},Gue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
         sumValue += dot(values, segFilter);
     `,p="";a%r>0&&(p=`
         if (inIdx < 0 || inIdx >= ${a}) {
@@ -4667,11 +4667,11 @@ return a / b;`,qie=`
         }
         setOutput(${l});
       }
-    `}};function _ue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=w.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=ch(a.dtype),g=(b,v,S,T,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},z=new Pue(I,v),O=r.compileAndRun(z,[b,S],T);if(l.push(O),O.shape[1]===E)return O;let j=w8({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=I8({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(O,v,X,T,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=vr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var zue={kernelName:dh,backendName:"webgl",kernelFunc:_ue},Oue=[Wte,Ute,Hte,Xte,Yte,ere,rre,are,lre,dre,cre,gre,xre,kre,Tre,Cre,Rre,Pre,zre,Dre,Vre,Xre,Yre,Qre,sne,one,pne,wte,fne,xne,kne,Ene,Mne,$ne,_ne,One,Bne,Une,Hne,Kne,Zne,Jne,tae,nae,oae,uae,hae,mae,yae,vae,Sae,Eae,Fae,_ae,zae,Dae,Bae,Vae,Gae,Hae,Zae,Qae,rse,ase,ose,dse,fse,Ase,vte,bse,yne,kse,Tse,Ese,Ite,$se,Ose,Lse,Use,Hse,Zse,Qse,nie,oie,die,hie,gie,Aie,bie,Iie,Tie,Cie,Rie,Fie,zie,Bie,Gie,Jie,Ete,roe,soe,loe,poe,tne,foe,goe,Aoe,voe,Soe,Tte,Noe,Coe,rne,Kie,Moe,_oe,Loe,Mte,Uoe,Hoe,Zoe,Qoe,nle,sle,lle,ple,cle,gle,xle,kle,Tle,Ele,Fle,qre,Zie,_le,Ole,Lle,Wle,Ule,jle,qle,Xle,Yle,eue,rue,aue,oue,uue,pue,cue,Xie,Dte,gue,xue,wue,Tue,Eue,Lte,Mue,$ue,zue,moe];for(let e of Oue)Gn(e);var za=Y();za.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);za.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);za.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);za.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);za.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);za.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);za.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);za.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);za.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);za.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Due="return a + b;",Lue="return areal * breal - aimag * bimag;",Bue="return areal * bimag + aimag * breal;",Wue="return a / b;",Vue="return a * b;",Uue="return (a - b) * (a - b);",Gue="return a - b;",jue="return f32(a == b);",Hue="return vec4<f32>(a == b);",que="return f32(a > b);",Kue="return vec4<f32>(a > b);",Xue="return f32(a >= b);",Zue="return vec4<f32>(a >= b);",Yue="return f32(a < b);",Jue="return vec4<f32>(a < b);",Que="return f32(a <= b);",ede="return vec4<f32>(a <= b);",tde="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",rde=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
-  vec4<f32>(b >= vec4<f32>(1.0)));`,nde=`
+    `}};function jue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=vr({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=w.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=ch(a.dtype),g=(b,v,S,T,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},z=new Gue(I,v),O=r.compileAndRun(z,[b,S],T);if(l.push(O),O.shape[1]===E)return O;let j=T8({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=C8({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(O,v,X,T,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=vr({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Hue={kernelName:dh,backendName:"webgl",kernelFunc:jue},que=[Yte,Qte,rre,sre,ore,dre,hre,fre,Are,bre,kre,Tre,Ere,$re,zre,Dre,Bre,Gre,Hre,Kre,Jre,sne,one,une,mne,yne,vne,Fte,Ine,Ene,$ne,Lne,Wne,Une,jne,qne,Zne,Qne,rae,aae,iae,lae,pae,cae,yae,xae,wae,Sae,Nae,Mae,_ae,Lae,Vae,jae,Hae,Kae,Zae,Jae,ese,rse,ise,use,hse,fse,yse,bse,Ise,Cse,Mte,Rse,Nne,$se,zse,Lse,Pte,Use,qse,Xse,Qse,rie,iie,uie,cie,yie,bie,wie,Tie,Cie,Rie,Pie,zie,Die,Bie,Vie,Hie,Zie,eoe,loe,Lte,hoe,moe,Aoe,voe,pne,Ioe,Toe,Coe,Moe,_oe,zte,Ooe,Doe,hne,aoe,Woe,joe,Xoe,Wte,Qoe,rle,ile,ule,cle,mle,Ale,vle,kle,Tle,Ele,$le,zle,Lle,Vle,nne,ioe,jle,qle,Xle,Yle,Qle,tue,nue,sue,oue,due,hue,fue,yue,xue,vue,kue,soe,Kte,Tue,Eue,Fue,zue,Lue,Xte,Wue,Uue,Hue,Soe];for(let e of que)Gn(e);var za=Y();za.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);za.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);za.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);za.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);za.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);za.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);za.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);za.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);za.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);za.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var Kue="return a + b;",Xue="return areal * breal - aimag * bimag;",Zue="return areal * bimag + aimag * breal;",Yue="return a / b;",Jue="return a * b;",Que="return (a - b) * (a - b);",ede="return a - b;",tde="return f32(a == b);",rde="return vec4<f32>(a == b);",nde="return f32(a > b);",ade="return vec4<f32>(a > b);",sde="return f32(a >= b);",ide="return vec4<f32>(a >= b);",ode="return f32(a < b);",lde="return vec4<f32>(a < b);",ude="return f32(a <= b);",dde="return vec4<f32>(a <= b);",pde="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",hde=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  vec4<f32>(b >= vec4<f32>(1.0)));`,cde=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
-  `,S8=`
+  `,E8=`
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -4684,12 +4684,12 @@ return a / b;`,qie=`
   if (isNaN.a) {
     resultTemp.a = uniforms.NAN;
   }
-  `,ade=`
+  `,fde=`
   let s = sign(a) * sign(b);
   let ia = i32(round(a));
   let ib = i32(round(b));
   return f32(idiv(ia, ib, s));
-  `,sde=`
+  `,mde=`
   let ia = vec4<i32>(round(a));
   let ib = vec4<i32>(round(b));
   let cond = ib != vec4<i32>(0);
@@ -4710,7 +4710,7 @@ return a / b;`,qie=`
     resultTemp[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4<f32>(resultTemp);
-  `,ide="return f32(a != b);",ode="return vec4<f32>(a != b);",lde=`
+  `,gde="return f32(a != b);",yde="return vec4<f32>(a != b);",Ade=`
   if(a < 0.0 && floor(b) < b) {
     return uniforms.NAN;
   }
@@ -4721,7 +4721,7 @@ return a / b;`,qie=`
     return pow(abs(a), b);
   }
   return sign(a) * pow(abs(a), b);
-  `,ude=`
+  `,xde=`
   let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
   let isModRound1 = vec4<f32>(isModRound1Bool);
   let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
@@ -4742,22 +4742,22 @@ return a / b;`,qie=`
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${S8}
+  ${E8}
   return resultTemp;
-  `,dde="if (a < 0.0) { return b * a; }  return a;",pde=`
+  `,bde="if (a < 0.0) { return b * a; }  return a;",vde=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function rw(e,t){let r=t?S8:nde;return t?`
+  `;function nw(e,t){let r=t?E8:cde;return t?`
     var resultTemp = vec4<f32>(${e}(a, b));
     let isNaN = isnanVec4(a) | isnanVec4(b);
     `+r+`
     return resultTemp;
   `:r+`
     return ${e}(a, b);
-  `}function Wh(e,t){switch(e){case 0:return Vue;case 1:return Due;case 2:return Gue;case 3:return Wue;case 4:return t?Hue:jue;case 5:return t?Kue:que;case 6:return t?Zue:Xue;case 7:return t?Jue:Yue;case 8:return t?ede:Que;case 9:return t?rde:tde;case 10:return t?ode:ide;case 11:return Uue;case 12:return t?sde:ade;case 14:return t?pde:dde;case 15:return rw("max",t);case 16:return rw("min",t);case 13:return t?ude:lde;case 17:return Lue;case 18:return Bue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var hde="return abs(a);",cde="return ceil(a);",fde="return cos(a);",mde=`
+  `}function Wh(e,t){switch(e){case 0:return Jue;case 1:return Kue;case 2:return ede;case 3:return Yue;case 4:return t?rde:tde;case 5:return t?ade:nde;case 6:return t?ide:sde;case 7:return t?lde:ode;case 8:return t?dde:ude;case 9:return t?hde:pde;case 10:return t?yde:gde;case 11:return Que;case 12:return t?mde:fde;case 14:return t?vde:bde;case 15:return nw("max",t);case 16:return nw("min",t);case 13:return t?xde:Ade;case 17:return Xue;case 18:return Zue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var wde="return abs(a);",kde="return ceil(a);",Ide="return cos(a);",Sde=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
-`,gde="return exp(a) - 1.0;",yde="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Ade=`
+`,Tde="return exp(a) - 1.0;",Nde="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Cde=`
   var resFloat = exp(a) - vec4<f32>(1.0);
   if (a.r >= 0.0) {
     resFloat.r = a.r;
@@ -4772,11 +4772,11 @@ return a / b;`,qie=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,xde="return exp(a);",bde="return floor(a);",vde="return a;",wde=`if (a < 0.0) { return 1.0/0.0; }
-  return log(a);`,kde="return f32(!(a >= 1.0));",Ide="return -a;",Sde="if (a < 0.0) { return uniforms.alpha * a; } return a;",Tde=`
+`,Ede="return exp(a);",Rde="return floor(a);",Mde="return a;",Fde=`if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`,$de="return f32(!(a >= 1.0));",Pde="return -a;",_de="if (a < 0.0) { return uniforms.alpha * a; } return a;",zde=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`,Nde="if(a < 0.0) { return 0.0; } return a;",Cde="return clamp(a, 0.0, 6.0);",Ede="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Rde=`
+`,Ode="if(a < 0.0) { return 0.0; } return a;",Dde="return clamp(a, 0.0, 6.0);",Lde="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Bde=`
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
   let isNaN = isnanVec4(a);
 
@@ -4793,26 +4793,26 @@ return a / b;`,qie=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,Mde="return 1.0/sqrt(a);",Fde="return 1.0 / (1.0 + exp(-1.0 * a));",$de="return sin(a);",Pde=`
+`,Wde="return 1.0/sqrt(a);",Vde="return 1.0 / (1.0 + exp(-1.0 * a));",Ude="return sin(a);",Gde=`
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
-`,_de="return sqrt(a);",zde="return a * a;",Ode=`
+`,jde="return sqrt(a);",Hde="return a * a;",qde=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,Dde="return f32(i32((a)));";function fo(e,t){switch(e){case 0:return hde;case 2:return fde;case 3:return mde;case 1:return cde;case 4:return t?Ade:yde;case 5:return xde;case 6:return gde;case 7:return bde;case 8:return vde;case 9:return wde;case 10:return kde;case 11:return Ide;case 14:return t?Tde:Sde;case 12:return t?Rde:Nde;case 13:return t?Ede:Cde;case 15:return Mde;case 18:return Fde;case 16:return $de;case 17:return Pde;case 19:return _de;case 20:return zde;case 21:return Ode;case 22:return Dde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function ss(e,t=!1){if(e===null)return null;if(e==="linear")return fo(8);if(e==="relu")return fo(12,t);if(e==="elu")return fo(4,t);if(e==="relu6")return fo(13,t);if(e==="prelu")return Wh(14,t);if(e==="sigmoid")return fo(18,t);if(e==="leakyrelu")return fo(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Lde(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function gr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function pf(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function yb(){return`
+`,Kde="return f32(i32((a)));";function yo(e,t){switch(e){case 0:return wde;case 2:return Ide;case 3:return Sde;case 1:return kde;case 4:return t?Cde:Nde;case 5:return Ede;case 6:return Tde;case 7:return Rde;case 8:return Mde;case 9:return Fde;case 10:return $de;case 11:return Pde;case 14:return t?zde:_de;case 12:return t?Bde:Ode;case 13:return t?Lde:Dde;case 15:return Wde;case 18:return Vde;case 16:return Ude;case 17:return Gde;case 19:return jde;case 20:return Hde;case 21:return qde;case 22:return Kde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function is(e,t=!1){if(e===null)return null;if(e==="linear")return yo(8);if(e==="relu")return yo(12,t);if(e==="elu")return yo(4,t);if(e==="relu6")return yo(13,t);if(e==="prelu")return Wh(14,t);if(e==="sigmoid")return yo(18,t);if(e==="leakyrelu")return yo(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function Xde(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function gr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function pf(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function xb(){return`
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
-`}function Ui(){return`
-  ${yb()}
+`}function Hi(){return`
+  ${xb()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
     localId = LocalId;
     globalId = GlobalId;
     numWorkgroups = NumWorkgroups;
-`}function tt(){return`
-    ${Ui()}
+`}function et(){return`
+    ${Hi()}
       let index = getGlobalIndex();
-`}function Bde(e,t,r,n=!1){let a=[];if(a.push(`
+`}function Zde(e,t,r,n=!1){let a=[];if(a.push(`
     let workGroupSizeX = ${r.workGroupSize[0]}u;
     let workGroupSizeY = ${r.workGroupSize[1]}u;
     let workGroupSizeZ = ${r.workGroupSize[2]}u;
@@ -4847,8 +4847,8 @@ return a / b;`,qie=`
 
       @group(0) @binding(0) var<storage, write> result: array<${pf(t.dtype,r.isVec4)}>;
       @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `),[nw,a.join(`
-`),aw(t.shape),r.getUserCode()].join(`
+    `),[aw,a.join(`
+`),sw(t.shape),r.getUserCode()].join(`
 `);let s="struct Uniforms { NAN : f32, ";r.variableNames.forEach((d,h)=>{s+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${gr(e[h].shape.length)}, `}),s+=`outShape : ${gr(t.shape.length)}, `;let i=t.shape.length-1;s+=`
        outShapeStrides: ${gr(i)}, `,r.size&&(s+="size : i32, "),r.uniforms&&(s+=r.uniforms),s+="};",a.push(s),r.atomic?a.push(`
     @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
@@ -4858,10 +4858,10 @@ return a / b;`,qie=`
     @group(0) @binding(${1+h}) var<storage, read> ${d}: array<${pf(e[h].dtype,r.isVec4)}>;
     `)}),s!==""&&a.push(`
     @group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms: Uniforms;
-    `);let[o,l]=Hde(t.shape,r.dispatchLayout),u=[nw,a.join(`
-`),aw(t.shape),o,Wde(t.shape.length)];if(r.atomic||u.push(Vde(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>Ude(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
+    `);let[o,l]=rpe(t.shape,r.dispatchLayout),u=[aw,a.join(`
+`),sw(t.shape),o,Yde(t.shape.length)];if(r.atomic||u.push(Jde(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>Qde(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
 `);u.push(d)}return u.push(r.getUserCode()),u.join(`
-`)}var nw=`
+`)}var aw=`
   // Checks whether coordinates lie within the bounds of the shape.
   fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
     return all(coord >= vec2<i32>(0)) && all(coord < shape);
@@ -4908,7 +4908,7 @@ return a / b;`,qie=`
   fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
-`;function Wde(e){let t="";switch(e){case 0:case 1:t+=`
+`;function Yde(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
           return coords;
         }
@@ -4925,7 +4925,7 @@ return a / b;`,qie=`
           return dot(coords, vec4<i32>(
             uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
         }
-        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Vde(e,t,r){let n=e.length,a=pf(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Jde(e,t,r){let n=e.length,a=pf(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${a}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4953,7 +4953,7 @@ return a / b;`,qie=`
         let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return s}function Ude(e,t,r,n){let a=Gde(e,r);return e.shape.length<=t.length&&(a+=jde(e,t,r,n)),a}function Gde(e,t){let r=e.name,n=e.shape.length,a=gr(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
+    `}return s}function Qde(e,t,r,n){let a=epe(e,r);return e.shape.length<=t.length&&(a+=tpe(e,t,r,n)),a}function epe(e,t){let r=e.name,n=e.shape.length,a=gr(n),s="get"+r.charAt(0).toUpperCase()+r.slice(1),i=["d0","d1","d2","d3"].slice(0,n),o=i.map(d=>`${d} : i32`).join(", ");if(n<1)return t?`
         fn ${s}() -> vec4<f32> {
           return vec4<f32>(${r}[0]);
         }
@@ -4971,7 +4971,7 @@ return a / b;`,qie=`
       return f32(${r}[getIndexFromCoords${u}(${a}(${i.join(",")}),
         ${l})]);
     }
-   `}function jde(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=gr(l);if(w.arraysEqual(e.shape,t)&&n)return r?`
+   `}function tpe(e,t,r,n){let a=e.name,s=a.charAt(0).toUpperCase()+a.slice(1),i="get"+s+"ByOutput",o=e.shape.length,l=t.length,u=gr(l);if(w.arraysEqual(e.shape,t)&&n)return r?`
         fn ${i}Index(globalIndex : i32) -> vec4<f32> {
           return vec4<f32>(${a}[globalIndex]);
         }
@@ -5028,13 +5028,13 @@ return a / b;`,qie=`
       ${p}
       return f32(${a}[getIndexFromCoords${m}(${c}, ${f})]);
     }
-  `}function Hde(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${gr(s)}{
+  `}function rpe(e,t){let{x:r,y:n=[],z:a=[]}=t,s=e.length;if(r.length===s)return[`fn getOutputCoords() -> ${gr(s)}{
       let globalIndex = getGlobalIndex();
       return getCoordsFromIndex(globalIndex);
     }
-    `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let f=Lde(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${p} - d${c[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${c[m]} * ${f[m]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=gr(l),h=`fn getOutputCoords() -> ${d} {
+    `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let f=Xde(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${p} - d${c[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${c[m]} * ${f[m]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=gr(l),h=`fn getOutputCoords() -> ${d} {
     ${i}
-  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function aw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),n=gr(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function sw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),n=gr(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
       let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
       return vec2<i32>(d0, d1);
     }`;let s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides[${o}]`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`;return`${l}; ${u};`}).join("");return`
@@ -5042,7 +5042,7 @@ return a / b;`,qie=`
       ${s}
       return ${n}(${a.join(",")});
     }
-  `}var T8={};Le(T8,{ArrayBufferToTypedArray:()=>C8,GPUBytesPerElement:()=>Qy,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>Ab,computeWorkGroupSizeForMatMul:()=>N8,computeWorkPerThreadForConv2d:()=>xb,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>bb,tilesFitEvenlyIntoShape:()=>Za});var So=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Za(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Oe(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(So(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(So(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(So(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function Ab(e,t){let r=So(e.x.map(a=>t[a])),n=So(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function N8(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function xb(e,t){let r=So(e.x.map(a=>t[a])),n=So(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,r)=>r)}}function Qy(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function C8(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function bb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function E8(e,t,r,n){return w.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
+  `}var R8={};Le(R8,{ArrayBufferToTypedArray:()=>F8,GPUBytesPerElement:()=>t2,computeDispatch:()=>ze,computeWorkGroupSizeForConv2d:()=>bb,computeWorkGroupSizeForMatMul:()=>M8,computeWorkPerThreadForConv2d:()=>vb,flatDispatchLayout:()=>Ke,isWebGPUSupported:()=>wb,tilesFitEvenlyIntoShape:()=>Za});var Co=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function Za(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function ze(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(Co(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(Co(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(Co(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function bb(e,t){let r=Co(e.x.map(a=>t[a])),n=Co(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function M8(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function vb(e,t){let r=Co(e.x.map(a=>t[a])),n=Co(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Ke(e){return{x:e.map((t,r)=>r)}}function t2(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function F8(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function wb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function $8(e,t,r,n){return w.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
   var<workgroup> mm_Asub : array<array<vec4<f32>, ${n/e[0]}>, ${t}>;
   var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;
 
@@ -5050,7 +5050,7 @@ return a / b;`,qie=`
   let ColPerThread = ${e[0]};
   let TileInner = ${n};
 
-  ${Ui()}
+  ${Hi()}
 
     let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
     let tileCol = i32(localId.x);
@@ -5109,13 +5109,13 @@ return a / b;`,qie=`
                  globalCol,
                  acc[innerRow], globalId);
     }
-  }`}var qde=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[Za(n,this.aShape.slice(1)),Za(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  }`}var npe=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],r=[this.outputShape[0],e,t],n=[this.tileAOuter,this.tileInner],a=[this.tileInner,this.tileBOuter];return[Za(n,this.aShape.slice(1)),Za(a,r.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
         }
         return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
              return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
         }
-        return vec4<f32>(0.0)`,r="",n="";if(this.activation){let s=ss(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+        return vec4<f32>(0.0)`,r="",n="";if(this.activation){let s=is(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
                   let b = getPreluActivationWeightsByOutputCoords(outCoord);
                   ${s}
                 }`:r=`
@@ -5146,11 +5146,11 @@ return a / b;`,qie=`
           setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
         }
       }
-      ${E8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
-    `}};function vb(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
+      ${$8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
+    `}};function kb(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
     var<workgroup> mm_Asub : array<array<f32, ${a}>, ${r}>;
     var<workgroup> mm_Bsub : array<array<f32, ${n}>, ${a}>;
-    ${Ui()}
+    ${Hi()}
       let tileRow = i32(localId.y) * ${e[1]};
       let tileCol = i32(localId.x) * ${e[0]};
 
@@ -5231,11 +5231,11 @@ return a / b;`,qie=`
         }
       }
     }
-  `}function Kde(e){return`
+  `}function ape(e){return`
     let TileSize = ${e[0]*4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
-    ${Ui()}
+    ${Hi()}
       let tileCol = i32(localId.x);
       let globalCol = i32(globalId.x);
       let globalRow = i32(globalId.y);
@@ -5274,7 +5274,7 @@ return a / b;`,qie=`
         mm_write(globalRow, globalCol, acc, globalId);
       }
     }
-  `}var R8=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=n?e[1]:e[2];this.workGroupSize=N8(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[Za(a,this.aShape.slice(1)),Za(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var P8=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=n?e[1]:e[2];this.workGroupSize=M8(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[Za(a,this.aShape.slice(1)),Za(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
              return A[batch * batchASize + row * uniforms.dimInner + col];
            }
            return 0.0;`:e=this.fitA?"return A[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
@@ -5286,7 +5286,7 @@ return a / b;`,qie=`
            return 0.0;`:t=this.fitB?"return B[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
              return B[batch * batchBSize + col * uniforms.dimInner + row];
            }
-           return 0.0;`;let r="",n="";if(this.activation){let s=ss(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+           return 0.0;`;let r="",n="";if(this.activation){let s=is(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${s}
             }`:r=`
@@ -5316,10 +5316,10 @@ return a / b;`,qie=`
         ${n}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1]>1?vb([this.workPerThread,this.workPerThread,1],this.workGroupSize):Kde(this.workGroupSize)}
-    `}};function Xde(){return`
+      ${this.outputShape[1]>1?kb([this.workPerThread,this.workPerThread,1],this.workGroupSize):ape(this.workGroupSize)}
+    `}};function spe(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
-    ${Ui()}
+    ${Hi()}
       let coords = getOutputCoords();
       let batch = coords[0];
       let row = coords[1];
@@ -5348,7 +5348,7 @@ return a / b;`,qie=`
         mm_write(batch, row, col, sum);
       }
     }
-  `}var Zde=class{constructor(e,t=!1,r=!1,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=r,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${r}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=ss(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+  `}var ipe=class{constructor(e,t=!1,r=!1,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=n!=null,o=s!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=r,this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${r}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let r="",n="";if(this.activation){let s=is(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${s}
             }`:r=`
@@ -5375,8 +5375,8 @@ return a / b;`,qie=`
         ${n}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${Xde()}
-    `}};function Yde(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
+      ${spe()}
+    `}};function ope(e){let t=e[1]/2,r=e[0],n=t>r?t:r;return`
   var<workgroup> mm_Asub1 : array<array<f32, ${n}>, ${t}>;
   var<workgroup> mm_Bsub1 : array<array<f32, ${r}>, ${n}>;
   var<workgroup> mm_Asub2 : array<array<f32, ${n}>, ${t}>;
@@ -5388,7 +5388,7 @@ return a / b;`,qie=`
   // arithmetic operations and others handle IO operations between barrier api,
   // makes ALUs and load/store units work simultaneously, could improves
   // the performance.
-  ${Ui()}
+  ${Hi()}
     let tileRow = i32(localId.y);
     let tileCol = i32(localId.x);
     let globalRow = i32(globalId.y);
@@ -5463,13 +5463,13 @@ return a / b;`,qie=`
       mm_write(writeCol, globalCol, acc, globalId);
     }
   }
-  `}var Jde=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var lpe=class{constructor(e,t,r,n=null,a=null,s=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=r,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(r[2]/this.workGroupSize[0]),Math.ceil(r[1]*2/this.workGroupSize[1]),r[0]];let i=n!=null;i&&this.variableNames.push("bias");let o=s!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=a,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
           return A[batch * batchASize + row * uniforms.dimInner + col];
         }
         return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
            return B[batch * batchBSize + row * uniforms.dimBOuter + col];
          }
-         return 0.0;`,r="",n="";if(this.activation){let s=ss(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+         return 0.0;`,r="",n="";if(this.activation){let s=is(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
             let b = getPreluActivationWeightsByOutputCoords(outCoord);
             ${s}
             }`:r=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
@@ -5497,14 +5497,14 @@ return a / b;`,qie=`
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${Yde(this.workGroupSize)}
-    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Qde={kernelName:ul,backendName:"webgpu",kernelFunc:qe};function wb({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=Sl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],S=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),T=qe({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,T],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new Zde([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new Jde(b,v,[R,c,f],s,l,i):_?M=new qde(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new R8(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[S,T];s&&I.push(s),i&&I.push(i);let z=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}];l==="leakyrelu"&&(z.push({type:"float32",data:[o]}),M.uniforms+=" alpha : f32,");let O=a.runWebGPUProgram(M,I,e.dtype,z),j=qe({inputs:{x:O},backend:a,attrs:{shape:x}});E.push(O);for(let X of E)a.disposeData(X.dataId);return j}function epe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return wb({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var tpe={kernelName:Ms,backendName:"webgpu",kernelFunc:epe},sw=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+      ${ope(this.workGroupSize)}
+    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var upe={kernelName:pl,backendName:"webgpu",kernelFunc:qe};function Ib({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=Nl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],S=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),T=qe({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[S,T],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new ipe([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new lpe(b,v,[R,c,f],s,l,i):_?M=new npe(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new P8(b,[R,c,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[S,T];s&&I.push(s),i&&I.push(i);let z=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}];l==="leakyrelu"&&(z.push({type:"float32",data:[o]}),M.uniforms+=" alpha : f32,");let O=a.runWebGPUProgram(M,I,e.dtype,z),j=qe({inputs:{x:O},backend:a,attrs:{shape:x}});E.push(O);for(let X of E)a.disposeData(X.dataId);return j}function dpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Ib({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var ppe={kernelName:Fs,backendName:"webgpu",kernelFunc:dpe},iw=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
         ${Wh(this.op,!1)}
       }
 
-      ${tt()}
+      ${et()}
         if(index < uniforms.size) {
           let areal = getARealByOutputIndex(index);
           let aimag = getAImagByOutputIndex(index);
@@ -5513,14 +5513,14 @@ return a / b;`,qie=`
           setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
         }
       }
-    `}},rpe=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+    `}},hpe=class{constructor(e,t,r,n){this.variableNames=["A","B"],this.size=!0;let a=256;this.workGroupSize=[a,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.lastDimensionSize=n?r[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=n,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
           ${Wh(this.op,!1)}
         }
         var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
-        ${tt()}
+        ${et()}
 
           // Fill in the shared memory buffer. Here we need a loop to make sure
           // that all data in A|B are uploaded when |sharedMemorySize| is larger
@@ -5540,40 +5540,40 @@ return a / b;`,qie=`
             }
           }
         }
-        `}},npe=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
+        `}},cpe=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
         ${Wh(this.op,this.isVec4)}
       }
-      ${tt()}
+      ${et()}
         if (index < uniforms.size) {
           let a = getAByOutputIndex(index);
           let b = getBByOutputIndex(index);
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-    `}},M8=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
+    `}},_8=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
       fn binaryOperation(a : f32, b : f32) -> f32 {
         ${Wh(this.op,!1)}
       }
-      ${tt()}
+      ${et()}
         if (index < uniforms.size) {
           let a = getAByOutputIndex(index);
           let b = getBByOutputIndex(index);
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function iw(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new npe(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new rpe(e,t,r,a):new M8(e,t,r)}function Vn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var ape={kernelName:pi,backendName:"webgpu",kernelFunc:Vn};function Cd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Vn({inputs:{x:n},backend:r}),l=Vn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var spe={kernelName:Xp,backendName:"webgpu",kernelFunc:Cd},Vh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function ow(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new cpe(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new hpe(e,t,r,a):new _8(e,t,r)}function Pn(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var fpe={kernelName:ci,backendName:"webgpu",kernelFunc:Pn};function Cd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Pn({inputs:{x:n},backend:r}),l=Pn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var mpe={kernelName:Xp,backendName:"webgpu",kernelFunc:Cd},Vh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
-        ${fo(this.op,!1)}
+        ${yo(this.op,!1)}
       }
-      ${tt()}
+      ${et()}
         if (index < uniforms.size) {
           let a = getAByOutputIndex(index);
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function kr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Vh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function jr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=iw(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Cr(y.dtype,A.dtype))});else{let g=new sw(17,i.shape,o.shape),y=new sw(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=Cd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Cr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=iw(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:ipe,ceilImpl:ope,concatImpl:lpe,equalImpl:upe,expImpl:dpe,expm1Impl:ppe,floorImpl:hpe,gatherNdImpl:cpe,gatherV2Impl:fpe,greaterEqualImpl:mpe,greaterImpl:gpe,lessEqualImpl:ype,lessImpl:Ape,logImpl:xpe,maxImpl:bpe,maximumImpl:vpe,minimumImpl:wpe,multiplyImpl:kpe,negImpl:Ipe,notEqualImpl:Spe,prodImpl:Tpe,rangeImpl:Npe,rsqrtImpl:Cpe,simpleAbsImpl:Epe,sliceImpl:Rpe,stridedSliceImpl:Mpe,stringNGramsImpl:Fpe,subImpl:$pe,tileImpl:Ppe,topKImpl:_pe,transposeImpl:zpe,uniqueImpl:BAe}=c0,Ope=kr({opType:0,cpuKernelImpl:Epe}),Dpe={kernelName:Lo,backendName:"webgpu",kernelFunc:Ope},Lpe=jr({opSnippet:1,cpuKernelImpl:ipe,supportsComplex:!0}),Bpe={kernelName:Ya,backendName:"webgpu",kernelFunc:Lpe},Wpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
-      ${tt()}
+      `}};function kr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Vh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function jr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=ow(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Cr(y.dtype,A.dtype))});else{let g=new iw(17,i.shape,o.shape),y=new iw(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=Cd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Cr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=ow(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:gpe,ceilImpl:ype,concatImpl:Ape,equalImpl:xpe,expImpl:bpe,expm1Impl:vpe,floorImpl:wpe,gatherNdImpl:kpe,gatherV2Impl:Ipe,greaterEqualImpl:Spe,greaterImpl:Tpe,lessEqualImpl:Npe,lessImpl:Cpe,logImpl:Epe,maxImpl:Rpe,maximumImpl:Mpe,minimumImpl:Fpe,multiplyImpl:$pe,negImpl:Ppe,notEqualImpl:_pe,prodImpl:zpe,rangeImpl:Ope,rsqrtImpl:Dpe,simpleAbsImpl:Lpe,sliceImpl:Bpe,stridedSliceImpl:Wpe,stringNGramsImpl:Vpe,subImpl:Upe,tileImpl:Gpe,topKImpl:jpe,transposeImpl:Hpe,uniqueImpl:exe}=g0,qpe=kr({opType:0,cpuKernelImpl:Lpe}),Kpe={kernelName:Vo,backendName:"webgpu",kernelFunc:qpe},Xpe=jr({opSnippet:1,cpuKernelImpl:gpe,supportsComplex:!0}),Zpe={kernelName:Ja,backendName:"webgpu",kernelFunc:Xpe},Ype=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
+      ${et()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
           if (flatIndex < uniforms.size) {
@@ -5584,7 +5584,7 @@ return a / b;`,qie=`
           }
         }
       }
-    `}};function Vpe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return Vn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Cr(o,l)),s=n.map(o=>o.shape),i=new Wpe(s);return r.runWebGPUProgram(i,n,a)}var Upe={kernelName:qs,backendName:"webgpu",kernelFunc:Vpe},F8=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+    `}};function Jpe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return Pn({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Cr(o,l)),s=n.map(o=>o.shape),i=new Ype(s);return r.runWebGPUProgram(i,n,a)}var Qpe={kernelName:Ks,backendName:"webgpu",kernelFunc:Jpe},z8=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
     `,t=(n,a)=>this.outputShape.length===1?n:`${n}[${a}]`,r=n=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${n}]`;return`
@@ -5624,7 +5624,7 @@ return a / b;`,qie=`
         return coordInfo[0] + coordInfo[1] * index;
       }
 
-      ${tt()}
+      ${et()}
         let outputIndex = index / i32(workGroupSizeX);
         let coordInfo = getInputCoordInfo(outputIndex);
         let Length = ${r("uniforms.axis")};
@@ -5664,10 +5664,10 @@ return a / b;`,qie=`
           setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
         }
       }
-    `}},Gpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+    `}},ehe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${yb()}
+      ${xb()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5686,8 +5686,8 @@ return a / b;`,qie=`
             [localId.y]);
         }
       }
-    `}},jpe=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=gr(this.outputShape.length),t=Hpe(this.newDim);return`
-      ${tt()}
+    `}},the=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=gr(this.outputShape.length),t=rhe(this.newDim);return`
+      ${et()}
 
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5698,8 +5698,8 @@ return a / b;`,qie=`
           }
         }
       }
-    `}};function Hpe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function zl(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=zpe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&w.arraysEqual(s,[1,0])){let d=new Gpe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new jpe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var qpe={kernelName:Oi,backendName:"webgpu",kernelFunc:zl};function Kpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=zl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new F8(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Xpe={kernelName:Ks,backendName:"webgpu",kernelFunc:Kpe};function Zpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=zl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new F8(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Ype={kernelName:Du,backendName:"webgpu",kernelFunc:Zpe},$8=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
-      ${tt()}
+    `}};function rhe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Ya(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Hpe(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&w.arraysEqual(s,[1,0])){let d=new ehe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new the(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var nhe={kernelName:Bi,backendName:"webgpu",kernelFunc:Ya};function ahe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new z8(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var she={kernelName:Xs,backendName:"webgpu",kernelFunc:ahe};function ihe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Ya({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new z8(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var ohe={kernelName:Lu,backendName:"webgpu",kernelFunc:ihe},O8=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+      ${et()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
           let batch = coords[0];
@@ -5731,8 +5731,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, ${t});
         }
       }
-    `}},P8=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
-      ${tt()}
+    `}},D8=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+      ${et()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let batch = coords[0];
@@ -5746,8 +5746,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function Jpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return Vn({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new P8(d):(h=new $8(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var Qpe={kernelName:Xs,backendName:"webgpu",kernelFunc:Jpe};function ehe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return wb({a,b:s,transposeA:i,transposeB:o,backend:r})}var the={kernelName:Zs,backendName:"webgpu",kernelFunc:ehe},rhe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${gr(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=gr(this.rank),t=nhe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${e2[a]} = uniforms.start[${a}] + coords.${e2[a]};`),`
-      ${tt()}
+    `}};function lhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return Pn({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new D8(d):(h=new O8(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var uhe={kernelName:Zs,backendName:"webgpu",kernelFunc:lhe};function dhe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Ib({a,b:s,transposeA:i,transposeB:o,backend:r})}var phe={kernelName:Ys,backendName:"webgpu",kernelFunc:dhe},hhe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${gr(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=gr(this.rank),t=che(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${r2[a]} = uniforms.start[${a}] + coords.${r2[a]};`),`
+      ${et()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
           let coords = getCoordsFromIndex(index);
@@ -5756,8 +5756,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},e2=["x","y","z","w","u","v"];function nhe(e){if(e===1)return"sourceLoc";if(e<=6)return e2.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Ed(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=Rpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new rhe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var ahe={kernelName:fl,backendName:"webgpu",kernelFunc:Ed},she=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=zl({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=Ed({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},ihe={kernelName:Bo,backendName:"webgpu",kernelFunc:she},_8=jr({opSnippet:10,dtype:"bool",cpuKernelImpl:Spe}),ohe={kernelName:rl,backendName:"webgpu",kernelFunc:_8};function Uh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Vn({inputs:{x:a.complexTensorInfos.real},backend:r})}var lhe={kernelName:ah,backendName:"webgpu",kernelFunc:Uh};function uhe(e,t){let r=new Vh(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function t2(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Vn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=t2({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Cd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=Uh({inputs:{input:a},backend:r}),o=t2({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=Vn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return uhe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=_8({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var dhe={kernelName:Ys,backendName:"webgpu",kernelFunc:t2},phe=kr({opType:1,cpuKernelImpl:ope}),hhe={kernelName:Js,backendName:"webgpu",kernelFunc:phe},che=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
-      ${tt()}
+    `}},r2=["x","y","z","w","u","v"];function che(e){if(e===1)return"sourceLoc";if(e<=6)return r2.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Ed(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=Bpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new hhe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var fhe={kernelName:gl,backendName:"webgpu",kernelFunc:Ed},mhe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Ya({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=Ed({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},ghe={kernelName:Uo,backendName:"webgpu",kernelFunc:mhe},L8=jr({opSnippet:10,dtype:"bool",cpuKernelImpl:_pe}),yhe={kernelName:sl,backendName:"webgpu",kernelFunc:L8};function Uh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Pn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Ahe={kernelName:ah,backendName:"webgpu",kernelFunc:Uh};function xhe(e,t){let r=new Vh(e.shape,22),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function n2(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Pn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=n2({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Cd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=Uh({inputs:{input:a},backend:r}),o=n2({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=Pn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return xhe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=L8({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var bhe={kernelName:Js,backendName:"webgpu",kernelFunc:n2},vhe=kr({opType:1,cpuKernelImpl:ype}),whe={kernelName:Qs,backendName:"webgpu",kernelFunc:vhe},khe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+      ${et()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
           var clampedValue : vec4<f32>;
@@ -5772,8 +5772,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, clampedValue);
         }
       }
-    `}},fhe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
-      ${tt()}
+    `}},Ihe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+      ${et()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
           if (isnan(value)) {
@@ -5783,8 +5783,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
         }
       }
-    `}};function mhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return w.sizeFromShape(a.shape)%4===0?o=new che(a.shape):o=new fhe(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var ghe={kernelName:Ja,backendName:"webgpu",kernelFunc:mhe},yhe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
-      ${tt()}
+    `}};function She(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{clipValueMin:s,clipValueMax:i}=n,o,l=[{type:"float32",data:[s]},{type:"float32",data:[i]}];return w.sizeFromShape(a.shape)%4===0?o=new khe(a.shape):o=new Ihe(a.shape),r.runWebGPUProgram(o,[a],a.dtype,l)}var The={kernelName:Qa,backendName:"webgpu",kernelFunc:She},Nhe=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let n=1;n<this.offsetLength;n++)e.push(`else if (yC < uniforms.offset${[n]}){ setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${n-1})); }`);let t=this.offsetLength,r=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${t}(yR, yC - uniforms.offset${r})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+      ${et()}
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
           if(flatIndex < uniforms.size) {
@@ -5797,8 +5797,8 @@ return a / b;`,qie=`
           }
         }
       }
-    `}};function w0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Vn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Ahe={kernelName:eh,backendName:"webgpu",kernelFunc:w0};function r2(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>Uh({inputs:{input:A},backend:r})),f=e.map(A=>w0({inputs:{input:A},backend:r})),m=r2(c,t,r),g=r2(f,t,r),y=Cd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=lpe(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=xhe(e,t,r),o=s.map(c=>c.shape),l=new yhe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function xhe(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function z8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return Vn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),r2(o,s,r)}var bhe={kernelName:Wo,backendName:"webgpu",kernelFunc:z8},vhe=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
-      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.hasLeakyreluAlpha=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],n=this.outputShape[3],a=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Za(e,[r,a]),Za(t,[a,n])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
+    `}};function S0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Pn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var Che={kernelName:eh,backendName:"webgpu",kernelFunc:S0};function a2(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>Uh({inputs:{input:A},backend:r})),f=e.map(A=>S0({inputs:{input:A},backend:r})),m=a2(c,t,r),g=a2(f,t,r),y=Cd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=Ape(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=Ehe(e,t,r),o=s.map(c=>c.shape),l=new Nhe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function Ehe(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function B8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return Pn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),a2(o,s,r)}var Rhe={kernelName:Go,backendName:"webgpu",kernelFunc:B8},Mhe=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.hasLeakyreluAlpha=a,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],r=this.outputShape[1]*this.outputShape[2],n=this.outputShape[3],a=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Za(e,[r,a]),Za(t,[a,n])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
     let divBy4Remainder${e} = flatIndex${e} % 4;
     let divBy4Index${e} = flatIndex${e} / 4;
     let curData${e} = x[divBy4Index${e}];
@@ -5817,7 +5817,7 @@ return a / b;`,qie=`
         temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
       }
     }
-    `}getUserCode(){let e=E8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
+    `}getUserCode(){let e=$8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
         let outCol = r % uniforms.outShape[2];
         let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
         let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
@@ -5857,7 +5857,7 @@ return a / b;`,qie=`
            return W[row * uniforms.dimBOuter / 4 + col];
          }
          return vec4<f32>(0.0);
-        `,a="",s="";if(this.activation){let o=ss(this.activation,this.isVec4);if(this.hasPreluActivationWeights)a=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+        `,a="",s="";if(this.activation){let o=is(this.activation,this.isVec4);if(this.hasPreluActivationWeights)a=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${o}
         }`;else{if(this.hasLeakyreluAlpha)throw a=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
@@ -5896,7 +5896,7 @@ return a / b;`,qie=`
           }
         }
         ${e}
-      `}},whe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Ab(this.dispatchLayout,this.outputShape),this.elementsPerThread=xb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Za(n,[s,o]),Za(a,[o,i])]}getUserCode(){let e=vb(this.elementsPerThread,this.workGroupSize),t=`
+      `}},Fhe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=bb(this.dispatchLayout,this.outputShape),this.elementsPerThread=vb(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Za(n,[s,o]),Za(a,[o,i])]}getUserCode(){let e=kb(this.elementsPerThread,this.workGroupSize),t=`
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -5920,7 +5920,7 @@ return a / b;`,qie=`
            return W[row * uniforms.dimBOuter + col];
 	 }
 	 return 0.0;
-	 `,a="",s="";if(this.activation){let o=ss(this.activation,!1);this.hasPreluActivationWeights?a=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
+	 `,a="",s="";if(this.activation){let o=is(this.activation,!1);this.hasPreluActivationWeights?a=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                   let b = getPreluActivationWeightsByOutputCoords(outCoord);
                   ${o}
                 }`:a=`
@@ -5951,7 +5951,7 @@ return a / b;`,qie=`
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
     ${e}
-  `}},khe=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=ss(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
+  `}},$he=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=is(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${n}
              }`:e=`
@@ -5985,7 +5985,7 @@ return a / b;`,qie=`
         }
       }
 
-      ${Ui()}
+      ${Hi()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let outChannel = coords[3];
@@ -6006,9 +6006,9 @@ return a / b;`,qie=`
 
         writeResult(batch, coords[1], coords[2], outChannel, acc);
       }
-    `}},Ihe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
-      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
-    ${tt()}
+    `}},Phe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
+      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
+    ${et()}
 
       for(var i = 0; i<${this.workPerThread}; i = i + 1) {
         let flatIndex = index * ${this.workPerThread} + i;
@@ -6036,7 +6036,7 @@ return a / b;`,qie=`
         }
       }
     }
-  `}};function She({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=wb({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function The({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,v=m*f,S=[v,b],T=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new Ihe(S,x),z=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],O=n.runWebGPUProgram(I,[_],_.dtype,z),j=qe({inputs:{x:O},backend:n,attrs:{shape:[1,S[0],S[1]]}});R.push(O),R.push(j);let X=[1,S[0],S[1]],D=new R8(X,[1,v,r.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),T,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,J=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],se=[j,M];a&&se.push(a),s&&se.push(s),o==="leakyrelu"&&(z.push({type:"float32",data:[i]}),D.uniforms+=" alpha : f32,");let Z=n.runWebGPUProgram(D,se,j.dtype,J),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function O8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return She({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return The({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new khe(r,l,o,u);else{p?d=new vhe(r,l,o,u):d=new whe(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),o==="leakyrelu"&&(f.push({type:"float32",data:[i]}),d.uniforms+=" alpha : f32,"),n.runWebGPUProgram(d,m,e.dtype,f)}function Nhe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return O8({x:a,filter:s,convInfo:p,backend:n})}var Che={kernelName:Qs,backendName:"webgpu",kernelFunc:Nhe},Ehe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Ab(this.dispatchLayout,this.outputShape),this.elementsPerThread=xb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+  `}};function _he({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=Ib({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function zhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,v=m*f,S=[v,b],T=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new Phe(S,x),z=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],O=n.runWebGPUProgram(I,[_],_.dtype,z),j=qe({inputs:{x:O},backend:n,attrs:{shape:[1,S[0],S[1]]}});R.push(O),R.push(j);let X=[1,S[0],S[1]],D=new P8(X,[1,v,r.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),T,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,J=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],se=[j,M];a&&se.push(a),s&&se.push(s),o==="leakyrelu"&&(z.push({type:"float32",data:[i]}),D.uniforms+=" alpha : f32,");let Z=n.runWebGPUProgram(D,se,j.dtype,J),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function W8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return _he({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return zhe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new $he(r,l,o,u);else{p?d=new Mhe(r,l,o,u):d=new Fhe(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),o==="leakyrelu"&&(f.push({type:"float32",data:[i]}),d.uniforms+=" alpha : f32,"),n.runWebGPUProgram(d,m,e.dtype,f)}function Ohe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return W8({x:a,filter:s,convInfo:p,backend:n})}var Dhe={kernelName:ei,backendName:"webgpu",kernelFunc:Ohe},Lhe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=bb(this.dispatchLayout,this.outputShape),this.elementsPerThread=vb(this.dispatchLayout,this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6089,9 +6089,9 @@ return a / b;`,qie=`
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
 
-    ${vb(this.elementsPerThread,this.workGroupSize)}
-  `}},Rhe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
-    ${tt()} {
+    ${kb(this.elementsPerThread,this.workGroupSize)}
+  `}},Bhe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
+    ${et()} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
         let batch = coords[0];
@@ -6139,8 +6139,8 @@ return a / b;`,qie=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function Mhe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Rhe(p);else{f=new Ehe(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(f,[a,s],"float32",c)}var Fhe={kernelName:ei,backendName:"webgpu",kernelFunc:Mhe},$he=kr({opType:2}),Phe={kernelName:ti,backendName:"webgpu",kernelFunc:$he},_he=kr({opType:3}),zhe={kernelName:ri,backendName:"webgpu",kernelFunc:_he},Ohe=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
-      ${tt()}
+  `}};function Whe(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Bhe(p);else{f=new Lhe(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;c.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return r.runWebGPUProgram(f,[a,s],"float32",c)}var Vhe={kernelName:ti,backendName:"webgpu",kernelFunc:Whe},Uhe=kr({opType:2}),Ghe={kernelName:ri,backendName:"webgpu",kernelFunc:Uhe},jhe=kr({opType:3}),Hhe={kernelName:ni,backendName:"webgpu",kernelFunc:jhe},qhe=class{constructor(e,t,r,n){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[a]=t;this.outputShape=[a,r[0],r[1],e],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=n==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[r,n,a]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[s,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
+      ${et()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
         let height_ratio = f32(${r});
@@ -6195,8 +6195,24 @@ return a / b;`,qie=`
         }
       }
     }
-    `}},Dhe=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Ohe(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},Lhe={kernelName:Uo,backendName:"webgpu",kernelFunc:Dhe},Bhe=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
-      ${tt()}
+    `}},Khe=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new qhe(a.shape[3],s.shape,o,l),h=[{type:"float32",data:[u]}];return r.runWebGPUProgram(d,[a,s,i],"float32",h)},Xhe={kernelName:Ho,backendName:"webgpu",kernelFunc:Khe},lw=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let a=128;this.workGroupSize=[a,1,1],this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=r,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",r=this.exclusive?t:`getX(${uw(e,"coords",this.op)})`,n=this.outputShape[this.outputShape.length-1],a="",s="";return this.exclusive?(a=this.reverse?`end != ${n-1}`:"end != 0",s=this.reverse?"end + 1":"end - 1"):(a=this.reverse?`end + pow2 < ${n}`:"end >= pow2",s=this.reverse?"end + pow2":"end - pow2"),`
+      ${et()}
+       if (index < uniforms.size) {
+         var coords = getCoordsFromIndex(index);
+
+         let end = ${dw(e,"coords",this.op)};
+         var val = ${r};
+         let pow2 = i32(pow(2.0, uniforms.index));
+         if (${a}) {
+           let idx = ${s};
+           ${dw(e,"coords",this.op)} = idx;
+           val ${this.op}= getX(${uw(e,"coords",this.op)});
+         }
+         setOutputAtIndex(index, val);
+       }
+      }
+    `}};function uw(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function dw(e,t,r){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${r} for rank ${e} is not yet supported`)}function V8(e,t,r,n,a,s){let i=t.shape.length,o=N.getAxesPermutation([n],i),l=t;o!=null&&(l=Ya({inputs:{x:t},backend:r,attrs:{perm:o}}));let u=N.getInnerMostAxes(1,i)[0];if(u!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${n}`);let d=l.shape[u],h=Pn({inputs:{x:l},backend:r});for(let p=0;p<=Math.ceil(Math.log2(d))-1;p++){let c=new lw(e,l.shape,!1,s),f=h,m=[{type:"float32",data:[p]}];h=r.runWebGPUProgram(c,[h],h.dtype,m),r.disposeData(f.dataId)}if(a){let p=new lw(e,l.shape,a,s),c=h,f=[{type:"float32",data:[0]}];h=r.runWebGPUProgram(p,[h],h.dtype,f),r.disposeData(c.dataId)}if(o!=null){let p=N.getUndoAxesPermutation(o),c=Ya({inputs:{x:h},backend:r,attrs:{perm:p}});return r.disposeData(h.dataId),r.disposeData(l.dataId),c}return h}function Zhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return V8("*",a,r,s,i,o)}var Yhe={kernelName:jo,backendName:"webgpu",kernelFunc:Zhe};function Jhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;return V8("+",a,r,s,i,o)}var Qhe={kernelName:ai,backendName:"webgpu",kernelFunc:Jhe},ece=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+      ${et()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let b = coords[0];
@@ -6215,7 +6231,7 @@ return a / b;`,qie=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Whe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new Bhe(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var Vhe={kernelName:Go,backendName:"webgpu",kernelFunc:Whe},D8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=ss(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function tce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new ece(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var rce={kernelName:qo,backendName:"webgpu",kernelFunc:tce},U8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=is(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${n}
         }`:e=`
@@ -6225,7 +6241,7 @@ return a / b;`,qie=`
         `,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
       ${e}
 
-      ${yb()}
+      ${xb()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -6287,9 +6303,9 @@ return a / b;`,qie=`
           }
         }
       }
-    `}},L8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+    `}},G8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
-      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=ss(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=is(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${n}
         }`:e=`
@@ -6307,7 +6323,7 @@ return a / b;`,qie=`
         }
       }
 
-      ${Ui()}
+      ${Hi()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
@@ -6369,7 +6385,7 @@ return a / b;`,qie=`
         ${t}
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
-    `}};function Uhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new D8(h):(c=new L8(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var Ghe={kernelName:ni,backendName:"webgpu",kernelFunc:Uhe},B8=jr({opSnippet:0,cpuKernelImpl:kpe,supportsComplex:!0}),jhe={kernelName:vi,backendName:"webgpu",kernelFunc:B8},Hhe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+    `}};function nce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.filterHeight===3&&h.inChannels%4===0?c=new U8(h):(c=new G8(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var ace={kernelName:si,backendName:"webgpu",kernelFunc:nce},j8=jr({opSnippet:0,cpuKernelImpl:$pe,supportsComplex:!0}),sce={kernelName:ki,backendName:"webgpu",kernelFunc:j8},ice=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
          if (isnan(candidate)) {
           bestValue = uniforms.NAN;
          } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
@@ -6386,7 +6402,7 @@ return a / b;`,qie=`
          let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
           return offset;
        }
-       ${tt()}
+       ${et()}
          let outputIndex = index / i32(workGroupSizeX);
          let offset = getOffset(outputIndex);
          var bestValue = ${t};
@@ -6417,14 +6433,14 @@ return a / b;`,qie=`
           ${r}
         }
        }
-     `}};function Gh(e,t,r,n,a){let s=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=zl({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=bpe(m,w.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=Tpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),g=w.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":ch(e.dtype),x=[{type:"int32",data:[m]}],b=new Hhe(y,n),v=a.runWebGPUProgram(b,[d],A,x);i.push(v),f=qe({inputs:{x:v},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function kb(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Gh(a,s,i,"sum",r)}var qhe={kernelName:Fi,backendName:"webgpu",kernelFunc:kb};function Khe(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=zl({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=B8({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=kb({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var Xhe={kernelName:Qp,backendName:"webgpu",kernelFunc:Khe},Zhe=kr({opType:4}),Yhe={kernelName:si,backendName:"webgpu",kernelFunc:Zhe},Jhe=jr({opSnippet:4,dtype:"bool",cpuKernelImpl:upe}),Qhe={kernelName:jo,backendName:"webgpu",kernelFunc:Jhe},W8=kr({opType:5,cpuKernelImpl:dpe,dtype:"float32"}),ece={kernelName:ii,backendName:"webgpu",kernelFunc:W8};function n2(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var tce={kernelName:Ho,backendName:"webgpu",kernelFunc:n2},rce=kr({opType:6,cpuKernelImpl:ppe}),nce={kernelName:qo,backendName:"webgpu",kernelFunc:rce},ace=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
-    ${tt()}
+     `}};function Gh(e,t,r,n,a){let s=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=Ya({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=Rpe(m,w.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=zpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),g=w.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":ch(e.dtype),x=[{type:"int32",data:[m]}],b=new ice(y,n),v=a.runWebGPUProgram(b,[d],A,x);i.push(v),f=qe({inputs:{x:v},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function Sb(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Gh(a,s,i,"sum",r)}var oce={kernelName:_i,backendName:"webgpu",kernelFunc:Sb};function lce(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Ya({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=j8({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=Sb({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var uce={kernelName:Qp,backendName:"webgpu",kernelFunc:lce},dce=kr({opType:4}),pce={kernelName:oi,backendName:"webgpu",kernelFunc:dce},hce=jr({opSnippet:4,dtype:"bool",cpuKernelImpl:xpe}),cce={kernelName:Ko,backendName:"webgpu",kernelFunc:hce},H8=kr({opType:5,cpuKernelImpl:bpe,dtype:"float32"}),fce={kernelName:li,backendName:"webgpu",kernelFunc:H8};function s2(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var mce={kernelName:Xo,backendName:"webgpu",kernelFunc:s2},gce=kr({opType:6,cpuKernelImpl:vpe}),yce={kernelName:Zo,backendName:"webgpu",kernelFunc:gce},Ace=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+    ${et()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function Rd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new ace(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var sce={kernelName:Hu,backendName:"webgpu",kernelFunc:Rd},ice=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
-      ${tt()}
+  `}};function Rd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Ace(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var xce={kernelName:Hu,backendName:"webgpu",kernelFunc:Rd},bce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+      ${et()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let coordX = uniforms.xShape[2] - coords[2] - 1;
@@ -6432,8 +6448,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},oce={kernelName:Ko,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new ice(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},lce=kr({opType:7,cpuKernelImpl:hpe}),uce={kernelName:oi,backendName:"webgpu",kernelFunc:lce},dce=jr({opSnippet:12,dtype:"int32"}),pce={kernelName:li,backendName:"webgpu",kernelFunc:dce},hce=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},V8=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=Bde(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function U8(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function ow(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=w.sizeFromShape(a),l=w.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=U8(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>V8(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var cce={kernelName:Pp,backendName:"webgpu",kernelFunc:fce},lu;function fce(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Y().getBool("WEBGPU_USE_IMPORT")&&i)return ow({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(lu==null&&(lu=document.createElement("canvas").getContext("2d")),lu.canvas.width=d,lu.canvas.height=h,lu.drawImage(a,0,0,d,h),a=lu.canvas),u||l||i||o)return ow({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var mce=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
-      ${tt()}
+    `}},vce={kernelName:Yo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new bce(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},wce=kr({opType:7,cpuKernelImpl:wpe}),kce={kernelName:ui,backendName:"webgpu",kernelFunc:wce},Ice=jr({opSnippet:12,dtype:"int32"}),Sce={kernelName:di,backendName:"webgpu",kernelFunc:Ice},Tce=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},q8=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=Zde(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function K8(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function pw(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=w.sizeFromShape(a),l=w.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=K8(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>q8(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var Nce={kernelName:Pp,backendName:"webgpu",kernelFunc:Cce},uu;function Cce(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(Y().getBool("WEBGPU_USE_IMPORT")&&i)return pw({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(uu==null&&(uu=document.createElement("canvas").getContext("2d")),uu.canvas.width=d,uu.canvas.height=h,uu.drawImage(a,0,0,d,h),a=uu.canvas),u||l||i||o)return pw({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var Ece=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+      ${et()}
         if (index < uniforms.size)
         {
           let xValue = getXByOutputIndex(index);
@@ -6445,8 +6461,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},gce={kernelName:ui,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new mce(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function yce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return O8({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var Ace={kernelName:Fs,backendName:"webgpu",kernelFunc:yce};function xce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=d;f==null&&(f=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=N.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?b=new D8(m,y,p,A):(b=new L8(m,y,p,A),x.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var bce={kernelName:$s,backendName:"webgpu",kernelFunc:xce},vce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${gr(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
-        ${tt()}
+  `}},Rce={kernelName:pi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new Ece(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function Mce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return W8({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var Fce={kernelName:$s,backendName:"webgpu",kernelFunc:Mce};function $ce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=d;f==null&&(f=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=N.computeConv2DInfo(a.shape,s.shape,l,f,u,h,!0),g=[a,s],y=i!=null,A=o!=null;y&&g.push(i),A&&g.push(o);let x=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?b=new U8(m,y,p,A):(b=new G8(m,y,p,A),x.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),p==="leakyrelu"&&(x.push({type:"float32",data:[c]}),b.uniforms+=" alpha : f32,"),r.runWebGPUProgram(b,g,"float32",x)}var Pce={kernelName:Ps,backendName:"webgpu",kernelFunc:$ce},_ce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${gr(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+        ${et()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           var flattenIndex = 0;
@@ -6459,8 +6475,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function wce(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=cpe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let f=new vce(i,[u,d]),m=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(f,[c,p],c.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var kce={kernelName:Zo,backendName:"webgpu",kernelFunc:wce},Ice=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Sce(this.aShape);return`
-      ${tt()}
+      `}};function zce(e){let{inputs:t,backend:r}=e,{params:n,indices:a}=t,s=a.shape,i=s[s.length-1],o=w.sizeFromShape(n.shape),[l,u,d,h]=N.prepareAndValidate(n,a),p=qe({inputs:{x:a},backend:r,attrs:{shape:[u,i]}}),c=qe({inputs:{x:n},backend:r,attrs:{shape:[w.sizeFromShape(n.shape)/d,d]}});if(r.shouldExecuteOnCPU([n,a])||n.dtype==="string"){let A=r.readSync(a.dataId),x=r.bufferSync(n),b=kpe(A,x,n.dtype,u,i,d,h,n.shape,o);return r.makeTensorInfo(l,n.dtype,b.values)}let f=new _ce(i,[u,d]),m=[{type:"int32",data:[i]},{type:"int32",data:h}],g=r.runWebGPUProgram(f,[c,p],c.dtype,m),y=qe({inputs:{x:g},backend:r,attrs:{shape:l}});return r.disposeData(p.dataId),r.disposeData(c.dataId),r.disposeData(g.dataId),y}var Oce={kernelName:Qo,backendName:"webgpu",kernelFunc:zce},Dce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Lce(this.aShape);return`
+      ${et()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           let indexZ = i32(getIndices(resRC.x, resRC.z));
@@ -6468,8 +6484,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function Sce(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function G8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,v=We(p.shape,p.dtype,b),S=fpe(v,x,f);return h.forEach(T=>r.disposeData(T.dataId)),r.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new Ice(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var Tce={kernelName:Xo,backendName:"webgpu",kernelFunc:G8},Nce=jr({opSnippet:5,cpuKernelImpl:gpe,dtype:"bool"}),Cce={kernelName:Yo,backendName:"webgpu",kernelFunc:Nce},Ece=jr({opSnippet:6,dtype:"bool",cpuKernelImpl:mpe}),Rce={kernelName:di,backendName:"webgpu",kernelFunc:Ece};function Mce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Vh(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var Fce={kernelName:hi,backendName:"webgpu",kernelFunc:Mce},$ce=jr({opSnippet:7,dtype:"bool",cpuKernelImpl:Ape}),Pce={kernelName:Jo,backendName:"webgpu",kernelFunc:$ce},_ce=jr({opSnippet:8,dtype:"bool",cpuKernelImpl:ype}),zce={kernelName:Qo,backendName:"webgpu",kernelFunc:_ce},Oce=kr({opType:9,cpuKernelImpl:xpe}),Dce={kernelName:ci,backendName:"webgpu",kernelFunc:Oce},Lce=jr({opSnippet:9,dtype:"bool"}),Bce={kernelName:el,backendName:"webgpu",kernelFunc:Lce},Wce=kr({opType:10}),Vce={kernelName:Yu,backendName:"webgpu",kernelFunc:Wce};function j8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return Gh(a,s,i,"max",r)}var Uce={kernelName:fi,backendName:"webgpu",kernelFunc:j8},Gce=jr({opSnippet:15,cpuKernelImpl:vpe}),jce={kernelName:mi,backendName:"webgpu",kernelFunc:Gce};function Hce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(w.arraysEqual(d.inShape,d.outShape))return Vn({inputs:{x:a},backend:r});h=new P8(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new $8(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var qce={kernelName:gi,backendName:"webgpu",kernelFunc:Hce};function Kce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return Gh(a,i,s,"mean",r)}var Xce={kernelName:yi,backendName:"webgpu",kernelFunc:Kce};function Zce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Gh(a,s,i,"min",r)}var Yce={kernelName:Ai,backendName:"webgpu",kernelFunc:Zce},Jce=jr({opSnippet:16,cpuKernelImpl:wpe}),Qce={kernelName:xi,backendName:"webgpu",kernelFunc:Jce},efe=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=gr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
-      ${tt()}
+    `}};function Lce(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[];for(let n=0;n<e.length;n++)n===2?r.push("indexZ"):r.push(`${t[n]}`);return r.join()}function X8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,v=We(p.shape,p.dtype,b),S=Ipe(v,x,f);return h.forEach(T=>r.disposeData(T.dataId)),r.makeTensorInfo(u.outputShape,S.dtype,S.values)}let m=new Dce(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var Bce={kernelName:Jo,backendName:"webgpu",kernelFunc:X8},Wce=jr({opSnippet:5,cpuKernelImpl:Tpe,dtype:"bool"}),Vce={kernelName:el,backendName:"webgpu",kernelFunc:Wce},Uce=jr({opSnippet:6,dtype:"bool",cpuKernelImpl:Spe}),Gce={kernelName:hi,backendName:"webgpu",kernelFunc:Uce};function jce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Vh(a.shape,14);return o.uniforms="alpha : f32,",r.runWebGPUProgram(o,[a],"float32",i)}var Hce={kernelName:fi,backendName:"webgpu",kernelFunc:jce},qce=jr({opSnippet:7,dtype:"bool",cpuKernelImpl:Cpe}),Kce={kernelName:tl,backendName:"webgpu",kernelFunc:qce},Xce=jr({opSnippet:8,dtype:"bool",cpuKernelImpl:Npe}),Zce={kernelName:rl,backendName:"webgpu",kernelFunc:Xce},Yce=kr({opType:9,cpuKernelImpl:Epe}),Jce={kernelName:mi,backendName:"webgpu",kernelFunc:Yce},Qce=jr({opSnippet:9,dtype:"bool"}),efe={kernelName:nl,backendName:"webgpu",kernelFunc:Qce},tfe=kr({opType:10}),rfe={kernelName:Yu,backendName:"webgpu",kernelFunc:tfe};function Z8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return Gh(a,s,i,"max",r)}var nfe={kernelName:gi,backendName:"webgpu",kernelFunc:Z8},afe=jr({opSnippet:15,cpuKernelImpl:Mpe}),sfe={kernelName:yi,backendName:"webgpu",kernelFunc:afe};function ife(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(w.arraysEqual(d.inShape,d.outShape))return Pn({inputs:{x:a},backend:r});h=new D8(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new O8(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var ofe={kernelName:Ai,backendName:"webgpu",kernelFunc:ife};function lfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return Gh(a,i,s,"mean",r)}var ufe={kernelName:xi,backendName:"webgpu",kernelFunc:lfe};function dfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Gh(a,s,i,"min",r)}var pfe={kernelName:bi,backendName:"webgpu",kernelFunc:dfe},hfe=jr({opSnippet:16,cpuKernelImpl:Fpe}),cfe={kernelName:vi,backendName:"webgpu",kernelFunc:hfe},ffe=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>,`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=gr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+      ${et()}
         if (index < uniforms.size) {
           let start = ${i}(${t});
           let end = ${i}(${r});
@@ -6485,8 +6501,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, getX(${o}));
         }
       }
-    `}},tfe={kernelName:bi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new efe(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function rfe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=Ipe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Vh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var nfe={kernelName:tl,backendName:"webgpu",kernelFunc:rfe};function afe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=qn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var sfe={kernelName:nl,backendName:"webgpu",kernelFunc:afe};function ife(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=qn.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var ofe={kernelName:al,backendName:"webgpu",kernelFunc:ife};function Lf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Uh({inputs:{input:n},backend:r}),s=Lf({inputs:{x:a},backend:r}),i=w0({inputs:{input:n},backend:r}),o=Lf({inputs:{x:i},backend:r}),l=Cd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Rd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var lfe={kernelName:kl,backendName:"webgpu",kernelFunc:Lf};function H8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Uh({inputs:{input:n},backend:r}),s=H8({inputs:{x:a},backend:r}),i=w0({inputs:{input:n},backend:r}),o=Lf({inputs:{x:i},backend:r}),l=Cd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Rd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var ufe={kernelName:sl,backendName:"webgpu",kernelFunc:H8};function dfe(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return n2({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=n2({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=z8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var pfe={kernelName:ol,backendName:"webgpu",kernelFunc:dfe},hfe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=gr(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
-      ${tt()}
+    `}},mfe={kernelName:wi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new ffe(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function gfe(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=Ppe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Vh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var yfe={kernelName:al,backendName:"webgpu",kernelFunc:gfe};function Afe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=qn.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var xfe={kernelName:il,backendName:"webgpu",kernelFunc:Afe};function bfe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=qn.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var vfe={kernelName:ol,backendName:"webgpu",kernelFunc:bfe};function Vf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Uh({inputs:{input:n},backend:r}),s=Vf({inputs:{x:a},backend:r}),i=S0({inputs:{input:n},backend:r}),o=Vf({inputs:{x:i},backend:r}),l=Cd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Rd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var wfe={kernelName:Sl,backendName:"webgpu",kernelFunc:Vf};function Y8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Uh({inputs:{input:n},backend:r}),s=Y8({inputs:{x:a},backend:r}),i=S0({inputs:{input:n},backend:r}),o=Vf({inputs:{x:i},backend:r}),l=Cd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return Rd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var kfe={kernelName:ll,backendName:"webgpu",kernelFunc:Y8};function Ife(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return s2({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=s2({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=B8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var Sfe={kernelName:dl,backendName:"webgpu",kernelFunc:Ife},Tfe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=gr(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+      ${et()}
         if (index < uniforms.size) {
           let start = ${a};
           let end = ${s};
@@ -6500,8 +6516,8 @@ return a / b;`,qie=`
           }
         }
       }
-    `}},q8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>w.arraysEqual(u,[0,0])))return Vn({inputs:{x:a},backend:r});if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Rd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new hfe(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},cfe={kernelName:wi,backendName:"webgpu",kernelFunc:q8},ffe=jr({opSnippet:13}),mfe={kernelName:ki,backendName:"webgpu",kernelFunc:ffe};function gfe(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new M8(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var yfe={kernelName:Ii,backendName:"webgpu",kernelFunc:gfe};function Afe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Gh(a,s,i,"prod",r)}var xfe={kernelName:ll,backendName:"webgpu",kernelFunc:Afe},bfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Npe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},vfe={kernelName:ed,backendName:"webgpu",kernelFunc:bfe},K8=jr({opSnippet:3}),wfe={kernelName:ai,backendName:"webgpu",kernelFunc:K8},kfe=kr({opType:12}),Ife={kernelName:Si,backendName:"webgpu",kernelFunc:kfe},Sfe=kr({opType:13}),Tfe={kernelName:Ni,backendName:"webgpu",kernelFunc:Sfe},Nfe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
-      ${tt()}
+    `}},J8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>w.arraysEqual(u,[0,0])))return Pn({inputs:{x:a},backend:r});if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Rd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Tfe(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},Nfe={kernelName:Ii,backendName:"webgpu",kernelFunc:J8},Cfe=jr({opSnippet:13}),Efe={kernelName:Si,backendName:"webgpu",kernelFunc:Cfe};function Rfe(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new _8(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var Mfe={kernelName:Ti,backendName:"webgpu",kernelFunc:Rfe};function Ffe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Gh(a,s,i,"prod",r)}var $fe={kernelName:Ni,backendName:"webgpu",kernelFunc:Ffe},Pfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Ope(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},_fe={kernelName:ed,backendName:"webgpu",kernelFunc:Pfe},Q8=jr({opSnippet:3}),zfe={kernelName:ii,backendName:"webgpu",kernelFunc:Q8},Ofe=kr({opType:12}),Dfe={kernelName:Ci,backendName:"webgpu",kernelFunc:Ofe},Lfe=kr({opType:13}),Bfe={kernelName:Ri,backendName:"webgpu",kernelFunc:Lfe},Wfe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+      ${et()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
           let b = coords[0];
@@ -6543,8 +6559,8 @@ return a / b;`,qie=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Cfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new Nfe(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var Efe={kernelName:Ti,backendName:"webgpu",kernelFunc:Cfe},Rfe=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
-      ${tt()}
+    `}};function Vfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,size:i,halfPixelCenters:o}=n,[l,u]=i,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[o?.5:0]}],c=new Wfe(a.shape,l,u);return r.runWebGPUProgram(c,[a],"float32",p)}var Ufe={kernelName:Ei,backendName:"webgpu",kernelFunc:Vfe},Gfe=class{constructor(e,t,r,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
+      ${et()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let b = coords[0];
@@ -6574,9 +6590,9 @@ return a / b;`,qie=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Mfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new Rfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var Ffe={kernelName:rd,backendName:"webgpu",kernelFunc:Mfe},$fe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+    `}};function jfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new Gfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var Hfe={kernelName:rd,backendName:"webgpu",kernelFunc:jfe},qfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
-        ${tt()}
+        ${et()}
 
           if (index < uniforms.size) {
             let coords = getCoordsFromIndex(index);
@@ -6596,7 +6612,7 @@ return a / b;`,qie=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},Pfe={kernelName:Il,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new $fe(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},_fe=kr({opType:15,cpuKernelImpl:Cpe}),zfe={kernelName:Ci,backendName:"webgpu",kernelFunc:_fe},Ofe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Xe(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=gr(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
+      `}},Kfe={kernelName:Tl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{image:n}=e,{radians:a,fillValue:s,center:i}=t,o=r,l=new qfe(n.shape,s),[u,d]=N.getImageCenter(i,n.shape[1],n.shape[2]),h=[{type:"float32",data:[u]},{type:"float32",data:[d]},{type:"float32",data:[Math.sin(a)]},{type:"float32",data:[Math.cos(a)]}];return typeof s=="number"?h.push({type:"float32",data:[Number.parseFloat(s.toFixed(2))]}):h.push({type:"float32",data:s}),o.runWebGPUProgram(l,[n],n.dtype,h)}},Xfe=kr({opType:15,cpuKernelImpl:Dpe}),Zfe={kernelName:Mi,backendName:"webgpu",kernelFunc:Xfe},Yfe=class{constructor(e,t,r,n,a,s,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=s,this.type=i,this.dispatchLayout=Ke(e),this.dispatch=ze(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${r}_${n}_${this.sliceDimGreaterThanOne}_${i}`;let o=gr(a.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=n,this.indicesRank=r}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,r=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",n="",a="",s="";this.updatesRank===1?(n="coords[0]",a="flattenedIndex",s=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6619,7 +6635,7 @@ return a / b;`,qie=`
      `;return`
     ${s}
 
-      ${tt()}
+      ${et()}
 
         if (index < uniforms.size) {
           let coords = getUpdatesCoordsFromFlatIndex(index);
@@ -6633,8 +6649,8 @@ return a / b;`,qie=`
 
          ${o}
         }
-      }`}};function Dfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=Rd({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new Ofe(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),v=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),v}var Lfe={kernelName:hl,backendName:"webgpu",kernelFunc:Dfe},Bfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
-      ${tt()}
+      }`}};function Jfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=Rd({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new Yfe(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),v=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),v}var Qfe={kernelName:fl,backendName:"webgpu",kernelFunc:Jfe},eme=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
+      ${et()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           let cVal = getC(${e});
@@ -6645,8 +6661,8 @@ return a / b;`,qie=`
           }
         }
       }
-    `}};function Wfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new Bfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Cr(a.dtype,s.dtype))}var Vfe={kernelName:cl,backendName:"webgpu",kernelFunc:Wfe},Ufe=kr({opType:18}),Gfe={kernelName:Ri,backendName:"webgpu",kernelFunc:Ufe},jfe=kr({opType:16}),Hfe={kernelName:Ei,backendName:"webgpu",kernelFunc:jfe},qfe=kr({opType:17}),Kfe={kernelName:ml,backendName:"webgpu",kernelFunc:qfe},X8=jr({opSnippet:2,cpuKernelImpl:$pe,supportsComplex:!0}),Xfe={kernelName:_i,backendName:"webgpu",kernelFunc:X8};function Zfe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=j8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=X8({inputs:{a,b:u},backend:r}),h=W8({inputs:{x:d},backend:r}),p=kb({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=K8({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var Yfe={kernelName:$i,backendName:"webgpu",kernelFunc:Zfe},Jfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=q8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=zl({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},Qfe={kernelName:gl,backendName:"webgpu",kernelFunc:Jfe},eme=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=gr(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
-      ${tt()}
+    `}};function tme(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new eme(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Cr(a.dtype,s.dtype))}var rme={kernelName:ml,backendName:"webgpu",kernelFunc:tme},nme=kr({opType:18}),ame={kernelName:$i,backendName:"webgpu",kernelFunc:nme},sme=kr({opType:16}),ime={kernelName:Fi,backendName:"webgpu",kernelFunc:sme},ome=kr({opType:17}),lme={kernelName:yl,backendName:"webgpu",kernelFunc:ome},eT=jr({opSnippet:2,cpuKernelImpl:Upe,supportsComplex:!0}),ume={kernelName:Di,backendName:"webgpu",kernelFunc:eT};function dme(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=Z8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=eT({inputs:{a,b:u},backend:r}),h=H8({inputs:{x:d},backend:r}),p=Sb({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=Q8({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var pme={kernelName:zi,backendName:"webgpu",kernelFunc:dme},hme=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=J8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Ya({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},cme={kernelName:Al,backendName:"webgpu",kernelFunc:hme},fme=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=gr(a.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+      ${et()}
 
         let globalIndex = index * ${this.workPerThread};
         if (globalIndex < uniforms.size) {
@@ -6675,23 +6691,23 @@ return a / b;`,qie=`
             }
           }
         }
-      }`}};function tme(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new eme(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var rme={kernelName:lh,backendName:"webgpu",kernelFunc:tme};function nme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=Ed({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var ame={kernelName:yl,backendName:"webgpu",kernelFunc:nme},sme=kr({opType:19}),ime={kernelName:Mi,backendName:"webgpu",kernelFunc:sme},ome={kernelName:od,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Vh(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},lme=jr({opSnippet:11}),ume={kernelName:Pi,backendName:"webgpu",kernelFunc:lme},dme=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=gr(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
-       ${tt()}
+      }`}};function mme(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new fme(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var gme={kernelName:lh,backendName:"webgpu",kernelFunc:mme};function yme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=Ed({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Ame={kernelName:xl,backendName:"webgpu",kernelFunc:yme},xme=kr({opType:19}),bme={kernelName:Pi,backendName:"webgpu",kernelFunc:xme},vme={kernelName:od,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Vh(r.shape,20);return n.runWebGPUProgram(a,[r],r.dtype)}},wme=jr({opSnippet:11}),kme={kernelName:Oi,backendName:"webgpu",kernelFunc:wme},Ime=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=gr(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+       ${et()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function pme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=_t.computeOutShape(A,x,b),T=Ed({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});v=qe({inputs:{x:T},backend:r,attrs:{shape:f}}),r.disposeData(T.dataId)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),T=We(a.shape,a.dtype,S),E=Mpe(c,T,b,A);v=r.makeTensorInfo(f,a.dtype,E.values)}else{let S=new dme(c),T=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(S,[a],a.dtype,T);v=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return v}var hme={kernelName:Al,backendName:"webgpu",kernelFunc:pme};function cme(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Fpe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var fme={kernelName:uh,backendName:"webgpu",kernelFunc:cme},mme=kr({opType:21}),gme={kernelName:zi,backendName:"webgpu",kernelFunc:mme},yme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Ame(this.rank,"uniforms.");return`
-      ${tt()}
+     `}};function Sme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=_t.computeOutShape(A,x,b),T=Ed({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});v=qe({inputs:{x:T},backend:r,attrs:{shape:f}}),r.disposeData(T.dataId)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),T=We(a.shape,a.dtype,S),E=Wpe(c,T,b,A);v=r.makeTensorInfo(f,a.dtype,E.values)}else{let S=new Ime(c),T=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(S,[a],a.dtype,T);v=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return v}var Tme={kernelName:bl,backendName:"webgpu",kernelFunc:Sme};function Nme(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Vpe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var Cme={kernelName:uh,backendName:"webgpu",kernelFunc:Nme},Eme=kr({opType:21}),Rme={kernelName:Li,backendName:"webgpu",kernelFunc:Eme},Mme=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Fme(this.rank,"uniforms.");return`
+      ${et()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function Ame(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function xme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Ppe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new yme(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var bme={kernelName:Qa,backendName:"webgpu",kernelFunc:xme},vme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+    `}};function Fme(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e;a++)n.push(`(${r[a]} % ${t}aShape[${a}])`);return n.join()}function $me(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(r.shouldExecuteOnCPU([a])||a.dtype==="string"||a.shape.length>=5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Gpe(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new Mme(a.shape,s);return r.runWebGPUProgram(i,[a],a.dtype)}var Pme={kernelName:es,backendName:"webgpu",kernelFunc:$me},_me=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
-        ${tt()}
+        ${et()}
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
             let batch = outC[0];
@@ -6757,8 +6773,8 @@ return a / b;`,qie=`
             }
           }
         }
-      `}},wme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
-        ${tt()}
+      `}},zme=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+        ${et()}
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
             let batch = outC[0];
@@ -6816,7 +6832,7 @@ return a / b;`,qie=`
             }
           }
         }
-      `}};function uu(e,t){t!==null&&e.disposeData(t.dataId)}function lw(e){let t=1;for(;t<e;)t*=2;return t}function kme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[v,S]=_pe(b,o,a.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(S.shape,S.dtype,S.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Rd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=w.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=lw(s),p=lw(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,v,S)=>{let T=f(),E=new vme(S),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],_=c;c=r.runWebGPUProgram(E,T,"int32",R),uu(r,_)};for(let b=1;b<h;b*=2){let v=b*2;for(let S=b;S>=1;S/=2)m(v,S,[u,p])}for(let b=p;b>h;b/=2){let v=f(),S=new wme([u,b/2]),T=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(S,v,"int32",T),uu(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=Ed({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),uu(r,g);let y=G8({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});uu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),uu(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),uu(r,x),[y,c]}var Ime={kernelName:bl,backendName:"webgpu",kernelFunc:kme},Sme=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function du(e,t){t!==null&&e.disposeData(t.dataId)}function hw(e){let t=1;for(;t<e;)t*=2;return t}function Ome(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[v,S]=jpe(b,o,a.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(S.shape,S.dtype,S.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,Rd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=w.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=hw(s),p=hw(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,v,S)=>{let T=f(),E=new _me(S),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],_=c;c=r.runWebGPUProgram(E,T,"int32",R),du(r,_)};for(let b=1;b<h;b*=2){let v=b*2;for(let S=b;S>=1;S/=2)m(v,S,[u,p])}for(let b=p;b>h;b/=2){let v=f(),S=new zme([u,b/2]),T=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(S,v,"int32",T),du(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=Ed({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),du(r,g);let y=X8({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});du(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),du(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),du(r,x),[y,c]}var Dme={kernelName:wl,backendName:"webgpu",kernelFunc:Ome},Lme=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6880,7 +6896,7 @@ return a / b;`,qie=`
             return outputValue;
           }
 
-          ${tt()}
+          ${et()}
             if (index < uniforms.size) {
               let coords = getCoordsFromIndex(index);
               var outputValue : f32;
@@ -6932,10 +6948,10 @@ return a / b;`,qie=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function Tme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new Sme(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var Nme={kernelName:vl,backendName:"webgpu",kernelFunc:Tme};function Cme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=Ed({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var Eme={kernelName:wl,backendName:"webgpu",kernelFunc:Cme},Rme=[tpe,Dpe,Bpe,Upe,Xpe,Ype,Qpe,the,ihe,dhe,hhe,ghe,spe,bhe,Che,Fhe,Phe,zhe,Lhe,Vhe,Ghe,Xhe,Yhe,Qhe,ece,tce,nce,sce,oce,cce,uce,pce,gce,Ace,bce,kce,Tce,Cce,Rce,ape,Ahe,Fce,Pce,zce,Dce,Bce,Vce,Uce,jce,qce,Xce,Yce,Qce,tfe,jhe,nfe,sfe,ofe,ohe,ufe,pfe,cfe,mfe,yfe,xfe,vfe,lhe,wfe,Ife,Tfe,Qde,Efe,Ffe,Pfe,zfe,Lfe,Vfe,Gfe,Hfe,Kfe,ahe,hme,fme,Yfe,Qfe,rme,ame,ime,ome,ume,Xfe,qhe,gme,bme,Ime,Nme,qpe,Eme,lfe];for(let e of Rme)Gn(e);var Mme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=uw(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=uw(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function uw(e,t){return`${e}_${t}`}var Z8=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+        `}};function Bme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new Lme(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var Wme={kernelName:kl,backendName:"webgpu",kernelFunc:Bme};function Vme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=Ed({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var Ume={kernelName:Il,backendName:"webgpu",kernelFunc:Vme},Gme=[ppe,Kpe,Zpe,Qpe,she,ohe,uhe,phe,ghe,bhe,whe,The,mpe,Rhe,Dhe,Vhe,Ghe,Hhe,Xhe,Yhe,Qhe,rce,ace,uce,pce,cce,fce,mce,yce,xce,vce,Nce,kce,Sce,Rce,Fce,Pce,Oce,Bce,Vce,Gce,fpe,Che,Hce,Kce,Zce,Jce,efe,rfe,nfe,sfe,ofe,ufe,pfe,cfe,mfe,sce,yfe,xfe,vfe,yhe,kfe,Sfe,Nfe,Efe,Mfe,$fe,_fe,Ahe,zfe,Dfe,Bfe,upe,Ufe,Hfe,Kfe,Zfe,Qfe,rme,ame,ime,lme,fhe,Tme,Cme,pme,cme,gme,Ame,bme,vme,kme,ume,oce,Rme,Pme,Dme,Wme,nhe,Ume,wfe];for(let e of Gme)Gn(e);var jme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=cw(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=cw(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function cw(e,t){return`${e}_${t}`}var tT=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Ke(this.outputShape),this.dispatch=ze(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
-      ${tt()}
+      ${et()}
         let flatIndexBase = index * uniforms.numChannels;
         for (var i = 0; i < uniforms.numChannels; i = i + 1) {
           let flatIndex = flatIndexBase + i;
@@ -6946,10 +6962,10 @@ return a / b;`,qie=`
           }
         }
       }
-  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Fme=class extends Z8{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},$me=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),dw=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;w.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),w.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},Y8=class extends Fu{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!bb())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Mme(this.device),this.tensorMap=new qp(this,br()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return Y8.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=w.sizeFromShape(t)*Qy(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*Qy(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new Z8),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Fme),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=C8(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(a.shape)===0){let T=this.tensorMap.get(a.dataId);return T.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=dw(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(T=>T.shape),o="int32";i.map(T=>{s.push({type:o,data:T})});let l=w.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let T=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?T/4:T]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((T,E)=>{if(T.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. 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Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. 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supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=k0[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=Sl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),v=r.dataIdMap.get(b.dataId).id,S=new Uint8Array(new Int32Array(a.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return Q8(p,S,a.shape.length,c,T,s.shape.length,l,u,g,f,m,h||0,v),b}var zme={kernelName:Ms,backendName:"wasm",setupFunc:Pme,kernelFunc:_me};function Ir(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||r(l,Vt[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Ome=Ir(Lo);function Hr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Vt[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var Dme=!0,Lme=Hr(Ya,Dme),eT;function Bme(e){eT=e.wasm.cwrap(qs,null,["array","number","number","number"])}function Wme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return eT(s,a.length,Vt[n.dtype],i),n}var Vme={kernelName:qs,backendName:"wasm",setupFunc:Bme,kernelFunc:Wme};function I0(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var Ume={kernelName:pi,backendName:"wasm",kernelFunc:I0},tT;function Gme(e){tT=e.wasm.cwrap(Oi,null,["number","array","number","number","number","array","number"])}function js(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Hme(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=jme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=I0({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return tT(d,c,l.shape.length,Vt[l.dtype],h,p,s.length),u}function jme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Hme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var qme={kernelName:Oi,backendName:"wasm",kernelFunc:js,setupFunc:Gme};function Gi(e,t,r){let n=e.shape,a=e.shape.length,s=w.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=js({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var rT;function Kme(e){rT=e.wasm.cwrap(zu,null,["number, number, number"])}function Xme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Gi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;rT(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Zme={kernelName:zu,backendName:"wasm",setupFunc:Kme,kernelFunc:Xme},nT;function Yme(e){nT=e.wasm.cwrap(Ou,null,["number, number, number"])}function Jme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Gi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;nT(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Qme={kernelName:Ou,backendName:"wasm",setupFunc:Yme,kernelFunc:Jme},aT;function e0e(e){aT=e.wasm.cwrap(Ks,null,["number","number","number","number","number"])}function t0e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Gi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=w.sizeFromShape(c.shape),g=l.shape[d[0]];return aT(o,Vt[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var r0e={kernelName:Ks,backendName:"wasm",kernelFunc:t0e,setupFunc:e0e},sT;function n0e(e){sT=e.wasm.cwrap(Xs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function a0e(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),v=n.dataIdMap.get(b.dataId).id;return sT(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,v),b}var s0e={kernelName:Xs,backendName:"wasm",setupFunc:n0e,kernelFunc:a0e};function en(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var i0e={kernelName:ul,backendName:"wasm",kernelFunc:en},iT;function o0e(e){iT=e.wasm.cwrap(Zs,null,["number","array","number","number","array","number","number","number","number"])}function l0e(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=Sl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],v=en({inputs:{x:a},backend:r,attrs:{shape:x}}),S=en({inputs:{x:s},backend:r,attrs:{shape:b}}),T=r.dataIdMap.get(v.dataId).id,E=r.dataIdMap.get(S.dataId).id,R=i?v.shape[2]:v.shape[1],_=o?S.shape[1]:S.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],v.dtype),z=r.dataIdMap.get(I.dataId).id,O=new Uint8Array(new Int32Array(v.shape).buffer),j=new Uint8Array(new Int32Array(S.shape).buffer);return iT(T,O,v.shape.length,E,j,S.shape.length,i,o,z),r.disposeData(v.dataId),r.disposeData(S.dataId),I.shape=A,I}var u0e={kernelName:Zs,backendName:"wasm",setupFunc:o0e,kernelFunc:l0e};function zo(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=_t.parseSliceParams(t,r,n),o=_t.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=w.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=_t.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+w.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(i))),u}if(t.dtype==="string"){let f=Pf(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)d0e(l,d[0],p,s,i);else if(c===3)p0e(l,d[0],d[1],p,s,i);else if(c===4)h0e(l,d[0],d[1],d[2],p,s,i);else{let f=Pf(l,s,i,t.shape,t.dtype);p.set(f)}return u}function d0e(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function p0e(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function h0e(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var c0e={kernelName:fl,backendName:"wasm",kernelFunc:zo};function f0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=en({inputs:{x:a},backend:r,attrs:{shape:l}}),f=js({inputs:{x:c},backend:r,attrs:{perm:u}}),m=en({inputs:{x:f},backend:r,attrs:{shape:d}}),g=zo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var m0e={kernelName:Bo,backendName:"wasm",kernelFunc:f0e};function jh(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var g0e={kernelName:Ys,backendName:"wasm",kernelFunc:jh},y0e=Ir(Js),oT;function A0e(e){oT=e.wasm.cwrap(Ja,null,["number","number","number","number"])}function 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k0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h,dataFormat:p}=r,c=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(a.shape,s.shape,l,u,d,h,!1,c),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,A=f.padInfo.right,x=f.padInfo.bottom,b=f.padInfo.left,v=f.dilationHeight,S=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,R=f.inChannels,_=f.outChannels,M=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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B0e(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return mT(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,f.length,b),m}var W0e={kernelName:Go,backendName:"wasm",setupFunc:L0e,kernelFunc:B0e},gT;function V0e(e){gT=e.wasm.cwrap(ni,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function U0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=N.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,v=c.dilationWidth,S=c.strideHeight,T=c.strideWidth,E=c.inChannels,R=c.outChannels,_=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let M=n.makeOutput(c.outShape,"float32"),I=n.dataIdMap.get(M.dataId).id;return gT(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,g,y,A,x,_,b,v,S,T,E,R,I),M}var G0e={kernelName:ni,backendName:"wasm",setupFunc:V0e,kernelFunc:U0e},j0e=Ir(si),H0e=!1,q0e=Hr(jo,H0e,"bool"),K0e=Ir(ii,"float32");function a2(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),en({inputs:{x:a},backend:n,attrs:{shape:o}})}var X0e={kernelName:Ho,backendName:"wasm",kernelFunc:a2};function yT(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var Z0e={kernelName:Hu,backendName:"wasm",kernelFunc:yT},AT;function Y0e(e){AT=e.wasm.cwrap(Ko,null,["number","number","number","number","number","number"])}function J0e(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return AT(s,o,l,u,d,i),a}var Q0e={kernelName:Ko,backendName:"wasm",kernelFunc:J0e,setupFunc:Y0e},ege=Ir(oi),tge=!1,rge=Hr(li,tge),xT;function nge(e){xT=e.wasm.cwrap(ui,null,["number","number","number","number","number","number","number"])}function age(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return xT(d,h,p,c,f,a,g),m}var sge={kernelName:ui,backendName:"wasm",setupFunc:nge,kernelFunc:age},bT;function ige(e){bT=e.wasm.cwrap(Fs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function oge(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=k0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,S=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,z=m.strideHeight,O=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),J=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return bT(y,D,Q,V,A,v,S,b,T,E,R,_,X,M,I,z,O,j,x,g,se,f||0,J),ee}var lge={kernelName:Fs,backendName:"wasm",setupFunc:ige,kernelFunc:oge},vT;function uge(e){vT=e.wasm.cwrap($s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function dge(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=k0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,S=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,z=m.strideHeight,O=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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jge={kernelName:Ai,backendName:"wasm",setupFunc:Uge,kernelFunc:Gge},Hge=!1,qge=Hr(xi,Hge),ET=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(ET||{}),RT;function Kge(e){RT=e.wasm.cwrap(bi,null,["number","array","number","number","array","array","number","number"])}function Xge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return RT(i,u,t.shape.length,Vt[t.dtype],p,c,ET[a],l),o}var Zge={kernelName:bi,backendName:"wasm",kernelFunc:Xge,setupFunc:Kge},Yge=!0,Jge=Hr(vi,Yge),Qge=Ir(tl);function Sb(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var MT;function e1e(e){MT=e.wasm.cwrap(nl,"number",["number","number","number","number","number"])}function t1e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=MT(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=Sb(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var r1e={kernelName:nl,backendName:"wasm",setupFunc:e1e,kernelFunc:t1e},FT;function n1e(e){FT=e.wasm.cwrap(Qu,"number",["number","number","number","number","number","bool"])}function a1e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=FT(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Sb(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var s1e={kernelName:Qu,backendName:"wasm",setupFunc:n1e,kernelFunc:a1e},$T;function i1e(e){$T=e.wasm.cwrap(al,"number",["number","number","number","number","number","number"])}function o1e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=$T(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Sb(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([f],"float32",m);return[y,A]}var l1e={kernelName:al,backendName:"wasm",setupFunc:i1e,kernelFunc:o1e},u1e=!1,d1e=Hr(rl,u1e,"bool"),PT;function p1e(e){PT=e.wasm.cwrap(il,null,["number","number","number","number","number"])}function 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y1e={kernelName:ol,backendName:"wasm",kernelFunc:g1e},_T;function A1e(e){_T=e.wasm.cwrap(wi,null,["number","array","number","number","array","array","number","number"])}function x1e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(w.sizeFromShape(t.shape)===0)return yT({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return _T(i,u,t.shape.length,Vt[t.dtype],p,c,a,l),o}var zT={kernelName:wi,backendName:"wasm",kernelFunc:x1e,setupFunc:A1e},b1e=!1,v1e=Hr(ki,b1e),OT;function w1e(e){OT=e.wasm.cwrap(Ii,null,["number","number","number"])}function k1e(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=jh({backend:r,inputs:{x:n},attrs:{dtype:"float32"}}),o=r.dataIdMap.get(u.dataId).id);let d=r.makeOutput(n.shape,"float32"),h=r.dataIdMap.get(d.dataId).id;return OT(o,i,h),l.dtype!=="float32"&&r.disposeData(u.dataId),d}var I1e={kernelName:Ii,backendName:"wasm",setupFunc:w1e,kernelFunc:k1e},DT;function S1e(e){DT=e.wasm.cwrap(ll,null,["number","number","number","number"])}function T1e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Gi(i,a,t),f=h;if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;DT(l,y,Vt[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var N1e={kernelName:ll,backendName:"wasm",setupFunc:S1e,kernelFunc:T1e},C1e=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Yx(n,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},E1e={kernelName:ed,backendName:"wasm",kernelFunc:C1e},R1e=!0,M1e=Hr(ai,R1e),F1e=Ir(Si),$1e=Ir(Ni),LT;function P1e(e){LT=e.wasm.cwrap(Ti,null,["number","number","number","number","number","number","number","number","number","number"])}function _1e(e){let{backend:t,inputs:r,attrs:n}=e,{images:a}=r,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[d,h,p,c]=a.shape,f=[d,l,u,c],m=t.dataIdMap.get(a.dataId),g;m.dtype!=="float32"&&(g=jh({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(w.sizeFromShape(a.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return LT(y,d,h,p,c,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var z1e={kernelName:Ti,backendName:"wasm",setupFunc:P1e,kernelFunc:_1e},BT;function O1e(e){BT=e.wasm.cwrap(dl,null,["number","array","number","array","number","number"])}function D1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=w.parseAxisParam(s,a.shape);if(a.shape.length===0)return I0({inputs:{x:a},backend:r});let o=r.makeOutput(a.shape,a.dtype),l=r.dataIdMap.get(a.dataId).id,u=r.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);BT(l,d,i.length,h,a.shape.length,u);let p=en({inputs:{x:o},attrs:{shape:a.shape},backend:r});return r.disposeData(o.dataId),p}var L1e={kernelName:dl,backendName:"wasm",kernelFunc:D1e,setupFunc:O1e},WT;function B1e(e){WT=e.wasm.cwrap(Il,null,["number","number","number","number","number","number","number","number","array","number","number"])}function W1e(e){let{inputs:t,backend:r,attrs:n}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=n,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(a.dataId).id,d=r.dataIdMap.get(l.dataId).id,[h,p,c,f]=a.shape,[m,g]=N.getImageCenter(o,p,c),y=i===0,A=255,x=typeof i=="number"?[i,i,i,y?0:A]:[...i,A],b=new Uint8Array(new Int32Array(x).buffer);return WT(u,h,p,c,f,s,m,g,b,x.length,d),l}var V1e={kernelName:Il,backendName:"wasm",kernelFunc:W1e,setupFunc:B1e},U1e=Ir(pl),G1e=Ir(Ci),VT;function j1e(e){VT=e.wasm.cwrap(hl,null,["number","number","number","number","number","number","array","number","number"])}function H1e(e){let{backend:t,inputs:r,attrs:n}=e,{indices:a,updates:s}=r,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=v2.calculateShapes(s,a,i),c=t.dataIdMap.get(a.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),g=t.dataIdMap.get(o.dataId).id;return VT(c,f,Vt[s.dtype],l,u,d,m,p,g),o}var q1e={kernelName:hl,backendName:"wasm",setupFunc:j1e,kernelFunc:H1e},UT;function K1e(e){UT=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function X1e(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=r.dataIdMap.get(n.dataId).id,o=r.dataIdMap.get(a.dataId).id,l=r.dataIdMap.get(s.dataId).id,u=r.makeOutput(a.shape,a.dtype),d=r.dataIdMap.get(u.dataId).id,h=n.shape.length,p=a.shape.length,c=h===0||h>1||p===1?1:w.sizeFromShape(a.shape.slice(1));return UT(i,o,l,c,d),u}var Z1e={kernelName:cl,backendName:"wasm",kernelFunc:X1e,setupFunc:K1e},GT;function Y1e(e){GT=e.wasm.cwrap(Ri,null,["number","number"])}function J1e(e){let{backend:t,inputs:{x:r}}=e,n=t.dataIdMap.get(r.dataId).id,a=t.makeOutput(r.shape,r.dtype),s=t.dataIdMap.get(a.dataId).id;return w.sizeFromShape(a.shape)===0||GT(n,s),a}var Q1e={kernelName:"Sigmoid",backendName:"wasm",setupFunc:Y1e,kernelFunc:J1e},eye=Ir(Ei),jT;function tye(e){jT=e.wasm.cwrap($i,null,["number","number","number","number"])}function rye(e){let{backend:t,inputs:{logits:r},attrs:{dim:n}}=e,a=t.dataIdMap.get(r.dataId).id,s=t.makeOutput(r.shape,r.dtype),i=t.dataIdMap.get(s.dataId).id,o=r.shape[n],l=w.sizeFromShape(r.shape)/o;return w.sizeFromShape(s.shape)===0||jT(a,i,o,l),s}var nye={kernelName:$i,backendName:"wasm",setupFunc:tye,kernelFunc:rye};function aye(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n,o=w.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<a.shape.length;++g)l.push([0,0]);let u=zT.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(d.length,s.length,!1),p=N.getReshapedPermuted(u.shape,s,o,!1),c=en({inputs:{x:u},backend:r,attrs:{shape:d}}),f=js({inputs:{x:c},backend:r,attrs:{perm:h}}),m=en({inputs:{x:f},backend:r,attrs:{shape:p}});return r.disposeData(u.dataId),r.disposeData(c.dataId),r.disposeData(f.dataId),m}var sye={kernelName:gl,backendName:"wasm",kernelFunc:aye},HT;function iye(e){HT=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function oye(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(d,n.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),v=t.dataIdMap.get(b.dataId).id,S=t.makeOutput([4],"int32"),T=t.dataIdMap.get(S.dataId).id,E=HT(h,p,Vt[a.dtype],o,u,l,c,m,y,x,v,T),R=t.readSync(S.dataId),_;switch(R[0]){case 1:{_=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{_=N.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:_=N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:_=""}if(t.disposeData(S.dataId),_)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(A.dataId),t.disposeData(b.dataId),new Error(_);let M=f,I=g;return E!==d[0]&&(M=zo({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),I=zo({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[M,I,A,b]}var lye={kernelName:sh,backendName:"wasm",setupFunc:iye,kernelFunc:oye},qT;function uye(e){qT=e.wasm.cwrap(id,null,["number","number","number","number","number","number","number"])}function dye(e){let{backend:t,inputs:r}=e,{inputIndices:n,inputShape:a,newShape:s}=r;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
+  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},Hme=class extends tT{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},qme=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),fw=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;w.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),w.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},rT=class extends $u{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!wb())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new jme(this.device),this.tensorMap=new qp(this,br()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return rT.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=w.sizeFromShape(t)*t2(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*t2(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new tT),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Hme),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=F8(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(a.shape)===0){let T=this.tensorMap.get(a.dataId);return T.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=fw(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(T=>T.shape),o="int32";i.map(T=>{s.push({type:o,data:T})});let l=w.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let T=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?T/4:T]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((T,E)=>{if(T.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. 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Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. 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supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=T0[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=Nl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),v=r.dataIdMap.get(b.dataId).id,S=new Uint8Array(new Int32Array(a.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return aT(p,S,a.shape.length,c,T,s.shape.length,l,u,g,f,m,h||0,v),b}var Zme={kernelName:Fs,backendName:"wasm",setupFunc:Kme,kernelFunc:Xme};function Ir(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||r(l,Vt[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var Yme=Ir(Vo);function Hr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Vt[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var Jme=!0,Qme=Hr(Ja,Jme),sT;function e0e(e){sT=e.wasm.cwrap(Ks,null,["array","number","number","number"])}function t0e(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return sT(s,a.length,Vt[n.dtype],i),n}var r0e={kernelName:Ks,backendName:"wasm",setupFunc:e0e,kernelFunc:t0e};function N0(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var n0e={kernelName:ci,backendName:"wasm",kernelFunc:N0},iT;function a0e(e){iT=e.wasm.cwrap(Bi,null,["number","array","number","number","number","array","number"])}function Hs(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=i0e(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=s0e(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=N0({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return iT(d,c,l.shape.length,Vt[l.dtype],h,p,s.length),u}function s0e(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function i0e(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var o0e={kernelName:Bi,backendName:"wasm",kernelFunc:Hs,setupFunc:a0e};function qi(e,t,r){let n=e.shape,a=e.shape.length,s=w.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=Hs({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var oT;function l0e(e){oT=e.wasm.cwrap(Ou,null,["number, number, number"])}function u0e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=qi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;oT(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var d0e={kernelName:Ou,backendName:"wasm",setupFunc:l0e,kernelFunc:u0e},lT;function p0e(e){lT=e.wasm.cwrap(Du,null,["number, number, number"])}function h0e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=qi(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;lT(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var c0e={kernelName:Du,backendName:"wasm",setupFunc:p0e,kernelFunc:h0e},uT;function f0e(e){uT=e.wasm.cwrap(Xs,null,["number","number","number","number","number"])}function m0e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=qi(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=w.sizeFromShape(c.shape),g=l.shape[d[0]];return uT(o,Vt[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var g0e={kernelName:Xs,backendName:"wasm",kernelFunc:m0e,setupFunc:f0e},dT;function y0e(e){dT=e.wasm.cwrap(Zs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function A0e(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),v=n.dataIdMap.get(b.dataId).id;return dT(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,v),b}var x0e={kernelName:Zs,backendName:"wasm",setupFunc:y0e,kernelFunc:A0e};function en(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var b0e={kernelName:pl,backendName:"wasm",kernelFunc:en},pT;function v0e(e){pT=e.wasm.cwrap(Ys,null,["number","array","number","number","array","number","number","number","number"])}function w0e(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=Nl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],v=en({inputs:{x:a},backend:r,attrs:{shape:x}}),S=en({inputs:{x:s},backend:r,attrs:{shape:b}}),T=r.dataIdMap.get(v.dataId).id,E=r.dataIdMap.get(S.dataId).id,R=i?v.shape[2]:v.shape[1],_=o?S.shape[1]:S.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],v.dtype),z=r.dataIdMap.get(I.dataId).id,O=new Uint8Array(new Int32Array(v.shape).buffer),j=new Uint8Array(new Int32Array(S.shape).buffer);return pT(T,O,v.shape.length,E,j,S.shape.length,i,o,z),r.disposeData(v.dataId),r.disposeData(S.dataId),I.shape=A,I}var k0e={kernelName:Ys,backendName:"wasm",setupFunc:v0e,kernelFunc:w0e};function Lo(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=_t.parseSliceParams(t,r,n),o=_t.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=w.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=_t.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+w.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(i))),u}if(t.dtype==="string"){let f=Of(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)I0e(l,d[0],p,s,i);else if(c===3)S0e(l,d[0],d[1],p,s,i);else if(c===4)T0e(l,d[0],d[1],d[2],p,s,i);else{let f=Of(l,s,i,t.shape,t.dtype);p.set(f)}return u}function I0e(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function S0e(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function T0e(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var N0e={kernelName:gl,backendName:"wasm",kernelFunc:Lo};function C0e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=en({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Hs({inputs:{x:c},backend:r,attrs:{perm:u}}),m=en({inputs:{x:f},backend:r,attrs:{shape:d}}),g=Lo({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var E0e={kernelName:Uo,backendName:"wasm",kernelFunc:C0e};function jh(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var R0e={kernelName:Js,backendName:"wasm",kernelFunc:jh},M0e=Ir(Qs),hT;function F0e(e){hT=e.wasm.cwrap(Qa,null,["number","number","number","number"])}function 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ege(e){let{backend:t,inputs:r,attrs:n}=e,{x:a}=r,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=t.makeOutput(f,"float32"),g=t.dataIdMap.get(a.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(a.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),b=t.dataIdMap.get(m.dataId).id;return bT(g,s,i==="NHWC"?1:0,y,a.shape.length-1,A,x,f.length,b),m}var tge={kernelName:qo,backendName:"wasm",setupFunc:Q0e,kernelFunc:ege},vT;function rge(e){vT=e.wasm.cwrap(si,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function nge(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,i=n.dataIdMap.get(a.dataId).id,o=n.dataIdMap.get(s.dataId).id,{strides:l,dilations:u,pad:d,dimRoundingMode:h}=r,p=u==null?[1,1]:u,c=N.computeConv2DInfo(a.shape,s.shape,l,p,d,h,!0),f=c.filterHeight,m=c.filterWidth,g=c.padInfo.top,y=c.padInfo.right,A=c.padInfo.bottom,x=c.padInfo.left,b=c.dilationHeight,v=c.dilationWidth,S=c.strideHeight,T=c.strideWidth,E=c.inChannels,R=c.outChannels,_=c.padInfo.type==="SAME"?1:0;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let M=n.makeOutput(c.outShape,"float32"),I=n.dataIdMap.get(M.dataId).id;return vT(i,a.shape[0],a.shape[1],a.shape[2],o,f,m,g,y,A,x,_,b,v,S,T,E,R,I),M}var age={kernelName:si,backendName:"wasm",setupFunc:rge,kernelFunc:nge},sge=Ir(oi),ige=!1,oge=Hr(Ko,ige,"bool"),lge=Ir(li,"float32");function i2(e){let{inputs:t,attrs:r,backend:n}=e,{input:a}=t,{dim:s}=r,i=a.shape.length,o=a.shape.slice(),l=s;return s<0&&(w.assert(-(i+1)<=s,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+s+1),o.splice(l,0,1),en({inputs:{x:a},backend:n,attrs:{shape:o}})}var uge={kernelName:Xo,backendName:"wasm",kernelFunc:i2};function wT(e){let{attrs:{shape:t,value:r,dtype:n},backend:a}=e,s=a.makeOutput(t,n);return a.typedArrayFromHeap(s).fill(r),s}var dge={kernelName:Hu,backendName:"wasm",kernelFunc:wT},kT;function pge(e){kT=e.wasm.cwrap(Yo,null,["number","number","number","number","number","number"])}function hge(e){let{inputs:t,backend:r}=e,{image:n}=t,a=r.makeOutput(n.shape,n.dtype),s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,[o,l,u,d]=n.shape;return kT(s,o,l,u,d,i),a}var cge={kernelName:Yo,backendName:"wasm",kernelFunc:hge,setupFunc:pge},fge=Ir(ui),mge=!1,gge=Hr(di,mge),IT;function yge(e){IT=e.wasm.cwrap(pi,null,["number","number","number","number","number","number","number"])}function Age(e){let{backend:t,inputs:r,attrs:n}=e,{varianceEpsilon:a}=n,{x:s,mean:i,variance:o,offset:l,scale:u}=r,d=t.dataIdMap.get(s.dataId).id,h=t.dataIdMap.get(i.dataId).id,p=t.dataIdMap.get(o.dataId).id,c=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(s.shape,s.dtype);if(w.sizeFromShape(s.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return IT(d,h,p,c,f,a,g),m}var xge={kernelName:pi,backendName:"wasm",setupFunc:yge,kernelFunc:Age},ST;function bge(e){ST=e.wasm.cwrap($s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vge(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p),g=T0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,S=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,z=m.strideHeight,O=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${h}'. Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),J=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return ST(y,D,Q,V,A,v,S,b,T,E,R,_,X,M,I,z,O,j,x,g,se,f||0,J),ee}var wge={kernelName:$s,backendName:"wasm",setupFunc:bge,kernelFunc:vge},TT;function kge(e){TT=e.wasm.cwrap(Ps,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ige(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=T0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,S=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,z=m.strideHeight,O=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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Got ${a.dtype}.`);let{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.dilationHeight,A=d.dilationWidth,x=d.strideHeight,b=d.strideWidth,v=d.inChannels,S=d.outChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. 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b=N.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var r1e={kernelName:xi,backendName:"wasm",setupFunc:e1e,kernelFunc:t1e},$T;function n1e(e){$T=e.wasm.cwrap(bi,null,["number","number","number","number"])}function a1e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=qi(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let f=u.shape.length;N.assertAxesAreInnerMostDims("min",h,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,h),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;$T(l,Vt[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var s1e={kernelName:bi,backendName:"wasm",setupFunc:n1e,kernelFunc:a1e},i1e=!1,o1e=Hr(vi,i1e),PT=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(PT||{}),_T;function l1e(e){_T=e.wasm.cwrap(wi,null,["number","array","number","number","array","array","number","number"])}function u1e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return _T(i,u,t.shape.length,Vt[t.dtype],p,c,PT[a],l),o}var d1e={kernelName:wi,backendName:"wasm",kernelFunc:u1e,setupFunc:l1e},p1e=!0,h1e=Hr(ki,p1e),c1e=Ir(al);function Nb(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var zT;function f1e(e){zT=e.wasm.cwrap(il,"number",["number","number","number","number","number"])}function m1e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=zT(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=Nb(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var g1e={kernelName:il,backendName:"wasm",setupFunc:f1e,kernelFunc:m1e},OT;function y1e(e){OT=e.wasm.cwrap(Qu,"number",["number","number","number","number","number","bool"])}function A1e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=OT(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Nb(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var x1e={kernelName:Qu,backendName:"wasm",setupFunc:y1e,kernelFunc:A1e},DT;function b1e(e){DT=e.wasm.cwrap(ol,"number",["number","number","number","number","number","number"])}function v1e(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=DT(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Nb(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([f],"float32",m);return[y,A]}var w1e={kernelName:ol,backendName:"wasm",setupFunc:b1e,kernelFunc:v1e},k1e=!1,I1e=Hr(sl,k1e,"bool"),LT;function S1e(e){LT=e.wasm.cwrap(ul,null,["number","number","number","number","number"])}function T1e(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return LT(d,s,i,o,u),l}var N1e={kernelName:ul,backendName:"wasm",setupFunc:S1e,kernelFunc:T1e};function C1e(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var E1e={kernelName:ll,backendName:"wasm",kernelFunc:C1e};function R1e(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return i2({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=i2({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=cT({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var M1e={kernelName:dl,backendName:"wasm",kernelFunc:R1e},BT;function F1e(e){BT=e.wasm.cwrap(Ii,null,["number","array","number","number","array","array","number","number"])}function $1e(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(w.sizeFromShape(t.shape)===0)return wT({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return BT(i,u,t.shape.length,Vt[t.dtype],p,c,a,l),o}var WT={kernelName:Ii,backendName:"wasm",kernelFunc:$1e,setupFunc:F1e},P1e=!1,_1e=Hr(Si,P1e),VT;function z1e(e){VT=e.wasm.cwrap(Ti,null,["number","number","number"])}function 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re(i),o},F0=(e,t)=>{let r=M0(e),n=$d(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},$0=e=>{let t=M0(e),r=$d(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},_N=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Hb=[[1,0,0],[0,1,0],[0,0,1]],A2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),x2e=(e,t)=>A2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var FN=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Dl=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},b2e=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},$N=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Dl(e[a],b2e(t,s)))}return r},zN=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=FN(t[0],t[1]),i=$N(s,a),o=FN(-t[0],-t[1]);return $N(i,o)},v2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Dl(t[0],r),-Dl(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},w2e=(e,t)=>[Dl(e,t[0]),Dl(e,t[1])];function ON(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function DN(e,t,r,n,a){let s=$d(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?zN(r,[0,0]):Hb,u=o?i.map(c=>[...w2e(c,l),c[2]]):i,d=o?v2e(n):Hb,h=M0(t),p=[Dl(h,d[0]),Dl(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function LN(e,t,r,n){let a=t.landmarks.length>=Vb.count?Vb.symmetryLine:Kh.symmetryLine,s=0,i=Hb,o;if(e&&he.kernels.includes("rotatewithoffset"))if(s=x2e(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=M0(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=zN(-s,u),o=jb(t,h,[n,n]),re(h)}else o=jb(t,r,[n,n]);else o=jb(t,r,[n,n]);return[s,i,o]}var k2e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},BN=(e,t)=>{let r=k2e(e),n=$d(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var WN=6,I2e=1.4,Oa,VN=null,ji=0,Zh=null,P0=()=>ji;async function UN(e){var t;return he.initial&&(Oa=null),Oa?e.debug&&ie("cached model:",Oa.modelUrl):Oa=await Ge((t=e.face.detector)==null?void 0:t.modelPath),ji=Oa.inputs[0].shape?Oa.inputs[0].shape[2]:0,Zh=Se(ji,"int32"),VN=pa(ON(ji)),Oa}function S2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,VN),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Zh),t.centersNormalized=pe(t.centers,Zh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=ce(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Zh),t.endNormalized=L(t.ends,Zh);let r=ud([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function GN(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[ji,ji]),r.div=pe(r.resized,Qe.tf127),r.normalized=ce(r.div,Qe.tf05);let n=Oa==null?void 0:Oa.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=kt([h[0],h[2]],2),r.concat512=kt([h[1],h[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else r.batch=et(n);re(n),r.boxes=S2e(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Nr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],WN-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=G(c.squeeze,[WN,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=PN(m,[(e.shape[2]||0)/ji,(e.shape[1]||0)/ji]),y=F0(g,t.face.scale||I2e),A=$0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var _0={};xs(_0,{connected:()=>Yb,kpt:()=>Zb});var Zb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Yb={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var HN=224,T2e,N2e=5,z0=[8,16,32,32,32];async function qN(){let e=[],t=0;for(;t<N2e;){let r=0,n=t;for(;n<z0.length&&z0[n]===z0[t];)r+=2,n++;let a=z0[t],s=Math.ceil(HN/a),i=Math.ceil(HN/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}T2e={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function ls(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function KN(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function O0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var YN={initial:!0},gn={detector:null,landmarks:null},Pd={detector:[224,224],landmarks:[256,256]},Jb=Number.MAX_SAFE_INTEGER,E2e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},L0=null,Yh,Hi=[[0,0],[0,0],[0,0],[0,0]],XN=0,ZN=e=>1-1/(1+Math.exp(e));async function JN(e){if(YN.initial&&(gn.detector=null),!gn.detector&&e.body.detector&&e.body.detector.modelPath){gn.detector=await Ge(e.body.detector.modelPath);let t=Object.values(gn.detector.modelSignature.inputs);Pd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Pd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&gn.detector&&ie("cached model:",gn.detector.modelUrl);return await qN(),gn.detector}async function QN(e){if(YN.initial&&(gn.landmarks=null),gn.landmarks)e.debug&&ie("cached model:",gn.landmarks.modelUrl);else{gn.landmarks=await Ge(e.body.modelPath);let t=Object.values(gn.landmarks.modelSignature.inputs);Pd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Pd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return gn.landmarks}async function R2e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Yh&&(r.cropped=Ie.cropAndResize(e,[Yh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Hi=[[0,0],a,s,[0,0]],r.pad=Hn(r.cropped||e,Hi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function M2e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Hi[2][0]+Hi[2][1])/t[0]-Hi[2][0]),Math.trunc(r.position[1]*(t[1]+Hi[1][0]+Hi[1][1])/t[1]-Hi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Yh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Yh[1],r.positionRaw[1]+Yh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function F2e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function $2e(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=gn.landmarks)==null?void 0:f.execute(e,E2e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=ZN(s[l*m+3]),y=ZN(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/Pd.landmarks[0],s[l*m+1]/Pd.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:Zb[m],positionRaw:x,position:b,distance:v,score:A})}if(a<(t.body.minConfidence||0))return null;F2e(o);let u=M2e(o,r),d=u.map(m=>m.position),h=ls(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(Yb)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(v=>v.part===g[A]),b=u.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function Qb(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-XN,a=Jb<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&L0!==null)Jb++;else{let s={};s.landmarks=await R2e(e,256),L0=await $2e(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),XN=oe(),Jb=0}return L0?[L0]:[]}var _d=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports 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phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var us,Ll=0,e5=[],tC=0,t5=Number.MAX_SAFE_INTEGER;async function rC(e){if(he.initial&&(us=null),us)e.debug&&ie("cached model:",us.modelUrl);else{us=await Ge(e.object.modelPath);let t=Object.values(us.modelSignature.inputs);Ll=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return us}async function P2e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Xt(n.squeeze,6,1);n.stack=or([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=_d[h].label,[c,f]=[s[0][u][0]/Ll,s[0][u][1]/Ll],m=[c,f,s[0][u][2]/Ll-c,s[0][u][3]/Ll-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function r5(e,t){let r=(t.object.skipTime||0)>oe()-tC,n=t5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&e5.length>0?(t5++,e5):(t5=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Ll,Ll]),o=t.object.enabled?us==null?void 0:us.execute(i,["tower_0/detections"]):null;tC=oe(),re(i);let l=await P2e(o,s,t);e5=l,a(l)}))}var B0={};xs(B0,{connected:()=>a5,kpt:()=>n5});var n5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],a5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mr,aC=0,Kr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},s5=Number.MAX_SAFE_INTEGER;async function sC(e){return he.initial&&(Mr=null),Mr?e.debug&&ie("cached model:",Mr.modelUrl):Mr=await Ge(e.body.modelPath),Mr}async function _2e(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=mr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Cn(a,0),l=hd(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function i5(e,t){let r=(t.body.skipTime||0)>oe()-aC,n=s5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(Kr.keypoints).length>0?(s5++,[Kr]):(s5=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Mr!=null&&Mr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Mr.inputs[0].shape[2],Mr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return ce(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Mr==null?void 0:Mr.execute(s)),aC=oe(),re(s),i){Kr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await _2e(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&Kr.keypoints.push({score:Math.round(100*y)/100,part:n5[f],positionRaw:[m/Mr.inputs[0].shape[2],g/Mr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Mr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}Kr.score=Kr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=Kr.keypoints.map(p=>p.position[0]),l=Kr.keypoints.map(p=>p.position[1]);Kr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Kr.keypoints.map(p=>p.positionRaw[0]),d=Kr.keypoints.map(p=>p.positionRaw[1]);Kr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(a5)){let f=[];for(let m=0;m<c.length-1;m++){let g=Kr.keypoints.find(A=>A.part===c[m]),y=Kr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}Kr.annotations[p]=f}a([Kr])}))}var z2e=["angry","disgust","fear","happy","sad","surprise","neutral"],zn,W0=[],oC=0,lC=0,o5=Number.MAX_SAFE_INTEGER;async function uC(e){var t;return he.initial&&(zn=null),zn?e.debug&&ie("cached model:",zn.modelUrl):zn=await Ge((t=e.face.emotion)==null?void 0:t.modelPath),zn}async function l5(e,t,r,n){var i,o;if(!zn)return[];let a=o5<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-lC;return t.skipAllowed&&s&&a&&oC===n&&W0[r]&&W0[r].length>0?(o5++,W0[r]):(o5=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=zn!=null&&zn.inputs[0].shape?zn.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=ce(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=zn==null?void 0:zn.execute(p.grayscaleMul),lC=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:z2e[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}W0[r]=u,oC=n,l(u)}))}var yn,u5=[],pC=0,hC=0,cC=Number.MAX_SAFE_INTEGER;async function fC(e){return he.initial&&(yn=null),yn?e.debug&&ie("cached model:",yn.modelUrl):yn=await Ge(e.face.mobilefacenet.modelPath),yn}async function d5(e,t,r,n){var i,o;if(!yn)return[];let a=cC<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-hC;return t.skipAllowed&&s&&a&&pC===n&&u5[r]?(cC++,u5[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(yn==null?void 0:yn.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[yn.inputs[0].shape[2],yn.inputs[0].shape[1]],!1),h.data=yn==null?void 0:yn.execute(h.crop);let p=await h.data.data();u=Array.from(p)}u5[r]=u,pC=n,hC=oe(),l(u)})}var ds,qi=0,O2e=2.3,p5=Jn.leftEyeLower0,h5=Jn.rightEyeLower0,zd={leftBounds:[p5[0],p5[p5.length-1]],rightBounds:[h5[0],h5[h5.length-1]]},Od={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function xC(e){var t;return he.initial&&(ds=null),ds?e.debug&&ie("cached model:",ds.modelUrl):ds=await Ge((t=e.face.iris)==null?void 0:t.modelPath),qi=ds.inputs[0].shape?ds.inputs[0].shape[2]:0,qi===-1&&(qi=64),ds}function V0(e,t,r,n){for(let a=0;a<Ub.length;a++){let{key:s,indices:i}=Ub[a],o=Jn[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var D2e=e=>{let t=e[zd.leftBounds[0]][2],r=e[zd.rightBounds[0]][2];return t-r},gC=(e,t,r,n,a,s=!1)=>{let i=$0(F0(_N([e[r],e[n]]),O2e)),o=$d(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[qi,qi]);if(s&&he.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);re(l),l=u}return{box:i,boxSize:o,crop:l}},yC=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Od.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/qi:i/qi)*r[0]+t.startPoint[0],o/qi*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Od.index)}},AC=(e,t,r)=>{let n=e[Jn[`${r}EyeUpper0`][Od.upperCenter]][2],a=e[Jn[`${r}EyeLower0`][Od.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function bC(e,t,r,n){if(!ds)return r.debug&&ie("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=gC(e,t,zd.leftBounds[0],zd.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=gC(e,t,zd.rightBounds[0],zd.rightBounds[1],n,!0),d=kt([i,u]);re(i),re(u);let h=ds.execute(d);re(d);let p=await h.data();re(h);let c=p.slice(0,Od.numCoordinates*3),{rawCoords:f,iris:m}=yC(c,a,s,!0),g=p.slice(Od.numCoordinates*3),{rawCoords:y,iris:A}=yC(g,o,l,!1),x=D2e(e);Math.abs(x)<30?(V0(e,f,"left",null),V0(e,y,"right",null)):x<1?V0(e,f,"left",["EyeUpper0","EyeLower0"]):V0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=AC(e,m,"left"),v=AC(e,A,"right");return e.concat(b).concat(v)}var Ki={eyeLLower:[33,7,163,144,145,153,154,155,133],eyeRLower:[263,249,390,373,374,380,381,382,362],lips:[61,76,91,181,84,17,314,405,321,291,291,185,40,39,37,0,267,269,270,291,62,183,88,178,87,14,268,303,304,408,291,184,42,178,87,14,268,303,304,408,61,62,90,180,85,16,315,404,307,308,291,185,40,73,72,0,302,269,270,409,61,184,95,179,86,15,316,403,324,408,291,184,74,41,38,11,268,303,304,408],eyeL:[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],eyeR:[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417]};async function wC(e,t){let r={irisL:t[3].dataSync(),irisR:t[1].dataSync(),eyeL:t[0].dataSync(),eyeR:t[6].dataSync(),lips:t[5].dataSync()},n=Ki.eyeRLower.reduce((s,i)=>s+=e[i][2],0)/Ki.eyeRLower.length;for(let s=0;s<r.irisR.length/2;s++)e.push([r.irisR[2*s+0],r.irisR[2*s+1],n]);let a=Ki.eyeLLower.reduce((s,i)=>s+=e[i][2],0)/Ki.eyeLLower.length;for(let s=0;s<r.irisL.length/2;s++)e.push([r.irisL[2*s+0],r.irisL[2*s+1],a]);for(let s=0;s<r.eyeL.length/2;s++)e[Ki.eyeL[s]]=[r.eyeL[2*s+0],r.eyeL[2*s+1],e[Ki.eyeL[s]][2]];for(let s=0;s<r.eyeR.length/2;s++)e[Ki.eyeR[s]]=[r.eyeR[2*s+0],r.eyeR[2*s+1],e[Ki.eyeR[s]][2]];return e}var Da={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},La=null,Dd=0;async function kC(e,t){var o,l,u,d,h,p,c,f,m,g;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-Da.timestamp,n=Da.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||Da.boxes.length===0?(Da.boxes=await GN(e,t),Da.timestamp=oe(),Da.skipped=0):Da.skipped++;let a=[],s=[],i=0;for(let y=0;y<Da.boxes.length;y++){let A=Da.boxes[y],x=0,b,v={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([x,b,v.tensor]=LN((u=t.face.detector)==null?void 0:u.rotation,A,e,(d=t.face.mesh)!=null&&d.enabled?Dd:P0()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let S=await S0(v.tensor);re(v.tensor),v.tensor=S}if(v.boxScore=Math.round(100*A.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!La)t.debug&&ie("face mesh detection requested, but model is not loaded");else{let S=La.execute(v.tensor),T=S.find(I=>I.shape[I.shape.length-1]===1),E=S.find(I=>I.shape[I.shape.length-1]===1404),R=await T.data();v.faceScore=Math.round(100*R[0])/100;let _=G(E,[-1,3]),M=await _.array();if(v.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))A.confidence=v.faceScore;else{(f=t.face.attention)!=null&&f.enabled?M=await wC(M,S):(m=t.face.iris)!=null&&m.enabled&&(M=await bC(M,v.tensor,t,Dd)),v.mesh=DN(M,A,x,b,Dd),v.meshRaw=v.mesh.map(z=>[z[0]/(e.shape[2]||0),z[1]/(e.shape[1]||0),(z[2]||0)/Dd]);for(let z of Object.keys(Jn))v.annotations[z]=Jn[z].map(O=>v.mesh[O]);v.score=v.faceScore;let I={...BN(v.mesh,A),confidence:A.confidence,landmarks:A.landmarks};v.box=qb(I,e),v.boxRaw=Kb(I,e),s.push(I)}re([...S,_])}else{v.box=qb(A,e),v.boxRaw=Kb(A,e),v.score=v.boxScore,v.mesh=A.landmarks.map(S=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*S[0]/P0(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*S[1]/P0()]),v.meshRaw=v.mesh.map(S=>[S[0]/(e.shape[2]||0),S[1]/(e.shape[1]||0),(S[2]||0)/Dd]);for(let S of Object.keys(Kh))v.annotations[S]=[v.mesh[Kh[S]]]}v.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?a.push(v):re(v.tensor)}return Da.boxes=s,a}async function IC(e){var t,r,n;return he.initial&&(La=null),La?e.debug&&ie("cached model:",La.modelUrl):(t=e.face.attention)!=null&&t.enabled?La=await 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h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)!=null&&p.enabled){let f=m5(e),m=An==null?void 0:An.execute(f);NC=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Cn(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let S=await m.find(R=>R.shape[1]===100).data();h.age=Math.round(S[b-1]>S[b+1]?10*b-100*S[b-1]:10*b+100*S[b+1])/10;let T=m.find(R=>R.shape[1]===1024),E=T?await T.data():[];h.descriptor=Array.from(E),m.forEach(R=>re(R))}U0[r]=h,CC=n,d(h)}))}function G0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Jh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function FC(e,t,r){let n=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a]];return Ie.cropAndResize(t,s,[0],r)}function $C(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:n,palmLandmarks:a,confidence:e.confidence}}function j0(e,t=1.5){let r=Jh(e),n=G0(e),a=[t*n[0]/2,t*n[1]/2],s=[r[0]-a[0],r[1]-a[1]],i=[r[0]+a[0],r[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function H0(e){let t=Jh(e),r=G0(e),a=Math.max(...r)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function B2e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function PC(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return B2e(r)}var RC=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Xi(e,t){let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}function W2e(e,t){let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r}function MC(e,t){let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Xi(e[a],W2e(t,s)))}return r}function A5(e,t){let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=RC(t[0],t[1]),i=MC(s,a),o=RC(-t[0],-t[1]);return MC(i,o)}function _C(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Xi(t[0],r),-Xi(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function x5(e,t){return[Xi(e,t[0]),Xi(e,t[1])]}var 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=ce(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Nr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=$C(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var G2e=5,DC=1.65,LC=[0,5,9,13,17,1,2],j2e=0,H2e=2,BC=0,K0=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>x5([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return j0(H0(a),G2e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=j0(H0(r),DC);n.palmLandmarks=[];for(let a=0;a<LC.length;a++)n.palmLandmarks.push(t[LC[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=G0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=A5(n,[0,0]),u=o.map(c=>[...x5(c,l),c[2]]),d=_C(a),h=[...Jh(r),1],p=[Xi(h,d[0]),Xi(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-BC,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?PC(u.palmLandmarks[j2e],u.palmLandmarks[H2e]):0,h=Jh(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&he.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=A5(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=FC(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);BC=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=G(x,[-1,3]),S=await v.array();re(x),re(v);let T=this.transformRawCoords(S,m,d,f),E=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=j0(H0(u),DC),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Xr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Xr.nameMapping[e],getPoints:e=>Xr.pointsMapping[e]},Yi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Yi.nameMapping[e]},Dt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Dt.nameMapping[e]},Zi=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:ba,index:ps,middle:hs,ring:Bl,pinky:Wl}=Xr,{none:va,half:K2e,full:wa}=Yi,{verticalUp:Ld,verticalDown:D7e,horizontalLeft:b5,horizontalRight:X2e,diagonalUpRight:Z2e,diagonalUpLeft:Bd,diagonalDownRight:L7e,diagonalDownLeft:B7e}=Dt,Ji=new Zi("thumbs up");Ji.curl(ba,va,1);Ji.direction(ba,Ld,1);Ji.direction(ba,Bd,.25);Ji.direction(ba,Z2e,.25);for(let e of[Xr.index,Xr.middle,Xr.ring,Xr.pinky])Ji.curl(e,wa,1),Ji.direction(e,b5,1),Ji.direction(e,X2e,1);var Qt=new Zi("victory");Qt.curl(ba,K2e,.5);Qt.curl(ba,va,.5);Qt.direction(ba,Ld,1);Qt.direction(ba,Bd,1);Qt.curl(ps,va,1);Qt.direction(ps,Ld,.75);Qt.direction(ps,Bd,1);Qt.curl(hs,va,1);Qt.direction(hs,Ld,1);Qt.direction(hs,Bd,.75);Qt.curl(Bl,wa,1);Qt.direction(Bl,Ld,.2);Qt.direction(Bl,Bd,1);Qt.direction(Bl,b5,.2);Qt.curl(Wl,wa,1);Qt.direction(Wl,Ld,.2);Qt.direction(Wl,Bd,1);Qt.direction(Wl,b5,.2);Qt.weight(ps,2);Qt.weight(hs,2);var Qi=new Zi("point");Qi.curl(ba,wa,1);Qi.curl(ps,va,.5);Qi.curl(hs,wa,.5);Qi.curl(Bl,wa,.5);Qi.curl(Wl,wa,.5);Qi.weight(ps,2);Qi.weight(hs,2);var eo=new Zi("middle finger");eo.curl(ba,va,1);eo.curl(ps,wa,.5);eo.curl(hs,wa,.5);eo.curl(Bl,wa,.5);eo.curl(Wl,wa,.5);eo.weight(ps,2);eo.weight(hs,2);var Wd=new Zi("open palm");Wd.curl(ba,va,.75);Wd.curl(ps,va,.75);Wd.curl(hs,va,.75);Wd.curl(Bl,va,.75);Wd.curl(Wl,va,.75);var WC=[Ji,Qt,Qi,eo,Wd];var Y2e=.7,Vl={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function VC(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function GC(e,t){if(!e||!t)return[0,0];let r=VC(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=VC(e[1],e[2],t[1],t[2]);return[r,n]}function UC(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function J2e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Vl.NO_CURL_START_LIMIT?y=Yi.none:g>Vl.HALF_CURL_START_LIMIT?y=Yi.half:y=Yi.full,y}function jC(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:n===Math.abs(t)?t>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:r>0?a=Dt.horizontalLeft:a=Dt.horizontalRight,a}function HC(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Dt.verticalDown:a=Dt.verticalUp:n===Math.abs(t)?t<0?a=Dt.verticalDown:a=Dt.verticalUp:r<0?a=Dt.verticalDown:a=Dt.verticalUp,a}function Q2e(e,t,r,n,a,s,i,o){let l,u=HC(e,t,r,n),d=jC(a,s,i,o);return u===Dt.verticalUp?d===Dt.horizontalLeft?l=Dt.diagonalUpLeft:l=Dt.diagonalUpRight:d===Dt.horizontalLeft?l=Dt.diagonalDownLeft:l=Dt.diagonalDownRight,l}function eAe(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=Vl.DISTANCE_VOTE_POWER:m>.66?c+=Vl.DISTANCE_VOTE_POWER:f+=Vl.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],v=e[1],S=r[0],T=r[1];x===g?(S=r[0],T=r[1]):x===A&&(b=t[0],v=t[1]);let _=GC([b,v],[S,T]),M=UC(_,Vl.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let z of n){let O=UC(z,Vl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],c+=O[1],f+=O[2]}let I;return p===Math.max(p,c,f)?I=HC(l,o,u,h):f===Math.max(c,f)?I=jC(s,a,i,d):I=Q2e(l,o,u,h,s,a,i,d),I}function qC(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Xr.all){let i=Xr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=GC(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Xr.all){let i=s===Xr.thumb?1:0,o=Xr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=J2e(l,u,d),p=eAe(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function X0(e){if(!e||e.length===0)return null;let t=qC(e),r={};for(let n of Xr.all)r[Xr.getName(n)]={curl:Yi.getName(t.curls[n]),direction:Dt.getName(t.directions[n])};return r}function KC(e){let t=[];if(!e||e.length===0)return t;let r=qC(e);for(let n of WC){let a=n.matchAgainst(r.curls,r.directions);a>=Y2e&&t.push({name:n.name,confidence:a})}return t}var XC={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Vd,Ud,ZC;async function w5(e,t){let r=await ZC.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(XC))s[d]=XC[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=X0(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function k5(e){var r,n;he.initial&&(Vd=null,Ud=null),!Vd||!Ud?[Vd,Ud]=await Promise.all([e.hand.enabled?Ge((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?Ge((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&ie("cached model:",Vd.modelUrl),e.debug&&ie("cached model:",Ud.modelUrl));let t=new q0(Vd);return ZC=new K0(t,Ud),[Vd,Ud]}var ur=[null,null],tAe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],to=[[0,0],[0,0]],rAe=["hand","fist","pinch","point","face","tip","pinchtip"],JC=4,QC=1.6,nAe=512,aAe=1.4,Z0=Number.MAX_SAFE_INTEGER,I5=0,cs=[0,0],jt={boxes:[],hands:[]},e9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function t9(e){var t;if(he.initial&&(ur[0]=null),ur[0])e.debug&&ie("cached model:",ur[0].modelUrl);else{Y0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),ur[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(ur[0].modelSignature.inputs);to[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,to[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return ur[0]}async function r9(e){var t;if(he.initial&&(ur[1]=null),ur[1])e.debug&&ie("cached model:",ur[1].modelUrl);else{ur[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(ur[1].modelSignature.inputs);to[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,to[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return ur[1]}async function sAe(e,t){let r=[];if(!e||!ur[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,nAe),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await ur[0].executeAsync(n.cast,tAe),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=tn(n.scores,1);re(o[JC]),o.splice(JC,1),n.filtered=or(o,1),re(o),n.max=mr(n.filtered,1),n.argmax=Cn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=O0(m,aAe),y=[Math.trunc(m[0]*cs[0]),Math.trunc(m[1]*cs[1]),Math.trunc(m[2]*cs[0]),Math.trunc(m[3]*cs[1])],A=d[p],x=rAe[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function S5(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&ur[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[to[1][0],to[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=ur[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/to[1][1],h[1]/to[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[cs[0]*(h[0]+t.boxRaw[0]),cs[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=X0(n.keypoints);for(let h of Object.keys(e9))n.annotations[h]=e9[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function T5(e,t){var a,s;if(!ur[0]||!ur[1]||!((a=ur[0])!=null&&a.inputs[0].shape)||!((s=ur[1])!=null&&s.inputs[0].shape))return[];cs=[e.shape[2]||0,e.shape[1]||0],Z0++;let r=(t.hand.skipTime||0)>oe()-I5,n=Z0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?jt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-I5,l=Z0<3*(t.hand.skipFrames||0);t.skipAllowed&&jt.hands.length===t.hand.maxDetected?jt.hands=await Promise.all(jt.boxes.map(d=>S5(e,d,t))):t.skipAllowed&&o&&l&&jt.hands.length>0?jt.hands=await Promise.all(jt.boxes.map(d=>S5(e,d,t))):(jt.boxes=await sAe(e,t),I5=oe(),jt.hands=await Promise.all(jt.boxes.map(d=>S5(e,d,t))),Z0=0);let u=[...jt.boxes];if(jt.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<jt.hands.length;d++){let h=KN(jt.hands[d].keypoints,cs);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&jt.hands[d].fingerScore&&jt.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=O0(h.box,QC),c=O0(h.boxRaw,QC);jt.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<jt.hands.length;d++){let h=ls(jt.hands[d].keypoints,cs);jt.hands[d].box=h.box,jt.hands[d].boxRaw=h.boxRaw}i(jt.hands)})}var Fr,J0=[],N5=Number.MAX_SAFE_INTEGER,a9=0,s9=0;async function i9(e){var t;return he.initial&&(Fr=null),Fr?e.debug&&ie("cached model:",Fr.modelUrl):Fr=await Ge((t=e.face.liveness)==null?void 0:t.modelPath),Fr}async function C5(e,t,r,n){var i,o;if(!Fr)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-s9,s=N5<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&a9===n&&J0[r]?(N5++,J0[r]):(N5=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Fr!=null&&Fr.inputs[0].shape?Fr.inputs[0].shape[2]:0,Fr!=null&&Fr.inputs[0].shape?Fr.inputs[0].shape[1]:0],!1),d=Fr==null?void 0:Fr.execute(u),h=(await d.data())[0];J0[r]=Math.round(100*h)/100,a9=n,s9=oe(),re([u,d]),l(J0[r])}))}var Qh={};xs(Qh,{connected:()=>eg,horizontal:()=>E5,kpt:()=>Q0,relative:()=>M5,vertical:()=>R5});var Q0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],E5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],R5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],M5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],eg={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var 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Ge(e.body.modelPath)),tg=bn.inputs[0].shape?bn.inputs[0].shape[2]:0,tg<64&&(tg=256),bn}async function oAe(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:Q0[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=ls(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(eg)){let p=[];for(let c=0;c<h.length-1;c++){let f=a.find(g=>g.part===h[c]),m=a.find(g=>g.part===h[c+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return F5(u),i.push(u),i}async function lAe(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:Q0[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ls(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(eg)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};F5(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function P5(e,t){if(!bn||!(bn!=null&&bn.inputs[0].shape))return[];t.skipAllowed||(Ul.boxes.length=0),$5++;let r=(t.body.skipTime||0)>oe()-Ul.last,n=$5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?Ul.bodies:new Promise(async a=>{let s={};$5=0,s.input=d9(e,tg),s.res=bn==null?void 0:bn.execute(s.input),Ul.last=oe();let i=await s.res.array();Ul.bodies=s.res.shape[2]===17?await oAe(i,t,e):await lAe(i,t,e);for(let o of Ul.bodies)p9(o,[e.shape[2]||1,e.shape[1]||1]),u9(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(Ul.bodies)})}var Gd,rg=[],f9=0,_5=Number.MAX_SAFE_INTEGER,ag=0,ng=2.5;async function m9(e){if(!Gd||he.initial){Gd=await Ge(e.object.modelPath);let t=Object.values(Gd.modelSignature.inputs);ag=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&ie("cached model:",Gd.modelUrl);return Gd}async function uAe(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===_d.length)),h=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<_d.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),f=await d.array();for(let m=0;m<d.shape[0];m++)for(let g=0;g<d.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/ag)),[v,S]=[A-ng/l*b[0],x-ng/l*b[1]],[T,E]=[A+ng/l*b[2]-v,x+ng/l*b[3]-S],R=[v,S,T,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:_d[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function z5(e,t){let r=(t.object.skipTime||0)>oe()-f9,n=_5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&rg.length>0?(_5++,rg):(_5=0,!he.kernels.includes("mod")||!he.kernels.includes("sparsetodense")?rg:new Promise(async a=>{let 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tc=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],dAe=tc.length,ec=tc.reduce((e,t,r)=>(e[t]=r,e),{}),pAe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],d4e=pAe.map(([e,t])=>[ec[e],ec[t]]),y9=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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re(i),o},_0=(e,t)=>{let r=P0(e),n=$d(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},z0=e=>{let t=P0(e),r=$d(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},BN=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Kb=[[1,0,0],[0,1,0],[0,0,1]],F2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),$2e=(e,t)=>F2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var ON=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ll=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},P2e=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},DN=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Ll(e[a],P2e(t,s)))}return r},WN=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=ON(t[0],t[1]),i=DN(s,a),o=ON(-t[0],-t[1]);return DN(i,o)},_2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Ll(t[0],r),-Ll(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},z2e=(e,t)=>[Ll(e,t[0]),Ll(e,t[1])];function VN(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function UN(e,t,r,n,a){let s=$d(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?WN(r,[0,0]):Kb,u=o?i.map(c=>[...z2e(c,l),c[2]]):i,d=o?_2e(n):Kb,h=P0(t),p=[Ll(h,d[0]),Ll(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function GN(e,t,r,n){let a=t.landmarks.length>=Gb.count?Gb.symmetryLine:Kh.symmetryLine,s=0,i=Kb,o;if(e&&he.kernels.includes("rotatewithoffset"))if(s=$2e(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=P0(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=WN(-s,u),o=qb(t,h,[n,n]),re(h)}else o=qb(t,r,[n,n]);else o=qb(t,r,[n,n]);return[s,i,o]}var O2e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},jN=(e,t)=>{let r=O2e(e),n=$d(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var HN=6,D2e=1.4,Oa,qN=null,Ki=0,Zh=null,O0=()=>Ki;async function KN(e){var t;return he.initial&&(Oa=null),Oa?e.debug&&ie("cached model:",Oa.modelUrl):Oa=await Ge((t=e.face.detector)==null?void 0:t.modelPath),Ki=Oa.inputs[0].shape?Oa.inputs[0].shape[2]:0,Zh=Se(Ki,"int32"),qN=pa(VN(Ki)),Oa}function L2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,qN),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Zh),t.centersNormalized=pe(t.centers,Zh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=ce(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Zh),t.endNormalized=L(t.ends,Zh);let r=ud([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function XN(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Ki,Ki]),r.div=pe(r.resized,Qe.tf127),r.normalized=ce(r.div,Qe.tf05);let n=Oa==null?void 0:Oa.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=kt([h[0],h[2]],2),r.concat512=kt([h[1],h[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=rt(r.concat,0)}else r.batch=rt(n);re(n),r.boxes=L2e(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Nr(r.logits),r.scores=rt(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],HN-1],[1,-1]),c.squeeze=rt(c.slice),c.landmarks=G(c.squeeze,[HN,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=LN(m,[(e.shape[2]||0)/Ki,(e.shape[1]||0)/Ki]),y=_0(g,t.face.scale||D2e),A=z0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var D0={};bs(D0,{connected:()=>Qb,kpt:()=>Jb});var Jb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Qb={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var YN=224,B2e,W2e=5,L0=[8,16,32,32,32];async function JN(){let e=[],t=0;for(;t<W2e;){let r=0,n=t;for(;n<L0.length&&L0[n]===L0[t];)r+=2,n++;let a=L0[t],s=Math.ceil(YN/a),i=Math.ceil(YN/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}B2e={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function us(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function QN(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function B0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var rC={initial:!0},gn={detector:null,landmarks:null},Pd={detector:[224,224],landmarks:[256,256]},e5=Number.MAX_SAFE_INTEGER,U2e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},V0=null,Yh,Xi=[[0,0],[0,0],[0,0],[0,0]],eC=0,tC=e=>1-1/(1+Math.exp(e));async function nC(e){if(rC.initial&&(gn.detector=null),!gn.detector&&e.body.detector&&e.body.detector.modelPath){gn.detector=await Ge(e.body.detector.modelPath);let t=Object.values(gn.detector.modelSignature.inputs);Pd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Pd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&gn.detector&&ie("cached model:",gn.detector.modelUrl);return await JN(),gn.detector}async function aC(e){if(rC.initial&&(gn.landmarks=null),gn.landmarks)e.debug&&ie("cached model:",gn.landmarks.modelUrl);else{gn.landmarks=await Ge(e.body.modelPath);let t=Object.values(gn.landmarks.modelSignature.inputs);Pd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Pd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return gn.landmarks}async function G2e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Yh&&(r.cropped=Ie.cropAndResize(e,[Yh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Xi=[[0,0],a,s,[0,0]],r.pad=Hn(r.cropped||e,Xi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function j2e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Xi[2][0]+Xi[2][1])/t[0]-Xi[2][0]),Math.trunc(r.position[1]*(t[1]+Xi[1][0]+Xi[1][1])/t[1]-Xi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Yh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Yh[1],r.positionRaw[1]+Yh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function H2e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function q2e(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=gn.landmarks)==null?void 0:f.execute(e,U2e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=tC(s[l*m+3]),y=tC(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/Pd.landmarks[0],s[l*m+1]/Pd.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:Jb[m],positionRaw:x,position:b,distance:v,score:A})}if(a<(t.body.minConfidence||0))return null;H2e(o);let u=j2e(o,r),d=u.map(m=>m.position),h=us(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(Qb)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(v=>v.part===g[A]),b=u.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function t5(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-eC,a=e5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&V0!==null)e5++;else{let s={};s.landmarks=await G2e(e,256),V0=await q2e(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),eC=oe(),e5=0}return V0?[V0]:[]}var _d=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports 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phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var ds,Bl=0,r5=[],iC=0,n5=Number.MAX_SAFE_INTEGER;async function oC(e){if(he.initial&&(ds=null),ds)e.debug&&ie("cached model:",ds.modelUrl);else{ds=await Ge(e.object.modelPath);let t=Object.values(ds.modelSignature.inputs);Bl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return ds}async function K2e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=rt(e);let i=Xt(n.squeeze,6,1);n.stack=or([i[1],i[0],i[3],i[2]],1),n.boxes=rt(n.stack),n.scores=rt(i[4]),n.classes=rt(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=_d[h].label,[c,f]=[s[0][u][0]/Bl,s[0][u][1]/Bl],m=[c,f,s[0][u][2]/Bl-c,s[0][u][3]/Bl-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function a5(e,t){let r=(t.object.skipTime||0)>oe()-iC,n=n5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&r5.length>0?(n5++,r5):(n5=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Bl,Bl]),o=t.object.enabled?ds==null?void 0:ds.execute(i,["tower_0/detections"]):null;iC=oe(),re(i);let l=await K2e(o,s,t);r5=l,a(l)}))}var U0={};bs(U0,{connected:()=>i5,kpt:()=>s5});var s5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],i5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mr,uC=0,Kr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},o5=Number.MAX_SAFE_INTEGER;async function dC(e){return he.initial&&(Mr=null),Mr?e.debug&&ie("cached model:",Mr.modelUrl):Mr=await Ge(e.body.modelPath),Mr}async function X2e(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=mr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Cn(a,0),l=hd(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function l5(e,t){let r=(t.body.skipTime||0)>oe()-uC,n=o5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(Kr.keypoints).length>0?(o5++,[Kr]):(o5=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Mr!=null&&Mr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Mr.inputs[0].shape[2],Mr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return ce(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Mr==null?void 0:Mr.execute(s)),uC=oe(),re(s),i){Kr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await X2e(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&Kr.keypoints.push({score:Math.round(100*y)/100,part:s5[f],positionRaw:[m/Mr.inputs[0].shape[2],g/Mr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Mr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}Kr.score=Kr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=Kr.keypoints.map(p=>p.position[0]),l=Kr.keypoints.map(p=>p.position[1]);Kr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=Kr.keypoints.map(p=>p.positionRaw[0]),d=Kr.keypoints.map(p=>p.positionRaw[1]);Kr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(i5)){let f=[];for(let m=0;m<c.length-1;m++){let g=Kr.keypoints.find(A=>A.part===c[m]),y=Kr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}Kr.annotations[p]=f}a([Kr])}))}var Z2e=["angry","disgust","fear","happy","sad","surprise","neutral"],On,G0=[],hC=0,cC=0,u5=Number.MAX_SAFE_INTEGER;async function fC(e){var t;return he.initial&&(On=null),On?e.debug&&ie("cached model:",On.modelUrl):On=await Ge((t=e.face.emotion)==null?void 0:t.modelPath),On}async function d5(e,t,r,n){var i,o;if(!On)return[];let a=u5<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-cC;return t.skipAllowed&&s&&a&&hC===n&&G0[r]&&G0[r].length>0?(u5++,G0[r]):(u5=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=On!=null&&On.inputs[0].shape?On.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=ce(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=On==null?void 0:On.execute(p.grayscaleMul),cC=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:Z2e[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}G0[r]=u,hC=n,l(u)}))}var yn,p5=[],gC=0,yC=0,AC=Number.MAX_SAFE_INTEGER;async function xC(e){return he.initial&&(yn=null),yn?e.debug&&ie("cached 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r={irisL:t[3].dataSync(),irisR:t[1].dataSync(),eyeL:t[0].dataSync(),eyeR:t[6].dataSync(),lips:t[5].dataSync()},n=Yi.eyeRLower.reduce((s,i)=>s+=e[i][2],0)/Yi.eyeRLower.length;for(let s=0;s<r.irisR.length/2;s++)e.push([r.irisR[2*s+0],r.irisR[2*s+1],n]);let a=Yi.eyeLLower.reduce((s,i)=>s+=e[i][2],0)/Yi.eyeLLower.length;for(let s=0;s<r.irisL.length/2;s++)e.push([r.irisL[2*s+0],r.irisL[2*s+1],a]);for(let s=0;s<r.eyeL.length/2;s++)e[Yi.eyeL[s]]=[r.eyeL[2*s+0],r.eyeL[2*s+1],e[Yi.eyeL[s]][2]];for(let s=0;s<r.eyeR.length/2;s++)e[Yi.eyeR[s]]=[r.eyeR[2*s+0],r.eyeR[2*s+1],e[Yi.eyeR[s]][2]];return e}var Da={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},La=null,Dd=0;async function CC(e,t){var o,l,u,d,h,p,c,f,m,g;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-Da.timestamp,n=Da.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||Da.boxes.length===0?(Da.boxes=await XN(e,t),Da.timestamp=oe(),Da.skipped=0):Da.skipped++;let a=[],s=[],i=0;for(let y=0;y<Da.boxes.length;y++){let A=Da.boxes[y],x=0,b,v={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([x,b,v.tensor]=GN((u=t.face.detector)==null?void 0:u.rotation,A,e,(d=t.face.mesh)!=null&&d.enabled?Dd:O0()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let S=await C0(v.tensor);re(v.tensor),v.tensor=S}if(v.boxScore=Math.round(100*A.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!La)t.debug&&ie("face mesh detection requested, but model is not loaded");else{let S=La.execute(v.tensor),T=S.find(I=>I.shape[I.shape.length-1]===1),E=S.find(I=>I.shape[I.shape.length-1]===1404),R=await T.data();v.faceScore=Math.round(100*R[0])/100;let _=G(E,[-1,3]),M=await _.array();if(v.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))A.confidence=v.faceScore;else{(f=t.face.attention)!=null&&f.enabled?M=await NC(M,S):(m=t.face.iris)!=null&&m.enabled&&(M=await 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n=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a]];return Ie.cropAndResize(t,s,[0],r)}function DC(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:n,palmLandmarks:a,confidence:e.confidence}}function K0(e,t=1.5){let r=Jh(e),n=q0(e),a=[t*n[0]/2,t*n[1]/2],s=[r[0]-a[0],r[1]-a[1]],i=[r[0]+a[0],r[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function X0(e){let t=Jh(e),r=q0(e),a=Math.max(...r)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function eAe(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function LC(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return eAe(r)}var _C=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ji(e,t){let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}function tAe(e,t){let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r}function zC(e,t){let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Ji(e[a],tAe(t,s)))}return r}function b5(e,t){let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=_C(t[0],t[1]),i=zC(s,a),o=_C(-t[0],-t[1]);return zC(i,o)}function BC(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Ji(t[0],r),-Ji(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function v5(e,t){return[Ji(e,t[0]),Ji(e,t[1])]}var 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=ce(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=rt(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Nr(n.slice),n.scores=rt(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=DC(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var aAe=5,UC=1.65,GC=[0,5,9,13,17,1,2],sAe=0,iAe=2,jC=0,Y0=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>v5([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return K0(X0(a),aAe)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=K0(X0(r),UC);n.palmLandmarks=[];for(let a=0;a<GC.length;a++)n.palmLandmarks.push(t[GC[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=q0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=b5(n,[0,0]),u=o.map(c=>[...v5(c,l),c[2]]),d=BC(a),h=[...Jh(r),1],p=[Ji(h,d[0]),Ji(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-jC,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?LC(u.palmLandmarks[sAe],u.palmLandmarks[iAe]):0,h=Jh(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&he.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=b5(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=OC(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);jC=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=G(x,[-1,3]),S=await v.array();re(x),re(v);let T=this.transformRawCoords(S,m,d,f),E=this.getBoxForHandLandmarks(T);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:T,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=K0(X0(u),UC),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Xr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Xr.nameMapping[e],getPoints:e=>Xr.pointsMapping[e]},eo={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>eo.nameMapping[e]},Dt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Dt.nameMapping[e]},Qi=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:ba,index:hs,middle:cs,ring:Wl,pinky:Vl}=Xr,{none:va,half:lAe,full:wa}=eo,{verticalUp:Ld,verticalDown:e4e,horizontalLeft:w5,horizontalRight:uAe,diagonalUpRight:dAe,diagonalUpLeft:Bd,diagonalDownRight:t4e,diagonalDownLeft:r4e}=Dt,to=new Qi("thumbs up");to.curl(ba,va,1);to.direction(ba,Ld,1);to.direction(ba,Bd,.25);to.direction(ba,dAe,.25);for(let e of[Xr.index,Xr.middle,Xr.ring,Xr.pinky])to.curl(e,wa,1),to.direction(e,w5,1),to.direction(e,uAe,1);var Qt=new Qi("victory");Qt.curl(ba,lAe,.5);Qt.curl(ba,va,.5);Qt.direction(ba,Ld,1);Qt.direction(ba,Bd,1);Qt.curl(hs,va,1);Qt.direction(hs,Ld,.75);Qt.direction(hs,Bd,1);Qt.curl(cs,va,1);Qt.direction(cs,Ld,1);Qt.direction(cs,Bd,.75);Qt.curl(Wl,wa,1);Qt.direction(Wl,Ld,.2);Qt.direction(Wl,Bd,1);Qt.direction(Wl,w5,.2);Qt.curl(Vl,wa,1);Qt.direction(Vl,Ld,.2);Qt.direction(Vl,Bd,1);Qt.direction(Vl,w5,.2);Qt.weight(hs,2);Qt.weight(cs,2);var ro=new Qi("point");ro.curl(ba,wa,1);ro.curl(hs,va,.5);ro.curl(cs,wa,.5);ro.curl(Wl,wa,.5);ro.curl(Vl,wa,.5);ro.weight(hs,2);ro.weight(cs,2);var no=new Qi("middle finger");no.curl(ba,va,1);no.curl(hs,wa,.5);no.curl(cs,wa,.5);no.curl(Wl,wa,.5);no.curl(Vl,wa,.5);no.weight(hs,2);no.weight(cs,2);var Wd=new Qi("open palm");Wd.curl(ba,va,.75);Wd.curl(hs,va,.75);Wd.curl(cs,va,.75);Wd.curl(Wl,va,.75);Wd.curl(Vl,va,.75);var HC=[to,Qt,ro,no,Wd];var pAe=.7,Ul={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function qC(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function XC(e,t){if(!e||!t)return[0,0];let r=qC(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=qC(e[1],e[2],t[1],t[2]);return[r,n]}function KC(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function hAe(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Ul.NO_CURL_START_LIMIT?y=eo.none:g>Ul.HALF_CURL_START_LIMIT?y=eo.half:y=eo.full,y}function ZC(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:n===Math.abs(t)?t>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:r>0?a=Dt.horizontalLeft:a=Dt.horizontalRight,a}function YC(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Dt.verticalDown:a=Dt.verticalUp:n===Math.abs(t)?t<0?a=Dt.verticalDown:a=Dt.verticalUp:r<0?a=Dt.verticalDown:a=Dt.verticalUp,a}function cAe(e,t,r,n,a,s,i,o){let l,u=YC(e,t,r,n),d=ZC(a,s,i,o);return u===Dt.verticalUp?d===Dt.horizontalLeft?l=Dt.diagonalUpLeft:l=Dt.diagonalUpRight:d===Dt.horizontalLeft?l=Dt.diagonalDownLeft:l=Dt.diagonalDownRight,l}function fAe(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=Ul.DISTANCE_VOTE_POWER:m>.66?c+=Ul.DISTANCE_VOTE_POWER:f+=Ul.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],v=e[1],S=r[0],T=r[1];x===g?(S=r[0],T=r[1]):x===A&&(b=t[0],v=t[1]);let _=XC([b,v],[S,T]),M=KC(_,Ul.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let z of n){let O=KC(z,Ul.SINGLE_ANGLE_VOTE_POWER);p+=O[0],c+=O[1],f+=O[2]}let I;return p===Math.max(p,c,f)?I=YC(l,o,u,h):f===Math.max(c,f)?I=ZC(s,a,i,d):I=cAe(l,o,u,h,s,a,i,d),I}function JC(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Xr.all){let i=Xr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=XC(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Xr.all){let i=s===Xr.thumb?1:0,o=Xr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=hAe(l,u,d),p=fAe(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function J0(e){if(!e||e.length===0)return null;let t=JC(e),r={};for(let n of Xr.all)r[Xr.getName(n)]={curl:eo.getName(t.curls[n]),direction:Dt.getName(t.directions[n])};return r}function QC(e){let t=[];if(!e||e.length===0)return t;let r=JC(e);for(let n of HC){let a=n.matchAgainst(r.curls,r.directions);a>=pAe&&t.push({name:n.name,confidence:a})}return t}var e9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Vd,Ud,t9;async function I5(e,t){let r=await t9.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(e9))s[d]=e9[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=J0(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function S5(e){var r,n;he.initial&&(Vd=null,Ud=null),!Vd||!Ud?[Vd,Ud]=await Promise.all([e.hand.enabled?Ge((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?Ge((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&ie("cached model:",Vd.modelUrl),e.debug&&ie("cached model:",Ud.modelUrl));let t=new Z0(Vd);return t9=new Y0(t,Ud),[Vd,Ud]}var ur=[null,null],mAe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ao=[[0,0],[0,0]],gAe=["hand","fist","pinch","point","face","tip","pinchtip"],n9=4,a9=1.6,yAe=512,AAe=1.4,Q0=Number.MAX_SAFE_INTEGER,T5=0,fs=[0,0],jt={boxes:[],hands:[]},s9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function i9(e){var t;if(he.initial&&(ur[0]=null),ur[0])e.debug&&ie("cached model:",ur[0].modelUrl);else{eg(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),ur[0]=await Ge((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(ur[0].modelSignature.inputs);ao[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,ao[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return ur[0]}async function o9(e){var t;if(he.initial&&(ur[1]=null),ur[1])e.debug&&ie("cached model:",ur[1].modelUrl);else{ur[1]=await Ge((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(ur[1].modelSignature.inputs);ao[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,ao[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return ur[1]}async function xAe(e,t){let r=[];if(!e||!ur[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,yAe),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await ur[0].executeAsync(n.cast,mAe),n.boxes=rt(n.rawBoxes,[0,2]),n.scores=rt(n.rawScores,[0]);let o=tn(n.scores,1);re(o[n9]),o.splice(n9,1),n.filtered=or(o,1),re(o),n.max=mr(n.filtered,1),n.argmax=Cn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=B0(m,AAe),y=[Math.trunc(m[0]*fs[0]),Math.trunc(m[1]*fs[1]),Math.trunc(m[2]*fs[0]),Math.trunc(m[3]*fs[1])],A=d[p],x=gAe[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function N5(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&ur[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[ao[1][0],ao[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=ur[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/ao[1][1],h[1]/ao[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[fs[0]*(h[0]+t.boxRaw[0]),fs[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=J0(n.keypoints);for(let h of Object.keys(s9))n.annotations[h]=s9[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function C5(e,t){var a,s;if(!ur[0]||!ur[1]||!((a=ur[0])!=null&&a.inputs[0].shape)||!((s=ur[1])!=null&&s.inputs[0].shape))return[];fs=[e.shape[2]||0,e.shape[1]||0],Q0++;let r=(t.hand.skipTime||0)>oe()-T5,n=Q0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?jt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-T5,l=Q0<3*(t.hand.skipFrames||0);t.skipAllowed&&jt.hands.length===t.hand.maxDetected?jt.hands=await Promise.all(jt.boxes.map(d=>N5(e,d,t))):t.skipAllowed&&o&&l&&jt.hands.length>0?jt.hands=await Promise.all(jt.boxes.map(d=>N5(e,d,t))):(jt.boxes=await xAe(e,t),T5=oe(),jt.hands=await Promise.all(jt.boxes.map(d=>N5(e,d,t))),Q0=0);let u=[...jt.boxes];if(jt.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<jt.hands.length;d++){let 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rg=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],M5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],F5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],$5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],ng={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var 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p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:rg[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=us(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(ng)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};P5(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function z5(e,t){if(!bn||!(bn!=null&&bn.inputs[0].shape))return[];t.skipAllowed||(Gl.boxes.length=0),_5++;let r=(t.body.skipTime||0)>oe()-Gl.last,n=_5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?Gl.bodies:new Promise(async a=>{let s={};_5=0,s.input=m9(e,ag),s.res=bn==null?void 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A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/og)),[v,S]=[A-ig/l*b[0],x-ig/l*b[1]],[T,E]=[A+ig/l*b[2]-v,x+ig/l*b[3]-S],R=[v,S,T,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:_d[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function D5(e,t){let r=(t.object.skipTime||0)>oe()-x9,n=O5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&sg.length>0?(O5++,sg):(O5=0,!he.kernels.includes("mod")||!he.kernels.includes("sparsetodense")?sg:new Promise(async a=>{let 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p=0;p<h;p++)Hl(a,d.mesh[p][0],d.mesh[p][1],d.mesh[p][2],n)}if(n.drawAttention&&d.mesh.length>468)for(let h=468;h<d.mesh.length;h++)Hl(a,d.mesh[h][0],d.mesh[h][1],-255,n);if(n.drawPolygons){if(d.mesh.length>450)for(let h=0;h<Dl.length/3;h++){let p=[Dl[h*3+0],Dl[h*3+1],Dl[h*3+2]].map(c=>d.mesh[c]);K5(a,p,n)}if(d.annotations&&d.annotations.leftEyeIris&&d.annotations.leftEyeIris[0]){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.color,a.beginPath();let h=Math.abs(d.annotations.leftEyeIris[3][0]-d.annotations.leftEyeIris[1][0])/2,p=Math.abs(d.annotations.leftEyeIris[4][1]-d.annotations.leftEyeIris[2][1])/2;a.ellipse(d.annotations.leftEyeIris[0][0],d.annotations.leftEyeIris[0][1],h,p,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.color,a.fill())}if(d.annotations&&d.annotations.rightEyeIris&&d.annotations.rightEyeIris[0]){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.color,a.beginPath();let 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               ${h} ${d.box[1]},
@@ -7053,7 +7069,7 @@ return a / b;`,qie=`
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t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}a.stroke()}}}async function Zd(e,t,r){let n=Ut(xr,r);if(!(!t||!e)&&n.drawGestures){let a=On(e);if(!a)return;a.font=n.font,a.fillStyle=n.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(d,8,2+s*n.lineHeight)),a.fillStyle=n.labelColor,a.fillText(d,6,0+s*n.lineHeight),s+=1}}}}var K5=0;async function X5(e,t,r){let n=Ut(xr,r);if(!t||!e)return;let a=On(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let 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e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of Jn.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Yd&&Yd>0&&(a=a.map(i=>({x:i.x>.5?i.x+Yd:i.x-Yd,y:i.y>.5?i.y+Yd:i.y-Yd})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)kAe(i/t,o/t,a)||(n.set(Q5*n.get(0,o,i,0),0,o,i,0),n.set(Q5*n.get(0,o,i,1),0,o,i,1),n.set(Q5*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var SAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let 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Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?g5(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(z=e.config.face.description)!=null&&z.enabled?await g5(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.description=he.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((O=e.config.face.ssrnet)==null?void 0:O.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let J=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,se=(Q=e.config.face.detector)!=null&&Q.return?et(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};d!=null&&d.age&&(Z.age=d.age),d!=null&&d.gender&&(Z.gender=d.gender),d!=null&&d.genderScore&&(Z.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Z.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),J&&J!==0&&(Z.iris=Math.trunc(500/J/11.7)/100),ee&&(Z.rotation=ee),se&&(Z.tensor=se),h.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var F9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},$9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},P9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(u=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},_9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=KC(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ce={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},t3=0;function z9(e,t){var i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,S,T,E,R,_,M,I,z,O,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ce.canvas=e.canvas),e.error&&(Ce.error=e.error),!Ce.body||e.body.length!==Ce.body.length)Ce.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((Z,ae)=>((a-1)*Ce.body[D].box[ae]+Z)/a),V=e.body[D].boxRaw.map((Z,ae)=>((a-1)*Ce.body[D].boxRaw[ae]+Z)/a),ee=e.body[D].keypoints.map((Z,ae)=>{var de,Ae,be,Ee,Me,De,Be,Ze,ot;return{score:Z.score,part:Z.part,position:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[0]||0)+(Z.position[0]||0))/a:Z.position[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[1]||0)+(Z.position[1]||0))/a:Z.position[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[2]||0)+(Z.position[2]||0))/a:Z.position[2]],positionRaw:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[0]||0)+(Z.positionRaw[0]||0))/a:Z.positionRaw[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[1]||0)+(Z.positionRaw[1]||0))/a:Z.positionRaw[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[2]||0)+(Z.positionRaw[2]||0))/a:Z.positionRaw[2]],distance:[Ce.body[D].keypoints[ae]?((a-1)*(((de=Ce.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((Ae=Z.distance)==null?void 0:Ae[0])||0))/a:(be=Z.distance)==null?void 0:be[0],Ce.body[D].keypoints[ae]?((a-1)*(((Ee=Ce.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+(((Me=Z.distance)==null?void 0:Me[1])||0))/a:(De=Z.distance)==null?void 0:De[1],Ce.body[D].keypoints[ae]?((a-1)*(((Be=Ce.body[D].keypoints[ae].distance)==null?void 0:Be[2])||0)+(((Ze=Z.distance)==null?void 0:Ze[2])||0))/a:(ot=Z.distance)==null?void 0:ot[2]]}}),J={},se={connected:{}};(o=(i=t.body)==null?void 0:i.modelPath)!=null&&o.includes("efficientpose")?se=B0:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?se=_0:(h=(d=t.body)==null?void 0:d.modelPath)!=null&&h.includes("movenet")&&(se=Qh);for(let[Z,ae]of Object.entries(se.connected)){let de=[];for(let Ae=0;Ae<ae.length-1;Ae++){let be=ee.find(Me=>Me.part===ae[Ae]),Ee=ee.find(Me=>Me.part===ae[Ae+1]);be&&Ee&&de.push([be.position,Ee.position])}J[Z]=de}Ce.body[D]={...e.body[D],box:Q,boxRaw:V,keypoints:ee,annotations:J}}if(!Ce.hand||e.hand.length!==Ce.hand.length)Ce.hand=JSON.parse(JSON.stringify(e.hand));else for(let D=0;D<e.hand.length;D++){let Q=e.hand[D].box.map((se,Z)=>((a-1)*Ce.hand[D].box[Z]+se)/a),V=e.hand[D].boxRaw.map((se,Z)=>((a-1)*Ce.hand[D].boxRaw[Z]+se)/a);Ce.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ce.hand[D].keypoints=e.hand[D].keypoints);let ee=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((se,Z)=>se.map((ae,de)=>((a-1)*(Ce.hand[D].keypoints[Z][de]||1)+(ae||0))/a)):[],J={};if(Object.keys(Ce.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ce.hand[D].annotations=e.hand[D].annotations,J=Ce.hand[D].annotations;else if(e.hand[D].annotations)for(let se of Object.keys(e.hand[D].annotations))J[se]=e.hand[D].annotations[se]&&e.hand[D].annotations[se][0]?e.hand[D].annotations[se].map((Z,ae)=>Z.map((de,Ae)=>((a-1)*Ce.hand[D].annotations[se][ae][Ae]+de)/a)):null;Ce.hand[D]={...e.hand[D],box:Q,boxRaw:V,keypoints:ee,annotations:J}}if(!Ce.face||e.face.length!==Ce.face.length)Ce.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let Q=e.face[D].box.map((ee,J)=>((a-1)*Ce.face[D].box[J]+ee)/a),V=e.face[D].boxRaw.map((ee,J)=>((a-1)*Ce.face[D].boxRaw[J]+ee)/a);if(e.face[D].rotation){let ee={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};ee.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,ee.angle={roll:((a-1)*(((f=(c=Ce.face[D].rotation)==null?void 0:c.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[D].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ce.face[D].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[D].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/a,pitch:((a-1)*(((S=(v=Ce.face[D].rotation)==null?void 0:v.angle)==null?void 0:S.pitch)||0)+(((E=(T=e.face[D].rotation)==null?void 0:T.angle)==null?void 0:E.pitch)||0))/a},ee.gaze={bearing:((a-1)*(((_=(R=Ce.face[D].rotation)==null?void 0:R.gaze)==null?void 0:_.bearing)||0)+(((I=(M=e.face[D].rotation)==null?void 0:M.gaze)==null?void 0:I.bearing)||0))/a,strength:((a-1)*(((O=(z=Ce.face[D].rotation)==null?void 0:z.gaze)==null?void 0:O.strength)||0)+(((X=(j=e.face[D].rotation)==null?void 0:j.gaze)==null?void 0:X.strength)||0))/a},Ce.face[D]={...e.face[D],rotation:ee,box:Q,boxRaw:V}}Ce.face[D]={...e.face[D],box:Q,boxRaw:V}}if(!Ce.object||e.object.length!==Ce.object.length)Ce.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let Q=e.object[D].box.map((ee,J)=>((a-1)*Ce.object[D].box[J]+ee)/a),V=e.object[D].boxRaw.map((ee,J)=>((a-1)*Ce.object[D].boxRaw[J]+ee)/a);Ce.object[D]={...e.object[D],box:Q,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ce.persons||D.length!==Ce.persons.length)Ce.persons=JSON.parse(JSON.stringify(D));else for(let Q=0;Q<D.length;Q++)Ce.persons[Q].box=D[Q].box.map((V,ee)=>((a-1)*Ce.persons[Q].box[ee]+V)/a)}e.gesture&&(Ce.gesture=e.gesture);let s=oe();return t3=he.perfadd?t3+Math.round(s-r):Math.round(s-r),e.performance&&(Ce.performance={...e.performance,interpolate:t3}),Ce}var a3={};xs(a3,{distance:()=>nc,match:()=>n3,similarity:()=>r3});function nc(e,t,r={order:2,multiplier:25}){let n=0;for(let a=0;a<e.length;a++){let s=!r.order||r.order===2?e[a]-t[a]:Math.abs(e[a]-t[a]);n+=!r.order||r.order===2?s*s:s**r.order}return(r.multiplier||20)*n}var O9=(e,t,r,n)=>{if(e===0)return 1;let a=t===2?Math.sqrt(e):e**(1/t),s=(1-a/100-r)/(n-r);return Math.max(Math.min(s,1),0)};function r3(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=nc(e,t,r);return O9(n,r.order||2,r.min||0,r.max||1)}function n3(e,t,r={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,a=-1;for(let i=0;i<t.length;i++){let o=nc(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let s=O9(n,r.order||2,r.min||0,r.max||1);return{index:a,distance:n,similarity:s}}function D9(e,t,r,n,a){var o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,S;let s=0,i=[];for(let T of e){let E={id:s++,face:T,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let O of t)T.box[0]>O.box[0]&&T.box[0]<O.box[0]+O.box[2]&&T.box[1]+T.box[3]>O.box[1]&&T.box[1]+T.box[3]<O.box[1]+O.box[3]&&(E.body=O);if(E.body)for(let O of r)O.box[0]+O.box[2]>E.body.box[0]&&O.box[0]+O.box[2]<E.body.box[0]+E.body.box[2]&&O.box[1]+O.box[3]>E.body.box[1]&&O.box[1]+O.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.left=O),O.box[0]<E.body.box[0]+E.body.box[2]&&O.box[0]>E.body.box[0]&&O.box[1]+O.box[3]>E.body.box[1]&&O.box[1]+O.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.right=O);for(let O of n)O.face!==void 0&&O.face===T.id?(o=E.gestures)==null||o.push(O):O.iris!==void 0&&O.iris===T.id?(l=E.gestures)==null||l.push(O):O.body!==void 0&&O.body===((u=E.body)==null?void 0:u.id)?(d=E.gestures)==null||d.push(O):O.hand!==void 0&&O.hand===((p=(h=E.hands)==null?void 0:h.left)==null?void 0:p.id)?(c=E.gestures)==null||c.push(O):O.hand!==void 0&&O.hand===((m=(f=E.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=E.gestures)==null||g.push(O));let R=[],_=[],M=O=>{O&&O.length===4&&(R.push(O[0],O[0]+O[2]),_.push(O[1],O[1]+O[3]))};M((y=E.face)==null?void 0:y.box),M((A=E.body)==null?void 0:A.box),M((b=(x=E.hands)==null?void 0:x.left)==null?void 0:b.box),M((S=(v=E.hands)==null?void 0:v.right)==null?void 0:S.box);let I=Math.min(...R),z=Math.min(..._);E.box=[I,z,Math.max(...R)-I,Math.max(..._)-z],a&&a[1]&&a[2]&&(E.boxRaw=[E.box[0]/a[2],E.box[1]/a[1],E.box[2]/a[2],E.box[3]/a[1]]),i.push(E)}return i}var dg=`
+          `);a.stroke(f),a.stroke(c)}if(n.drawGaze&&((o=(i=d.rotation)==null?void 0:i.gaze)==null?void 0:o.strength)&&((u=(l=d.rotation)==null?void 0:l.gaze)==null?void 0:u.bearing)&&d.annotations.leftEyeIris&&d.annotations.rightEyeIris&&d.annotations.leftEyeIris[0]&&d.annotations.rightEyeIris[0]){a.strokeStyle="pink",a.fillStyle="pink";let h=[d.annotations.leftEyeIris[0][0]+Math.sin(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[3],d.annotations.leftEyeIris[0][1]+Math.cos(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[2]];X5(a,[d.annotations.leftEyeIris[0][0],d.annotations.leftEyeIris[0][1]],[h[0],h[1]],4);let p=[d.annotations.rightEyeIris[0][0]+Math.sin(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[3],d.annotations.rightEyeIris[0][1]+Math.cos(d.rotation.gaze.bearing)*d.rotation.gaze.strength*d.box[2]];X5(a,[d.annotations.rightEyeIris[0][0],d.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function qd(e,t,r){var s;let n=Ut(xr,r);if(!t||!e)return;let a=Dn(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Va(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=n.useDepth&&t[i].keypoints[o].position[2]?Wa(t[i].keypoints[o].position[2]||0):n.color,Hl(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=n.useDepth&&o.position[2]?Wa(o.position[2]):n.color,a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)P9(a,l,n)}}}async function Kd(e,t,r){let n=Ut(xr,r);if(!t||!e)return;let a=Dn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of 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t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} ${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}a.stroke()}}}async function Zd(e,t,r){let n=Ut(xr,r);if(!(!t||!e)&&n.drawGestures){let a=Dn(e);if(!a)return;a.font=n.font,a.fillStyle=n.color;let s=1;for(let i=0;i<t.length;i++){let o=[],l=[];if([o,l]=Object.entries(t[i]),l.length>1&&l[1].length>0){let u=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${u}: ${l[1]}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(d,8,2+s*n.lineHeight)),a.fillStyle=n.labelColor,a.fillText(d,6,0+s*n.lineHeight),s+=1}}}}var Z5=0;async function Y5(e,t,r){let n=Ut(xr,r);if(!t||!e)return;let a=Dn(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s=0;s<t.length;s++)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Va(a,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels){let i=`person #${s}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),a.fillStyle=n.labelColor,a.fillText(i,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2])}a.stroke()}}}async function J5(e,t){if(!e||!t)return;let r=Dn(t);!r||r.drawImage(e,0,0)}async function Q5(e,t,r){if(!t||!t.performance||!t||!e)return null;let n=oe(),a=Ut(xr,r),s=Promise.all([Hd(e,t.face,a),qd(e,t.body,a),Kd(e,t.hand,a),Xd(e,t.object,a),Zd(e,t.gesture,a)]);return Z5=he.perfadd?Z5+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=Z5,s}var Yd=.1,t3=.5;function OAe(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function _9(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of Jn.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Yd&&Yd>0&&(a=a.map(i=>({x:i.x>.5?i.x+Yd:i.x-Yd,y:i.y>.5?i.y+Yd:i.y-Yd})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)OAe(i/t,o/t,a)||(n.set(t3*n.get(0,o,i,0),0,o,i,0),n.set(t3*n.get(0,o,i,1),0,o,i,1),n.set(t3*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var LAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},z9=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},a=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,v,S,T,E]=m,R,_,M;return 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V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){ie("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await _9(p[V]);re(p[V].tensor),p[V].tensor=ae}let ee=p[V].mesh&&p[V].mesh.length>200?z9(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(f=e.config.face.emotion)!=null&&f.enabled?d5(p[V].tensor||ct([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(m=e.config.face.emotion)!=null&&m.enabled?await d5(p[V].tensor||ct([]),e.config,V,p.length):[],e.performance.emotion=he.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?Ub(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await 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Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?A5(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(z=e.config.face.description)!=null&&z.enabled?await A5(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.description=he.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((O=e.config.face.ssrnet)==null?void 0:O.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let J=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,se=(Q=e.config.face.detector)!=null&&Q.return?rt(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};d!=null&&d.age&&(Z.age=d.age),d!=null&&d.gender&&(Z.gender=d.gender),d!=null&&d.genderScore&&(Z.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Z.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),J&&J!==0&&(Z.iris=Math.trunc(500/J/11.7)/100),ee&&(Z.rotation=ee),se&&(Z.tensor=se),h.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var O9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},D9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},L9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(u=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},B9=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=QC(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ce={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},n3=0;function W9(e,t){var i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,S,T,E,R,_,M,I,z,O,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ce.canvas=e.canvas),e.error&&(Ce.error=e.error),!Ce.body||e.body.length!==Ce.body.length)Ce.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((Z,ae)=>((a-1)*Ce.body[D].box[ae]+Z)/a),V=e.body[D].boxRaw.map((Z,ae)=>((a-1)*Ce.body[D].boxRaw[ae]+Z)/a),ee=e.body[D].keypoints.map((Z,ae)=>{var de,Ae,be,Ee,Me,De,Be,Ze,ot;return{score:Z.score,part:Z.part,position:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[0]||0)+(Z.position[0]||0))/a:Z.position[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[1]||0)+(Z.position[1]||0))/a:Z.position[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].position[2]||0)+(Z.position[2]||0))/a:Z.position[2]],positionRaw:[Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[0]||0)+(Z.positionRaw[0]||0))/a:Z.positionRaw[0],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[1]||0)+(Z.positionRaw[1]||0))/a:Z.positionRaw[1],Ce.body[D].keypoints[ae]?((a-1)*(Ce.body[D].keypoints[ae].positionRaw[2]||0)+(Z.positionRaw[2]||0))/a:Z.positionRaw[2]],distance:[Ce.body[D].keypoints[ae]?((a-1)*(((de=Ce.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((Ae=Z.distance)==null?void 0:Ae[0])||0))/a:(be=Z.distance)==null?void 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for(let D=0;D<e.hand.length;D++){let Q=e.hand[D].box.map((se,Z)=>((a-1)*Ce.hand[D].box[Z]+se)/a),V=e.hand[D].boxRaw.map((se,Z)=>((a-1)*Ce.hand[D].boxRaw[Z]+se)/a);Ce.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ce.hand[D].keypoints=e.hand[D].keypoints);let ee=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((se,Z)=>se.map((ae,de)=>((a-1)*(Ce.hand[D].keypoints[Z][de]||1)+(ae||0))/a)):[],J={};if(Object.keys(Ce.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ce.hand[D].annotations=e.hand[D].annotations,J=Ce.hand[D].annotations;else if(e.hand[D].annotations)for(let se of Object.keys(e.hand[D].annotations))J[se]=e.hand[D].annotations[se]&&e.hand[D].annotations[se][0]?e.hand[D].annotations[se].map((Z,ae)=>Z.map((de,Ae)=>((a-1)*Ce.hand[D].annotations[se][ae][Ae]+de)/a)):null;Ce.hand[D]={...e.hand[D],box:Q,boxRaw:V,keypoints:ee,annotations:J}}if(!Ce.face||e.face.length!==Ce.face.length)Ce.face=JSON.parse(JSON.stringify(e.face));else 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rc,this.draw={options:xr,canvas:(r,n)=>Z5(r,n),face:(r,n,a)=>Hd(r,n,a),body:(r,n,a)=>qd(r,n,a),hand:(r,n,a)=>Kd(r,n,a),gesture:(r,n,a)=>Zd(r,n,a),object:(r,n,a)=>Xd(r,n,a),person:(r,n,a)=>X5(r,n,a),all:(r,n,a)=>Y5(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=SC,this.faceUVMap=TC,this.gl=Ct,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(bs)),this.config.backend=t}validate(t){return G1(bs,t||this.config)}now(){return oe()}image(t,r=!0){return Fd(t,this.config,r)}async segmentation(t,r){return S9(t,r,this.config)}enhance(t){return m5(t)}compare(t,r){return cN(this.config,t,r)}async init(){await ug(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=Ut(this.config,t)),this.env.initial&&(this.config.debug&&ie(`version: ${this.version}`),this.config.debug&&ie(`tfjs version: ${this.tf.version["tfjs-core"]}`),await ug(this)||ie("error: backend check failed"),await ld(),this.env.browser&&(this.config.debug&&ie("configuration:",this.config),this.config.debug&&ie("environment:",this.env),this.config.debug&&ie("tf flags:",this.tf.ENV.flags))),await G5(this),this.env.initial&&this.config.debug&&ie("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await j5(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return z9(t,this.config)}async warmup(t){let r=oe(),n=await L9(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,v,S,T,E,R,_,M,I,z,O,j,X,D,Q,V,ee,J;this.state="config";let a;this.config=Ut(this.config,r),this.state="check";let s=pp(this,hg).call(this,t);s&&(ie(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await ug(this),await this.load(),a=oe(),this.state="image";let o=await Fd(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&ie("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await hN(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?e3(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await e3(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?Ut(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?W5(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?Qb(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?i5(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?P5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await W5(o.tensor,p):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?u=this.config.body.enabled?await Qb(o.tensor,p):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await i5(o.tensor,p):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await P5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?Ut(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?w5(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?T5(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(z=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&z.includes("handdetect")?d=this.config.hand.enabled?await w5(o.tensor,c):[]:(j=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await T5(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?z5(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?r5(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?h=this.config.object.enabled?await z5(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await r5(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...$9(l),...F9(u),..._9(d),...P9(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((J=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return D9(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Jd=new WeakMap,ac=new WeakMap,sc=new WeakMap,hg=new WeakMap;return DE(zAe);})();
+2Q==`;async function jAe(e){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),r,n;switch(e.config.warmup){case"face":r=await t(cg);break;case"body":case"full":r=await t(fg);break;default:r=null}if(r){let a=await createImageBitmap(r);n=await e.detect(a,e.config),a.close()}return n}async function HAe(e){return new Promise(t=>{let r;switch(e.config.warmup){case"face":r="data:image/jpeg;base64,"+cg;break;case"full":case"body":r="data:image/jpeg;base64,"+fg;break;default:r=null}let n;if(typeof Image!="undefined")n=new Image;else if(he.Image)n=new he.Image;else return;n.onload=async()=>{let a=qr(n.naturalWidth,n.naturalHeight);if(!a)ie("Warmup: Canvas not found"),t(void 0);else{let s=a.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(a),o=await e.detect(i.tensor,e.config);t(o)}},r?n.src=r:t(void 0)})}async function qAe(e){let t=a=>Buffer.from(a,"base64"),r;e.config.warmup==="face"?r=t(cg):r=t(fg);let n;if("node"in Ue){let a=(void 0).decodeJpeg(r),s=a.expandDims(0);e.tf.dispose(a),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&ie("Warmup tfjs-node not loaded");return n}async function KAe(e){let t;return typeof createImageBitmap=="function"?t=await jAe(e):typeof Image!="undefined"||he.Canvas!==void 0?t=await HAe(e):t=await qAe(e),t}async function G9(e,t){let r=oe();return e.state="warmup",t&&(e.config=Ut(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:oe(),persons:[],error:null}:new Promise(async n=>{let a=await KAe(e),s=oe();e.config.debug&&ie("warmup",e.config.warmup,Math.round(s-r),"ms"),e.emit("warmup"),n(a)})}var Jd,ac,sc,mg,o3=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");hp(this,Jd,void 0);hp(this,ac,void 0);hp(this,sc,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!pp(this,ac))return;let r=this.tf.engine().state.numTensors,n=pp(this,Jd);cp(this,Jd,r);let a=r-n;a!==0&&ie(...t,a)});hp(this,mg,t=>{if(!pp(this,sc))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof nt))return"input must be a tensor";try{this.tf.getBackend()}catch(r){return"backend not loaded"}return null});fe(this,"similarity",a3);fe(this,"distance",nc);fe(this,"match",s3);fe(this,"emit",t=>{var r;this.events&&this.events.dispatchEvent&&((r=this.events)==null||r.dispatchEvent(new Event(t)))});this.env=he,vs.wasmPath=Hh["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${C2}/dist/`,vs.modelBasePath=he.browser?"../models/":"file://models/",vs.backend=he.browser?"humangl":"tensorflow",this.version=$b,Object.defineProperty(this,"version",{value:$b}),this.config=JSON.parse(JSON.stringify(vs)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Ut(this.config,t)),xN(this.config),this.tf=Ue,this.state="idle",cp(this,Jd,0),cp(this,ac,!1),cp(this,sc,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new rc,this.draw={options:xr,canvas:(r,n)=>J5(r,n),face:(r,n,a)=>Hd(r,n,a),body:(r,n,a)=>qd(r,n,a),hand:(r,n,a)=>Kd(r,n,a),gesture:(r,n,a)=>Zd(r,n,a),object:(r,n,a)=>Xd(r,n,a),person:(r,n,a)=>Y5(r,n,a),all:(r,n,a)=>Q5(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=RC,this.faceUVMap=MC,this.gl=Ct,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(vs)),this.config.backend=t}validate(t){return q1(vs,t||this.config)}now(){return oe()}image(t,r=!0){return Fd(t,this.config,r)}async segmentation(t,r){return R9(t,r,this.config)}enhance(t){return y5(t)}compare(t,r){return AN(this.config,t,r)}async init(){await hg(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=Ut(this.config,t)),this.env.initial&&(this.config.debug&&ie(`version: ${this.version}`),this.config.debug&&ie(`tfjs version: ${this.tf.version["tfjs-core"]}`),await hg(this)||ie("error: backend check failed"),await ld(),this.env.browser&&(this.config.debug&&ie("configuration:",this.config),this.config.debug&&ie("environment:",this.env),this.config.debug&&ie("tf flags:",this.tf.ENV.flags))),await H5(this),this.env.initial&&this.config.debug&&ie("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await q5(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return W9(t,this.config)}async warmup(t){let r=oe(),n=await G9(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,v,S,T,E,R,_,M,I,z,O,j,X,D,Q,V,ee,J;this.state="config";let a;this.config=Ut(this.config,r),this.state="check";let s=pp(this,mg).call(this,t);s&&(ie(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await hg(this),await this.load(),a=oe(),this.state="image";let o=await Fd(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&ie("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await yN(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?r3(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await r3(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?Ut(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?U5(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?t5(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?l5(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?z5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await U5(o.tensor,p):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?u=this.config.body.enabled?await t5(o.tensor,p):[]:(S=this.config.body.modelPath)!=null&&S.includes("efficientpose")?u=this.config.body.enabled?await l5(o.tensor,p):[]:(T=this.config.body.modelPath)!=null&&T.includes("movenet")&&(u=this.config.body.enabled?await z5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?Ut(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?I5(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?C5(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(z=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&z.includes("handdetect")?d=this.config.hand.enabled?await I5(o.tensor,c):[]:(j=(O=this.config.hand.detector)==null?void 0:O.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await C5(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?D5(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?a5(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?h=this.config.object.enabled?await D5(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await a5(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...D9(l),...O9(u),...B9(d),...L9(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((J=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return U9(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Jd=new WeakMap,ac=new WeakMap,sc=new WeakMap,mg=new WeakMap;return UE(ZAe);})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -7994,6 +8010,31 @@ lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
  * limitations under the License.
  * =============================================================================
  */
+/**
+ * @license
+ * Copyright 2022 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
 /**
  * @license
  * Copyright 2022 Google LLC. All Rights Reserved.
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index 0b56b416..2fca7043 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -4,63 +4,63 @@
   author: <https://github.com/vladmandic>'
 */
 
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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new 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s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Of(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Of(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Zf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Of(e)?null:e.backwardsFunc,d;return 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Xf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Xf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of 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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new K$(this.backendInstance),!0}setupRegisteredKernels(){em(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){em(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof sl)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ir(`Initialization of backend ${e} failed`),ir(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ir(`Initialization of backend ${e} failed`),ir(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return nm.nextTensorId++}nextVariableId(){return nm.nextVariableId++}clone(e){let t=M.runKernel(Wa,{x:e}),n={x:e},s=a=>({x:()=>{let i="float32",o={x:a},u={dtype:i};return M.runKernel(Ta,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Jf(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=zf(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(zf(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Jf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:T,dtype:N}=x;return this.makeTensorFromDataId(k,T,N)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=zf(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=rx(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&or(e[0])&&(r=e.map(o=>Dl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n==="string"){let o=this.state.tensorInfo.get(a),u=Jw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new gd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Yf(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof gd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Yf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=rx(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=jd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Fg(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof et,()=>"The result y returned by f() must be a tensor.");let a=Q$(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[r.id]=n==null?c_(r.shape):n,Z$(i,a,u=>this.tidy(u),d_);let o=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let l of u.saved)l.dispose()}),this.state.activeTape=null),{value:r,grads:o}})}customGrad(e){return O(fr(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{O(t.every(i=>i instanceof et),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let n,s={};t.forEach((i,o)=>{s[o]=i});let r=(i,o)=>(n=e(...t,o),O(n.value instanceof et,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),O(fr(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),a=(i,o)=>{let u=n.gradFunc(i,o),l=Array.isArray(u)?u:[u];O(l.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),O(l.every(p=>p instanceof et),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let c={};return l.forEach((p,d)=>{c[d]=()=>p}),c};return this.runKernelFunc({forwardFunc:r,backwardsFunc:a,inputs:s})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Gu(),n=await this.backend.time(e);return n.wallMs=Gu()-t,n}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ux;for(let e in 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|nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(t.substr(0,4))}return!1}function mk(){return typeof window!="undefined"&&window.document!=null||typeof WorkerGlobalScope!="undefined"}var Kn=X();Kn.registerFlag("DEBUG",()=>!1,e=>{e&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});Kn.registerFlag("IS_BROWSER",()=>mk());Kn.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");Kn.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));Kn.registerFlag("PROD",()=>!1);Kn.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>Kn.getBool("DEBUG"));Kn.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);Kn.registerFlag("IS_TEST",()=>!1);Kn.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);Kn.registerFlag("WRAP_TO_IMAGEBITMAP",()=>!1);Kn.registerFlag("ENGINE_COMPILE_ONLY",()=>!1);function Rs(e,t){let n=e;if(Qt(e))return t==="string"?[]:[e.length];if(!Array.isArray(e))return[];let s=[];for(;Array.isArray(n)||Qt(n)&&t!=="string";)s.push(n.length),n=n[0];return Array.isArray(e)&&X().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&gk(e,s,[]),s}function gk(e,t,n){if(n=n||[],!Array.isArray(e)&&!Qt(e)){O(t.length===0,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);return}O(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),O(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);let s=t.slice(1);for(let r=0;r<e.length;++r)gk(e[r],s,n.concat(r))}function lx(e,t,n,s){if(e!=="string_or_numeric"){if(e==null)throw new Error("Expected dtype cannot be null.");if(e!=="numeric"&&e!==t||e==="numeric"&&t==="string")throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`)}}function _(e,t,n,s="numeric"){if(e instanceof et)return lx(s,e.dtype,t,n),e;let r=qd(e);if(r!=="string"&&["bool","int32","float32"].indexOf(s)>=0&&(r=s),lx(s,r,t,n),e==null||!Qt(e)&&!Array.isArray(e)&&typeof e!="number"&&typeof e!="boolean"&&typeof e!="string"){let u=e==null?"null":e.constructor.name;throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`)}let a=Rs(e,r);!Qt(e)&&!Array.isArray(e)&&(e=[e]);let o=r!=="string"?dp(e,r):ra(e,[],!0);return M.makeTensor(o,a,r)}function Hu(e,t,n,s="numeric"){if(!Array.isArray(e))throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);return e.map((a,i)=>_(a,`${t}[${i}]`,n,s))}var m_="__op";function L(e){let t=Object.keys(e);if(t.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. 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2 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Ku(i,o,u,c,l,a)}var FE=L({batchNorm2d_:DE});function OE(e,t,n,s,r,a){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),O(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),O(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Ku(i,o,u,c,l,a)}var PE=L({batchNorm3d_:OE});function 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+          ${a.shape}`);let i={data:s,indices:r,segmentIds:a};return M.runKernel(ip,i)}var zO=L({sparseSegmentMean_:PO});function MO(e,t,n){let s=_(e,"data","sparseSegmentSum"),r=_(t,"indices","sparseSegmentSum","int32"),a=_(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
-         ${a.shape}`);let i={data:s,indices:r,segmentIds:a};return M.runKernel(ap,i)}var FO=L({sparseSegmentSum_:DO});function OO(e,t,n,s,r,a,i,o){let u=_(e,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let l=_(t,"dataSplits","stringNGrams");if(l.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:i,preserveShortSequences:o},p={data:u,dataSplits:l},d=M.runKernel(op,p,c);return{nGrams:d[0],nGramsSplits:d[1]}}var PO=L({stringNGrams_:OO});function zO(e,t,n=!0){let s=_(e,"input","stringSplit","string"),r=_(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},i={input:s,delimiter:r},o=M.runKernel($g,i,a);return{indices:o[0],values:o[1],shape:o[2]}}var MO=L({stringSplit_:zO});function LO(e,t){let n=_(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return M.runKernel(_g,r,s)}var BO=L({stringToHashBucketFast_:LO}),Ipe={fft:lb,ifft:Id,rfft:cb,irfft:xI},Spe={hammingWindow:mF,hannWindow:AI,frame:EI,stft:vF},qn={flipLeftRight:IF,grayscaleToRGB:CF,resizeNearestNeighbor:XF,resizeBilinear:jF,rotateWithOffset:TF,cropAndResize:wF,nonMaxSuppression:_F,nonMaxSuppressionAsync:zF,nonMaxSuppressionWithScore:LF,nonMaxSuppressionWithScoreAsync:VF,nonMaxSuppressionPadded:UF,nonMaxSuppressionPaddedAsync:HF,threshold:ZF,transform:eO},VO={bandPart:nO,gramSchmidt:rO,qr:iO},Cpe={absoluteDifference:cO,computeWeightedLoss:Ys,cosineDistance:pO,hingeLoss:fO,huberLoss:gO,logLoss:yO,meanSquaredError:xO,sigmoidCrossEntropy:IO,softmaxCrossEntropy:NO},Wc={sparseFillEmptyRows:$O,sparseReshape:AO,sparseSegmentMean:RO,sparseSegmentSum:FO},Mf={stringNGrams:PO,stringSplit:MO,stringToHashBucketFast:BO},_r=class extends Vk{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(a)}else this.applyGradients(r);return Re(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return XR(e,t)}dispose(){this.iterations_!=null&&Re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:we(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(_r,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var fb=class extends _r{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;j(()=>{let l=ie(V(o,this.rho),V(ct(i),1-this.rho)),c=V(xe(dn(ie(u,this.epsilon)),dn(ie(o,this.epsilon))),i),p=ie(V(u,this.rho),V(ct(c),1-this.rho));o.assign(l),u.assign(p);let d=ie(V(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};fb.className="Adadelta";Tr(fb);var mb=class extends _r{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>Pl(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[s].variable;j(()=>{let o=ie(i,ct(a));i.assign(o);let u=ie(V(xe(a,dn(ie(o,M.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};mb.className="Adagrad";Tr(mb);var gb=class extends _r{constructor(e,t,n,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(dn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(pa(this.beta1,this.iterations_+1)),this.accBeta2.assign(pa(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};gb.className="Adam";Tr(gb);var bb=class extends _r{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=$r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};bb.className="Adamax";Tr(bb);var Ip=class extends _r{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Ip.className="SGD";Tr(Ip);var yb=class extends Ip{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=we(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulations[s]==null&&(this.accumulations[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(!1))});let a=this.accumulations[s].variable,i=Array.isArray(e)?e[s].tensor:e[n];i!=null&&j(()=>{let o,u=ie(V(this.m,a),i);this.useNesterov?o=ie(V(this.c,ie(i,V(u,this.m))),r):o=ie(V(this.c,u),r),a.assign(u),r.assign(o)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};yb.className="Momentum";Tr(yb);var vb=class extends _r{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};vb.className="RMSProp";Tr(vb);var Wr=class{static sgd(e){return new Ip(e)}static momentum(e,t,n=!1){return new yb(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new vb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new gb(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new fb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new bb(e,t,n,s,r)}static adagrad(e,t=.1){return new mb(e,t)}},Pi={sgd:Wr.sgd,momentum:Wr.momentum,adadelta:Wr.adadelta,adagrad:Wr.adagrad,rmsprop:Wr.rmsprop,adamax:Wr.adamax,adam:Wr.adam},WO=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function OI(){return new Promise(e=>WO(()=>e()))}var 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HO=L({stringToHashBucketFast_:GO}),zpe={fft:db,ifft:Id,rfft:pb,irfft:SI},Mpe={hammingWindow:xF,hannWindow:FI,frame:OI,stft:SF},jn={flipLeftRight:$F,grayscaleToRGB:AF,resizeNearestNeighbor:eO,resizeBilinear:ZF,rotateWithOffset:RF,cropAndResize:NF,nonMaxSuppression:FF,nonMaxSuppressionAsync:WF,nonMaxSuppressionWithScore:GF,nonMaxSuppressionWithScoreAsync:qF,nonMaxSuppressionPadded:KF,nonMaxSuppressionPaddedAsync:YF,threshold:sO,transform:aO},qO={bandPart:oO,gramSchmidt:lO,qr:dO},Lpe={absoluteDifference:mO,computeWeightedLoss:Qs,cosineDistance:bO,hingeLoss:vO,huberLoss:wO,logLoss:IO,meanSquaredError:CO,sigmoidCrossEntropy:$O,softmaxCrossEntropy:EO},Wc={sparseFillEmptyRows:DO,sparseReshape:OO,sparseSegmentMean:zO,sparseSegmentSum:LO},Bf={stringNGrams:VO,stringSplit:UO,stringToHashBucketFast:HO},Ar=class extends Hk{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(a)}else this.applyGradients(r);return Re(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return eD(e,t)}dispose(){this.iterations_!=null&&Re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:we(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ar,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var gb=class extends Ar{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;j(()=>{let l=ie(V(o,this.rho),V(ct(i),1-this.rho)),c=V(xe(dn(ie(u,this.epsilon)),dn(ie(o,this.epsilon))),i),p=ie(V(u,this.rho),V(ct(c),1-this.rho));o.assign(l),u.assign(p);let d=ie(V(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};gb.className="Adadelta";$r(gb);var bb=class extends Ar{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>Pl(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[s].variable;j(()=>{let o=ie(i,ct(a));i.assign(o);let u=ie(V(xe(a,dn(ie(o,M.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};bb.className="Adagrad";$r(bb);var yb=class extends Ar{constructor(e,t,n,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=we(t).variable(),this.accBeta2=we(n).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(dn(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(ha(this.beta1,this.iterations_+1)),this.accBeta2.assign(ha(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};yb.className="Adam";$r(yb);var vb=class extends Ar{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=_r(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};vb.className="Adamax";$r(vb);var Cp=class extends Ar{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(we(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Cp.className="SGD";$r(Cp);var xb=class extends Cp{constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=we(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulations[s]==null&&(this.accumulations[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(!1))});let a=this.accumulations[s].variable,i=Array.isArray(e)?e[s].tensor:e[n];i!=null&&j(()=>{let o,u=ie(V(this.m,a),i);this.useNesterov?o=ie(V(this.c,ie(i,V(u,this.m))),r):o=ie(V(this.c,u),r),a.assign(u),r.assign(o)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Re(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};xb.className="Momentum";$r(xb);var wb=class extends Ar{constructor(e,t=.9,n=0,s=null,r=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),dn(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),dn(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};wb.className="RMSProp";$r(wb);var Ur=class{static sgd(e){return new Cp(e)}static momentum(e,t,n=!1){return new xb(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new wb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new yb(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new gb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new vb(e,t,n,s,r)}static 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this.assertNotDisposed(),EL(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function EL(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function vm(e){return e.map(t=>t.read())}function Pb(e){e.forEach(t=>{t[0].write(t[1])})}var Dt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},$s=class{constructor(e,t,n,s,r,a,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=i,this.id=YI(),a!=null&&(this.originalName=HI(a),this.name=qI(this.originalName)),this.rank=t.length}},RL=0,Fp=class{constructor(e,t){this.callArgs=t,this.id=RL++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},DL=0,Ge=class extends ae.Serializable{constructor(e={}){super(),this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=DL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+"_"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new hs(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return bn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return bn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return ea(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=FL(e),i=this.computeOutputShape(a),o,u=OL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new hs(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Cd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return vm(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=vm(t);for(let r=0;r<s.length;++r){let a=s[r],i=t[r],o=e[r];if(!w.arraysEqual(a.shape,o.shape))throw new U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Pb(n)})}addWeight(e,t,n,s,r,a,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new U(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=o!=null?o():ht("zeros"));let u=s.apply(t,n),l=new AL(u,n,e,a,i);return u.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=dt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,i=null){let o=dt(e);t=dt(t),n=dt(n),s=dt(s),r=Sd(r),a=Sd(a);let u=[],l=[],c=[];for(let p of o)u.push(p.sourceLayer),l.push(p.nodeIndex),c.push(p.tensorIndex);new Fp({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function FL(e){e=dt(e);let t=[];for(let n of e)t.push(n.shape);return bn(t)}function OL(e){return"float32"}function QI(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let i=s.inputTensors[a],o=s.inboundLayers[a],u=s.nodeIndices[a],l=QI(i,o,u);for(let c of l)r.indexOf(c)===-1&&r.push(c)}return r}}}var Qo=class extends Ge{constructor(e){if(super({dtype:e.dtype,name:e.name!=null?e.name:Dp("input").toString()}),e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new U("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new U("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new $s(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new U(`Cannot pass any input to an InputLayer's apply() method. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function mB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){if(super({}),this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,"input layer has >1 nodes"),Cs(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Qo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,T)=>{(x==null||k==null||T==null)&&(x=b.sourceLayer,k=b.nodeIndex,T=b.tensorIndex);let N=x.inboundNodes[k];if(v.indexOf(N)!==-1)throw new hs(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(N)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(N)===-1&&v.push(N);let E=N.inboundLayers.length;for(let A=0;A<E;A++){let P=N.inputTensors[A],R=N.inboundLayers[A],F=N.nodeIndices[A],$=N.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(N);v.indexOf(N)>=0;)v.splice(v.indexOf(N),1);i.push(N)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],T=b.nodeIndices[x],N=k.inboundNodes[T],E=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(y+1,E),n[N.id]=N}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],T=a[x.id];return k<T?-1:k>T?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new hs(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new hs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Pb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${uS}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=wm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Qr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Au(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=fa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=fa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!BM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function gB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function lS(e,t){return gB(e,t,"classWeight")}async function cS(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Hu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,"float32")}else return null}function bB(e,t){return V(e,t)}var yB=32;function dS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Px("input",e.inputNames,n),i=Px("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Px(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function vB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function xB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Lx(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Lx(t,this.feedOutputNames,r,!1,"target"),TB(e,t,null),$B(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error("sample weight is not supported yet.");let u=null;if(s!=null){let l=lS(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await cS(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,"steps"),i=[];if(s>0)throw new Fe("Verbose mode is not implemented yet.");if(r!=null)throw new Fe("steps mode in testLoop() is not implemented yet");{let o=Im(a,n),u=Zt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=ta(u,c,p-c),h=Wb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(we(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;Sx(e,s)>1&&(r+=`_${Sx(e.slice(0,n),s)}`),t.push(r)}return t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],i=()=>{let c=[];for(let f=0;f<this.inputs.length;++f)c.push({key:this.inputs[f],value:n[f]});let p=new Qr(c),d=Au(this.outputs,p,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],d[f]);r[f]!=null&&(g=bB(g,r[f]));let b=St(g);t.push(b),f===0?h=g:h=ie(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],b=this.metricsTensors[f][1];m=St(g(s[b],d[b]))}Ht(m),a.push(m)}return h=St(h),this.calculateLosses().forEach(f=>{h=ie(h,f)}),h},o=this.collectedTrainableWeights.map(c=>c.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(a)}}makeTestFunction(){this.testFunction=e=>j(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Qr(a),o=Au(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=St(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=St(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return CB(this,e,t,n)}async fitDataset(e,t){return xB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ds(n[0],e),ds(n[1],t),bn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=dm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-dm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=Ws(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Zu(e.optimizer_config),n=ms(t),s;if(typeof e.loss=="string")s=Kr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Kr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Kr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Kr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Kr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=En.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await En.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:AB,generatedBy:`TensorFlow.js tfjs-layers v${uS}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:l,specs:c}=await En.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=En.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Dx(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Dx(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className="Model";ae.registerClass(pr);var hS=class extends pr{};hS.className="Functional";ae.registerClass(hS);async function EB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Zu(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function RB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return DB(e,void 0,t)}async function DB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Zu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=FB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function FB(e,t){let n=En.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Cm=class extends pr{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Cm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=ZI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=QI(this.outputs[0])}this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:fa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new hs("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Cm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Ub=Cm;Ub.className="Sequential";ae.registerClass(Ub);function Npe(e){return new pr(e)}function Tpe(e){return new Ub(e)}function $pe(e,t){return t==null&&(t={}),RB(e,t)}function OB(e){return ZI(e)}function _pe(e,t){zb.registerCallbackConstructor(e,t)}var In=class extends ae.Serializable{getConfig(){return{}}},fS=class extends In{apply(e,t=1){return aL(e,t)}};fS.className="elu";ae.registerClass(fS);var mS=class extends In{apply(e){return gI(e)}};mS.className="selu";ae.registerClass(mS);var gS=class extends In{apply(e){return Xs(e)}};gS.className="relu";ae.registerClass(gS);var bS=class extends In{apply(e){return j(()=>bp(6,Xs(e)))}};bS.className="relu6";ae.registerClass(bS);var yS=class extends In{apply(e){return e}};yS.className="linear";ae.registerClass(yS);var vS=class extends In{apply(e){return qs(e)}};vS.className="sigmoid";ae.registerClass(vS);var xS=class extends In{apply(e){return oL(e)}};xS.className="hardSigmoid";ae.registerClass(xS);var wS=class extends In{apply(e){return zl(e)}};wS.className="softplus";ae.registerClass(wS);var kS=class extends In{apply(e){return iL(e)}};kS.className="softsign";ae.registerClass(kS);var IS=class extends In{apply(e){return qu(e)}};IS.className="tanh";ae.registerClass(IS);var Gb=class extends In{apply(e,t=-1){return ub(e,t)}};Gb.className="softmax";ae.registerClass(Gb);var SS=class extends In{apply(e,t=-1){return iI(e,t)}};SS.className="logSoftmax";ae.registerClass(SS);var CS=class extends In{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};CS.className="swish";ae.registerClass(CS);var NS=class extends In{apply(e){return j(()=>V(e,qu(zl(e))))}};NS.className="mish";ae.registerClass(NS);function br(e){return e.getClassName()}function Gf(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function yr(e){if(e==null){let t={};return t.className="linear",t.config={},Gf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Gf(t)}else return e instanceof In?e:Gf(e)}function Hb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var TS=class extends ae.Serializable{},Gl=class extends TS{constructor(e){super(),Hb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Wl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Gl.className="L1L2";ae.registerClass(Gl);function PB(e){return Hb(e),new Gl({l1:e!=null?e.l1:null,l2:0})}function zB(e){return Hb(e),new Gl({l2:e!=null?e.l2:null,l1:0})}var Vx={l1l2:"L1L2"};function it(e){return wb(e)}function Wx(e,t={}){return Ll(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Vx?Vx[e]:e,config:{}};return Wx(n)}else return e instanceof TS?e:Wx(e)}var qb=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Vn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};qb.className="ReLU";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Xg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className="LeakyReLU";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),sb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:it(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Kb.className="PReLU";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="ELU";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Yb.className="ThresholdedReLU";ae.registerClass(Yb);var Qb=class extends Ge{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Gb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Qb.className="Softmax";ae.registerClass(Qb);function Xi(e,t,n){if(typeof e=="number")return fa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!tL(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(a==="channelsFirst"&&(e=qe(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Zk(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ws(o,n)),o})}function Ux(e,t,n,s=[1,1],r="valid",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Zb(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=ha.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=qe(u,[0,3,1,2])),u})}function LB(e,t,n,s=[1,1,1],r="valid",a,i){return j(()=>{if(a==null&&(a=bs()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=$S(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=eI(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ws(o,n)),a==="channelsFirst"&&(o=qe(o,[0,4,1,2,3])),o})}var Jb=class extends Ge{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Jb.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,"kernelSize"),this.strides=Xi(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hl=class extends Jb{constructor(e,t){super(e,t),this.kernel=null,Hl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=UI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=MB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Ux(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=LB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},_S=class extends Hl{constructor(e){super(2,e),_S.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=_S;zp.className="Conv2D";ae.registerClass(zp);var AS=class extends Hl{constructor(e){super(3,e),AS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=AS;Mp.className="Conv3D";ae.registerClass(Mp);var ey=class extends zp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let g=Jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className="Conv2DTranspose";ae.registerClass(ey);var ty=class extends Mp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,4,1]));let k=tR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ty.className="Conv3DTranspose";ae.registerClass(ty);var ES=class extends Hl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=c3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};ES.className="SeparableConv";var ny=class extends ES{constructor(e){super(2,e)}};ny.className="SeparableConv2D";ae.registerClass(ny);var RS=class extends Hl{constructor(e){super(1,e),RS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!kb(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},sy=RS;sy.className="Conv1D";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className="Cropping2D";ae.registerClass(ry);var ay=class extends Ge{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,ZM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?qn.resizeNearestNeighbor(n,[r,a]):qn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ay.className="UpSampling2D";ae.registerClass(ay);function BB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=bs()),Ct(r);let i=Zb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}var iy=class extends Jb{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=BB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};iy.className="DepthwiseConv2D";ae.registerClass(iy);function DS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function FS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=qe(r,l)),s&&(t=Zn(t,0),r!=null&&(r=Zn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Qn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Jn(c,1)),[p,g,d]})}var OS=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ym(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");ym(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=FS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Vl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?gm(t,[1,n]):t):this.cell.stateSize>1?[gm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===OS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Ar=OS;Ar.className="RNN";ae.registerClass(Ar);var ql=class extends Ge{},Lp=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className="SimpleRNNCell";ae.registerClass(Lp);var oy=class extends Ar{constructor(e){e.cell=new Lp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};oy.className="SimpleRNN";ae.registerClass(oy);var Bp=class extends ql{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className="GRUCell";ae.registerClass(Bp);var uy=class extends Ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className="GRU";ae.registerClass(uy);var jl=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return $x($x(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};jl.className="LSTMCell";ae.registerClass(jl);var ly=class extends Ar{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new jl(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};ly.className="LSTM";ae.registerClass(ly);var Vp=class extends ql{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){ym(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ea(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return vm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Pb(t)}};Vp.className="StackedRNNCells";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):XI(t(),n),o=()=>Ul(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var PS=class extends Ar{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};PS.className="ConvRNN2D";var Wp=class extends jl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Xi(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Xi(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Un(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=Xi(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return $b([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Qn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Qn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ca(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ca(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Wp.className="ConvLSTM2DCell";ae.registerClass(Wp);var cy=class extends PS{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};cy.className="ConvLSTM2D";ae.registerClass(cy);var Up=class extends Ge{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Ul(()=>XI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className="Dropout";ae.registerClass(Up);var dy=class extends Up{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};dy.className="SpatialDropout1D";ae.registerClass(dy);var py=class extends Ge{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=UI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};py.className="Dense";ae.registerClass(py);var hy=class extends Ge{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return rL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};hy.className="Flatten";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};fy.className="Activation";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),nL(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};my.className="RepeatVector";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};gy.className="Reshape";ae.registerClass(gy);var by=class extends Ge{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};by.className="Permute";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return pm(Xu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=pm(Xu(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};yy.className="Masking";ae.registerClass(yy);var vy=class extends Ge{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Xu(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Sp(n,"int32"));let s=KI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};vy.className="Embedding";ae.registerClass(vy);var bi=class extends Ge{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Vl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Pn(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},xy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};xy.className="Add";ae.registerClass(xy);var wy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};wy.className="Multiply";ae.registerClass(wy);var ky=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};ky.className="Average";ae.registerClass(ky);var Iy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$r(t,e[n]);return t})}};Iy.className="Maximum";ae.registerClass(Iy);var Sy=class extends bi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Sy.className="Minimum";ae.registerClass(Sy);var Cy=class extends bi{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>$b(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Qn(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Pn(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return qk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Concatenate";ae.registerClass(Cy);function Cu(e,t){for(;e<0;)e+=t;return e}function VB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var Ny=class extends bi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Cu(r,e[a].shape.length)):s=[Cu(this.axes,t.shape.length),Cu(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),VB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cu(this.axes,e.length),Cu(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Ny.className="Dot";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Ul(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ty.className="GaussianNoise";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Ul(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};$y.className="GaussianDropout";ae.registerClass($y);var _y=class extends Ge{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ul(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Ko(Ml(n),this.rate);u=Sp(u,"float32");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};_y.className="AlphaDropout";ae.registerClass(_y);function Ju(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=$E(e,t,n,s,r,a);else if(e.rank===3)i=AE(e,t,n,s,r,a);else if(e.rank===4)i=RE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function WB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance;return[Ju(e,i,o,n,t,r),i,o]})}function UB(e,t,n,s,r=.001){return j(()=>{let a=tb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Ju(e,l,c,d,p,r),i,o]})}function GB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?WB(e,t,n,s,r):UB(e,t,n,s,r)}var Ay=class extends Ge{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.movingMeanInitializer=ht(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=fa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Ju(s,b,y,v,x,this.epsilon)}else return Ju(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=GB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="BatchNormalization";ae.registerClass(Ay);var Ey=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=tb(n,this.axis,!0),u=fa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),Ju(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="LayerNormalization";ae.registerClass(Ey);function HB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=bs()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],fi(e,s)})}var Ry=class extends Ge{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>HB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ry.className="ZeroPadding2D";ae.registerClass(Ry);function Gp(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=Zb(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=eb(e,t,n,o):i=Gg(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}function zS(e,t,n,s,r,a){return j(()=>{Ct(r),GI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=$S(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=dI(e,t,n,o):i=Yk(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var MS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Vl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"max")}};Dy.className="MaxPooling1D";ae.registerClass(Dy);var Fy=class extends MS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"avg")}};Fy.className="AveragePooling1D";ae.registerClass(Fy);var LS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"max")}};Oy.className="MaxPooling2D";ae.registerClass(Oy);var Py=class extends LS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),Gp(e,t,n,s,r,"avg")}};Py.className="AveragePooling2D";ae.registerClass(Py);var BS=class extends Ge{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,"max")}};zy.className="MaxPooling3D";ae.registerClass(zy);var My=class extends BS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Un(s),zS(e,t,n,s,r,"avg")}};My.className="AveragePooling3D";ae.registerClass(My);var VS=class extends Ge{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Ly=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Ly.className="GlobalAveragePooling1D";ae.registerClass(Ly);var By=class extends VS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};By.className="GlobalMaxPooling1D";ae.registerClass(By);var WS=class extends Ge{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Vy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};Vy.className="GlobalAveragePooling2D";ae.registerClass(Vy);var Wy=class extends WS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};Wy.className="GlobalMaxPooling2D";ae.registerClass(Wy);var US=class extends Ge{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=ms(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Uy=class extends US{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Oe(e),FS((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Uy.className="TimeDistributed";ae.registerClass(Uy);function qB(e){mi(QM,"BidirectionalMergeMode",e)}var jB="concat",Gy=class extends US{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ms(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ms(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?jB:e.mergeMode,qB(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=DS(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(s!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let o=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Zn(r,1));let 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return ta(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(bn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=bn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=gz(e),i=this.computeOutputShape(a),o,u=bz(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. 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The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new hs(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Pp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Db(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${hS}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=Im(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Zr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Eu(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=ma(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(bn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return bn(r)}runInternalGraph(e,t){t==null&&(t=ma(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],T=y[v];n[x.id]=[k,T]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],T=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let N=r[x];if(N.inboundNodes.length<=k){i(m,g);return}let E=N.inboundNodes[k];b.push(E.outputTensors[T])}b.length>0&&m.apply(bn(b),y)}function u(m){let g=m.name,b=ms(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!UP(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function NB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function fS(e,t){return NB(e,t,"classWeight")}async function mS(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return cr(e);if(e.shape.length===2){if(e.shape[1]>1)return qu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Zt(i,"float32")}else return null}function TB(e,t){return V(e,t)}var $B=32;function gS(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=zx("input",e.inputNames,n),i=zx("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function zx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function _B(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function AB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),w.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),w.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,i;if(r)if(Mx(n.validationData))w.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=_B(n.validationData);a=g.xs,i=g.ys}let o=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(g=>"val_"+g)):l=u.slice();let c=aS(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=iS(c,p,n.epochs,null,null,EB(t,n),null,r,l);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let b=0,y=0;for(s||(m=await t.iterator());!s||b<n.batchesPerEpoch;){let v=await m.next();if(s&&v.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). 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this.userDefinedMetadata!=null&&(Px(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Px(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};hr.className="Model";ae.registerClass(hr);var yS=class extends hr{};yS.className="Functional";ae.registerClass(yS);async function VB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Ju(n),r=ms(s,t);if(e.weightsManifest!=null){let a=await En.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function WB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=En.getLoadHandlers(e,t);if(n.length===0)n.push(En.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return UB(e,void 0,t)}async function UB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=ms(Ju(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. 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this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Tm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=ms(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return 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t={};return t.className="linear",t.config={},Hf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Hf(t)}else return e instanceof In?e:Hf(e)}function jb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var RS=class extends ae.Serializable{},Gl=class extends RS{constructor(e){super(),jb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ve(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ve(V(this.l2,Vl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Gl.className="L1L2";ae.registerClass(Gl);function qB(e){return jb(e),new Gl({l1:e!=null?e.l1:null,l2:0})}function jB(e){return jb(e),new Gl({l2:e!=null?e.l2:null,l1:0})}var Wx={l1l2:"L1L2"};function it(e){return Ib(e)}function 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Zb.className="ThresholdedReLU";ae.registerClass(Zb);var Jb=class extends He{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new qb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Jb.className="Softmax";ae.registerClass(Jb);function Qi(e,t,n){if(typeof e=="number")return ma(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!ez(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function gs(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+br([n-t,0]);else if(s==="same")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function ey(e,t){return j(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,1]):e))}function DS(e,t){return j(()=>(Ct(t),t==="channelsFirst"?Ge(e,[0,2,3,4,1]):e))}function KB(e,t,n,s=1,r="valid",a,i=1){return j(()=>{if(a==null&&(a=ys()),Ct(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Ge(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=nI(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ws(o,n)),o})}function Gx(e,t,n,s=[1,1],r="valid",a,i,o=null){return j(()=>{if(a==null&&(a=ys()),Ct(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=ey(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=fa.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=Ge(u,[0,3,1,2])),u})}function XB(e,t,n,s=[1,1,1],r="valid",a,i){return j(()=>{if(a==null&&(a=ys()),Ct(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=DS(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=rI(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ws(o,n)),a==="channelsFirst"&&(o=Ge(o,[0,4,1,2,3])),o})}var ty=class extends He{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",ty.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Qi(t.kernelSize,e,"kernelSize"),this.strides=Qi(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Gn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ct(this.dataFormat),this.activation=vr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Qi(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Sb(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:yr(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Hl=class extends ty{constructor(e,t){super(e,t),this.kernel=null,Hl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=qI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Gx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=KB(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Gx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=XB(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=gs(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:it(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},FS=class extends Hl{constructor(e){super(2,e),FS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Sb(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Lp=FS;Lp.className="Conv2D";ae.registerClass(Lp);var OS=class extends Hl{constructor(e){super(3,e),OS.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Bp=OS;Bp.className="Conv3D";ae.registerClass(Bp);var ny=class extends Lp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,1]));let g=sI(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Ge(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ny.className="Conv2DTranspose";ae.registerClass(ny);var sy=class extends Bp{constructor(e){if(super(e),this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=Ge(n,[0,2,3,4,1]));let k=oR(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=Ge(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};sy.className="Conv3DTranspose";ae.registerClass(sy);var PS=class extends Hl{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Ge(e,[0,2,3,1])),n=m3(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Ge(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.pointwiseRegularizer=it(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};PS.className="SeparableConv";var ry=class extends PS{constructor(e){super(2,e)}};ry.className="SeparableConv2D";ae.registerClass(ry);var zS=class extends Hl{constructor(e){super(1,e),zS.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Sb(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ay=zS;ay.className="Conv1D";ae.registerClass(ay);var iy=class extends He{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Hc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Hc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Hc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Hc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};iy.className="Cropping2D";ae.registerClass(iy);var oy=class extends He{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,QP(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Ge(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a]);return Ge(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?jn.resizeNearestNeighbor(n,[r,a]):jn.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};oy.className="UpSampling2D";ae.registerClass(oy);function YB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=ys()),Ct(r);let i=ey(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=mp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=Ge(i,[0,3,1,2])),i})}var uy=class extends ty{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=YB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=gs(t,this.kernelSize[0],this.padding,this.strides[0]),a=gs(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=it(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};uy.className="DepthwiseConv2D";ae.registerClass(uy);function MS(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function LS(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(bs(2,u));if(t=Ge(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===u-1&&(r=Pn(r,-1)),r=Ge(r,l)),s&&(t=Jn(t,0),r!=null&&(r=Jn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],T=ge(Zn(k),k),N=ie(V(v[0],k),V(d[0],T)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,T)));return{output:N,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=es(c,1)),[p,g,d]})}var BS=class extends He{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Up({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return bs(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){xm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");xm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=MS(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=LS((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ve(t,[1,2]),t=Bl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?ym(t,[1,n]):t):this.cell.stateSize>1?[ym(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===BS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=ms(s,n);return new e(Object.assign(t,{cell:r}))}},Er=BS;Er.className="RNN";ae.registerClass(Er);var ql=class extends He{},Vp=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Vp.className="SimpleRNNCell";ae.registerClass(Vp);var ly=class extends Er{constructor(e){e.cell=new Vp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};ly.className="SimpleRNN";ae.registerClass(ly);var Wp=class extends ql{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Bn(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Bn(l,3,l.rank-1),[b,y]=Bn(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Wp.className="GRUCell";ae.registerClass(Wp);var cy=class extends Er{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Wp(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};cy.className="GRU";ae.registerClass(cy);var jl=class extends ql{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=vr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=eo([1,br([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=eo([1,br([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends ns{apply(i,o){let u=r.apply([a]),l=new _p().apply([a]),c=r.apply([a*2]);return kx(kx(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Bn(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:yr(this.activation),recurrentActivation:yr(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:it(this.kernelRegularizer),recurrentRegularizer:it(this.recurrentRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};jl.className="LSTMCell";ae.registerClass(jl);var dy=class extends Er{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new jl(e),super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};dy.className="LSTM";ae.registerClass(dy);var Up=class extends ql{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){xm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{ta(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(ms(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return wm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Db(t)}};Up.className="StackedRNNCells";ae.registerClass(Up);function xr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):JI(t(),n),o=()=>Wl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var VS=class extends Er{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=gs(u,s[0],r,a[0],i[0]),p=gs(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};VS.className="ConvRNN2D";var Gp=class extends jl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t}),this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Qi(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Qi(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Gn(this.padding),this.dataFormat=a||"channelsLast",Ct(this.dataFormat),this.dilationRate=Qi(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends ns{apply(c,p){let d=u.apply([l]),h=Mn([l]),f=u.apply([l*2]);return Cb([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=xr({ones:()=>Zn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=xr({ones:()=>Zn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,T]=Bn(this.kernel.read(),i,y),[N,E,A,P]=this.useBias?Bn(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,N,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,T,P,this.padding);let[R,F,$,z]=Bn(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=da(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return da(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Gp.className="ConvLSTM2DCell";ae.registerClass(Gp);var py=class extends VS{constructor(e){let t=new Gp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};py.className="ConvLSTM2D";ae.registerClass(py);var Hp=class extends He{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wl(()=>JI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Hp.className="Dropout";ae.registerClass(Hp);var hy=class extends Hp{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};hy.className="SpatialDropout1D";ae.registerClass(hy);var fy=class extends He{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=vr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=qI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:yr(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:it(this.kernelRegularizer),biasRegularizer:it(this.biasRegularizer),activityRegularizer:it(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};fy.className="Dense";ae.registerClass(fy);var my=class extends He{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],pr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Ge(n,s)}return sz(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};my.className="Flatten";ae.registerClass(my);var gy=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.activation=vr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:yr(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};gy.className="Activation";ae.registerClass(gy);var by=class extends He{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),tz(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};by.className="RepeatVector";ae.registerClass(by);var yy=class extends He{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=pr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};yy.className="Reshape";ae.registerClass(yy);var vy=class extends He{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=bs(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return 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t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Tp(n,"int32"));let s=ZI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:it(this.embeddingsRegularizer),activityRegularizer:it(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};wy.className="Embedding";ae.registerClass(wy);var xi=class extends He{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. 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e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Ty.className="Concatenate";ae.registerClass(Ty);function Nu(e,t){for(;e<0;)e+=t;return e}function QB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ve(V(e,t),a[0]):o=ve(V(Ge(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=gr(o,l)}return o.shape.length===1&&(o=Pn(o,1)),o})}var $y=class extends xi{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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He{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Wl(()=>ie($p(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};_y.className="GaussianNoise";ae.registerClass(_y);var Ay=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Wl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,$p(n.shape,1,r))},()=>n,t.training||!1):n})}};Ay.className="GaussianDropout";ae.registerClass(Ay);var Ey=class extends He{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=bs(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=ma(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,bs(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return el(s,b,y,v,x,this.epsilon)}else return el(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=eV(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),T=V(ge(k,y),x);b.write(ge(k,T))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ry.className="BatchNormalization";ae.registerClass(Ry);var Dy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==dr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=sb(n,this.axis,!0),u=ma(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=ps(i,d),o=ps(o,d),c=ps(c,h),p=ps(p,h),el(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:it(this.betaRegularizer),gammaRegularizer:it(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Dy.className="LayerNormalization";ae.registerClass(Dy);function tV(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=ys()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],bi(e,s)})}var Fy=class extends He{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?ys():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>tV(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Fy.className="ZeroPadding2D";ae.registerClass(Fy);function qp(e,t,n,s,r,a){return j(()=>{Ct(r),KI(a),Gn(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=ys()),a==null&&(a="max"),e=ey(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=nb(e,t,n,o):i=qg(e,t,n,o),r==="channelsFirst"&&(i=Ge(i,[0,3,1,2])),i})}function WS(e,t,n,s,r,a){return j(()=>{Ct(r),KI(a),Gn(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=ys()),a==null&&(a="max"),e=DS(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=mI(e,t,n,o):i=eI(e,t,n,o),r==="channelsFirst"&&(i=Ge(i,[0,4,1,2,3])),i})}var US=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Gn(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=gs(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Bl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return gr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends US{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,"max")}};Oy.className="MaxPooling1D";ae.registerClass(Oy);var Py=class extends US{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,"avg")}};Py.className="AveragePooling1D";ae.registerClass(Py);var GS=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zy=class extends GS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,"max")}};zy.className="MaxPooling2D";ae.registerClass(zy);var My=class extends GS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),qp(e,t,n,s,r,"avg")}};My.className="AveragePooling2D";ae.registerClass(My);var HS=class extends He{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),Gn(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=gs(t,this.poolSize[0],this.padding,this.strides[0]),n=gs(n,this.poolSize[1],this.padding,this.strides[1]),s=gs(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ly=class extends HS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),WS(e,t,n,s,r,"max")}};Ly.className="MaxPooling3D";ae.registerClass(Ly);var By=class extends HS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ct(r),Gn(s),WS(e,t,n,s,r,"avg")}};By.className="AveragePooling3D";ae.registerClass(By);var qS=class extends He{constructor(e){super(e),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},Vy=class extends qS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return St(n,1)})}};Vy.className="GlobalAveragePooling1D";ae.registerClass(Vy);var Wy=class extends qS{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};Wy.className="GlobalMaxPooling1D";ae.registerClass(Wy);var jS=class extends He{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ct(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Uy=class extends jS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?St(n,[1,2]):St(n,[2,3])})}};Uy.className="GlobalAveragePooling2D";ae.registerClass(Uy);var Gy=class extends jS{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};Gy.className="GlobalMaxPooling2D";ae.registerClass(Gy);var KS=class extends He{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=ms(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Hy=class extends KS{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Oe(e),LS((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Hy.className="TimeDistributed";ae.registerClass(Hy);function nV(e){yi(YP,"BidirectionalMergeMode",e)}var sV="concat",qy=class extends KS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=ms(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=ms(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?sV:e.mergeMode,nV(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):bn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=MS(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new Dt({shape:c.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(s!=null)throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");let o=a[0]instanceof $s;for(let u of a)if(u instanceof $s!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Jn(r,1));let i;return this.mergeMode==="concat"?i=Cb([s,r]):this.mergeMode==="sum"?i=ie(s,r):this.mergeMode==="ave"?i=V(.5,ie(s,r)):this.mergeMode==="mul"?i=V(s,r):this.mergeMode==null&&(i=[s,r]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){ta(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),ta(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=ms(t.layer);if(delete t.layer,t.numConstants!=null)throw new Fe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};qy.className="Bidirectional";ae.registerClass(qy);function rV(e){return new Zo(e)}function aV(e){return new Qb(e)}function iV(e){return new Kb(e)}function oV(e){return new Xb(e)}function uV(e){return new Yb(e)}function lV(e){return new Jb(e)}function cV(e){return new Zb(e)}function dV(e){return new ay(e)}function pV(e){return new Lp(e)}function hV(e){return new ny(e)}function 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TypeError(`Node type ${e.op} is not implemented`)}};function qn(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){w.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];w.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function Xx(e){return!(typeof e=="number"||e.some(t=>t<0))}function Tu(e,t,n){let s=Mm(e,n),r=!Xx(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=Mm(a.shape,s)}),!Xx(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function Mm(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var l4=class{constructor(e,t,n,s,r,a,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=we(0),Ht(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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+        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];j(()=>{t=G(t,[1,n,r]);for(let o=0;o<e.length;++o){let u=o===0?0:s[o-1],l=[0,u,0],c=[1,e[o],r];a[o]=G(qe(t,l,c),this.elementShape)}return a});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,a)}},no=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);qn(t,r.shape,"TensorList shape mismatch: "),Ht(r)}),this.idTensor=we(0),this.maxNumElements=s,Ht(this.idTensor)}get id(){return this.idTensor.id}copy(){return new no([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);qn(e,this.elementShape,"TensorList shape mismatch: ");let s=Tu(this.elementShape,this.tensors,e);return j(()=>{let r=this.tensors.map(a=>G(a,s));return es(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Tu(this.elementShape,this.tensors,e),s=this.tensors.pop();return qn(s.shape,e,"TensorList shape mismatch: "),G(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(qn(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Ht(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. 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s=I("size",e,t,n),r=I("dtype",e,t,n),a=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),u=I("identicalElementShapes",e,t,n),l=I("name",e,t,n),c=new JW(l,r,s,a,u,i,o);return n.addTensorArray(c),[c.idTensor,we(1)]}case"TensorArrayWriteV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case"TensorArrayReadV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("tensor",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case"TensorArrayConcatV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=I("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let 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s=I("elementShape",e,t,n),r=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let i=I(a,e,t,n),o=t4(s,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let s=I("tensorListId",e,t,n),r=I("indices",e,t,n),a=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case"TensorListStack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case"TensorListFromTensor":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),i=e4(s,r,a);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id),a=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(a,i)]}case"TensorListPushBack":{let s=I("tensorListId",e,t,n),r=I("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case"TensorListSplit":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("lengths",e,t,n),i=s4(s,a,r);return n.addTensorList(i),[i.idTensor]}case"TensorListLength":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),"int32")]}case"TensorListResize":{let s=I("tensorListId",e,t,n),r=I("size",e,t,n),i=n.getTensorList(s.id).resize(r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Xx(e,t,n){let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",i=!a,o=r==="prelu",u=s==="fusedbatchnorm",l=I("numArgs",e,t,n);if(a){if(o&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");let c=I("strides",e,t,n),p=sd(e,t,n),d=I("dataFormat",e,t,n).toUpperCase(),h=I("dilations",e,t,n),[f,m]=I("args",e,t,n);i&&(m=f,f=void 0);let g=I("leakyreluAlpha",e,t,n);return{stride:c,pad:p,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var a4=(e,t,n)=>{switch(e.op){case"Conv1D":{let s=I("stride",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[Zk(I("x",e,t,n),I("filter",e,t,n),s,r,a,i)]}case"Conv2D":{let s=I("strides",e,t,n),r=sd(e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[ca(I("x",e,t,n),I("filter",e,t,n),[s[1],s[2]],r,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Xx(e,t,n);return[ha.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Xx(e,t,n);return[ha.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let s=I("outputShape",e,t,n),r=I("strides",e,t,n),a=sd(e,t,n);return[Jk(I("x",e,t,n),I("filter",e,t,n),s,[r[1],r[2]],a)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let s=I("strides",e,t,n),r=sd(e,t,n),a=I("dilations",e,t,n),i=I("dataFormat",e,t,n).toUpperCase();return[hp(I("input",e,t,n),I("filter",e,t,n),[s[1],s[2]],r,i,[a[1],a[2]])]}case"Conv3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[eI(I("x",e,t,n),I("filter",e,t,n),[s[1],s[2],s[3]],r,a,[i[1],i[2],i[3]])]}case"AvgPool":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[Gg(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPool":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[eb(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case"MaxPoolWithArgmax":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:u}=fD(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r,i);return[o,u]}case"AvgPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[Yk(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"MaxPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[dI(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"Dilation2D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("dilations",e,t,n),i=s[1],o=s[2],u=a[1],l=a[2];return[fR(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[u,l],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},i4=(e,t,n)=>{switch(e.op){case"Fill":{let s=I("shape",e,t,n),r=I("dtype",e,t,n),a=I("value",e,t,n);return[Pl(s,a,r)]}case"LinSpace":{let s=I("start",e,t,n),r=I("stop",e,t,n),a=I("num",e,t,n);return[GR(s,r,a)]}case"Multinomial":{let s=I("logits",e,t,n),r=I("numSamples",e,t,n),a=I("seed",e,t,n);return[TD(s,r,a)]}case"OneHot":{let s=I("indices",e,t,n),r=I("depth",e,t,n),a=I("onValue",e,t,n),i=I("offValue",e,t,n);return[vd(s,r,a,i)]}case"Ones":return[Mn(I("shape",e,t,n),I("dtype",e,t,n))];case"OnesLike":return[Qn(I("x",e,t,n))];case"RandomUniform":return[Ml(I("shape",e,t,n),I("minval",e,t,n),I("maxval",e,t,n),I("dtype",e,t,n))];case"Range":{let s=I("start",e,t,n),r=I("stop",e,t,n),a=I("step",e,t,n);return[Yu(s,r,a,I("dtype",e,t,n))]}case"TruncatedNormal":{let s=I("shape",e,t,n),r=I("mean",e,t,n),a=I("stdDev",e,t,n),i=I("seed",e,t,n);return[db(s,r,a,I("dtype",e,t,n),i)]}case"Zeros":return[$t(I("shape",e,t,n),I("dtype",e,t,n))];case"ZerosLike":return[je(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Hf(e,t,n){let s=I("boxes",e,t,n),r=I("scores",e,t,n),a=I("maxOutputSize",e,t,n),i=I("iouThreshold",e,t,n),o=I("scoreThreshold",e,t,n),u=I("softNmsSigma",e,t,n);return{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}}var o4=async(e,t,n)=>{switch(e.op){case"NonMaxSuppressionV5":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}=Hf(e,t,n),l=await qn.nonMaxSuppressionWithScoreAsync(s,r,a,i,o,u);return[l.selectedIndices,l.selectedScores]}case"NonMaxSuppressionV4":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o}=Hf(e,t,n),u=I("padToMaxOutputSize",e,t,n),l=await qn.nonMaxSuppressionPaddedAsync(s,r,a,i,o,u);return[l.selectedIndices,l.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o}=Hf(e,t,n);return[await qn.nonMaxSuppressionAsync(s,r,a,i,o)]}case"Where":{let s=ce(I("condition",e,t,n),"bool"),r=[await II(s)];return 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I("x",e,t,n).map(l=>Zt(l.shape));case"Size":return[we(I("x",e,t,n).size,"int32")];case"Rank":return[we(I("x",e,t,n).rank,"int32")];case"NoOp":return[we(1)];case"Print":let a=I("x",e,t,n),i=I("data",e,t,n),o=I("message",e,t,n),u=I("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let l=0;l<i.length;l++)console.log(Array.prototype.slice.call(i[l].dataSync()).slice(0,u));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},c4=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=we(0),this.tensorMap=new Map,Ht(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return we(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return 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s=I("image",e,t,n),r=I("boxes",e,t,n),a=I("boxInd",e,t,n),i=I("cropSize",e,t,n),o=I("method",e,t,n),u=I("extrapolationValue",e,t,n);return[qn.cropAndResize(s,r,a,i,o,u)]}case"ImageProjectiveTransformV3":{let s=I("images",e,t,n),r=I("transforms",e,t,n),a=I("outputShape",e,t,n),i=I("fillValue",e,t,n),o=I("interpolation",e,t,n),u=I("fillMode",e,t,n);return[qn.transform(s,r,o.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${e.op} is not 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i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[fm(I("x",e,t,n),i,o)]}case"Sum":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[ve(I("x",e,t,n),i,o)]}case"All":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[qk(I("x",e,t,n),i,o)]}case"Any":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[pm(I("x",e,t,n),i,o)]}case"ArgMax":{let i=I("axis",e,t,n);return[Hu(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[JA(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),o=I("keepDims",e,t,n);return[pI(I("x",e,t,n),i,o)]}case"Cumprod":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),u=I("reverse",e,t,n);return[aR(I("x",e,t,n),i,o,u)]}case"Cumsum":{let i=I("axis",e,t,n),o=I("exclusive",e,t,n),u=I("reverse",e,t,n);return[sI(I("x",e,t,n),i,o,u)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[Qk(s,r,a)];case"DenseBincount":{let i=I("x",e,t,n),o=I("weights",e,t,n),u=I("size",e,t,n),l=I("binaryOutput",e,t,n);return[uR(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},b4=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),r=I("axis",e,t,n),a=I("tensors",e,t,n);return a=a.slice(0,s),[Ft(a,r)]}case"Gather":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Ku(s,ce(r,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),r=I("batchDims",e,t,n),a=I("x",e,t,n),i=I("indices",e,t,n);return[Ku(a,ce(i,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let i=0;i<s.length;i++)s[i]&&r.push(i);let a=I("x",e,t,n);return[Zn(a,r)]}case"ReverseV2":{let s=I("axis",e,t,n),r=I("x",e,t,n);return[Zn(r,s)]}case"Slice":{let s=I("begin",e,t,n),r=I("size",e,t,n);return[He(I("x",e,t,n),s,r)]}case"StridedSlice":{let s=I("begin",e,t,n),r=I("end",e,t,n),a=I("strides",e,t,n),i=I("beginMask",e,t,n),o=I("endMask",e,t,n),u=I("ellipsisMask",e,t,n),l=I("newAxisMask",e,t,n),c=I("shrinkAxisMask",e,t,n),p=I("x",e,t,n);return[D3(p,s,r,a,i,o,u,l,c)]}case"Pack":return j(()=>{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error("the input tensors shape does not match");return l?u:G(u,a)});return[Jn(o,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Fs(r,s)}case"Tile":{let s=I("reps",e,t,n);return[ps(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return Bn(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[X3(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[J3(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[NI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},y4=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Wc.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,i]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Wc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Wc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Wc.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},v4=(e,t,n)=>{switch(e.op){case"FFT":return[lb(I("x",e,t,n))];case"IFFT":return[Id(I("x",e,t,n))];case"RFFT":return[cb(I("x",e,t,n))];case"IRFFT":return[xI(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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s=I("axis",e,t,n);return[mr(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[xD(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[fi(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[nb(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Hg(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[cR(I("x",e,t,n),s,r)]}case"BroadcastTo":return[nd(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[OE(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Yx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case"arithmetic":return j(()=>QW(a,i,o));case"basic_math":return j(()=>ZW(a,i,o));case"control":return r4(a,i,o);case"convolution":return j(()=>a4(a,i,o));case"creation":return j(()=>i4(a,i,o));case"dynamic":return o4(a,i,o);case"evaluation":return j(()=>u4(a,i,o));case"image":return j(()=>p4(a,i,o));case"graph":return j(()=>l4(a,i,o));case"logical":return j(()=>h4(a,i,o));case"matrices":return j(()=>f4(a,i,o));case"normalization":return j(()=>m4(a,i,o));case"reduction":return j(()=>g4(a,i,o));case"slice_join":return j(()=>b4(a,i,o));case"sparse":return j(()=>y4(a,i,o));case"spectral":return j(()=>v4(a,i,o));case"string":return j(()=>x4(a,i,o));case"transformation":return j(()=>w4(a,i,o));case"hash_table":return d4(a,i,o,s);case"custom":let u=XS(a.op);if(u&&u.customExecutor)return u.customExecutor(new YW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Zx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((b0(d)||N4(d)||T4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function k4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var I4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],S4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],C4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function b0(e){return I4.indexOf(e.op)>=0}function N4(e){return S4.indexOf(e.op)>=0}function T4(e){return C4.indexOf(e.op)>=0}var zm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new zm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Zx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Re(e.value)}}},Y4=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Q4=class extends Ut{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Jx=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Qy=class extends Ut{constructor(){super(),this.outputQueue=new I0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},Z4=class extends Qy{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return!0}},C0=class extends Ut{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},N0=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(N0||{}),J4=class extends Ut{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await x0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},T0=class extends Ut{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new w0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},eU=class extends T0{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=F4.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Jo=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),_n(async()=>(await n.iterator()).columnMajorBatch(e,t,sU),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,_n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,_n(async()=>(await t.iterator()).filter(s=>j(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return _n(async()=>(await t.iterator()).map(n=>j(()=>e(n))),this.size)}mapAsync(e){let t=this;return _n(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return _n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,_n(async()=>{let s=Yy(async()=>({value:await t.iterator(),done:!1}));return V4(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,_n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=D4.alea(t||w.now().toString());return _n(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,_n(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Jo.MAX_BUFFER_SIZE=1e4;function _n(e,t=null){return new class extends Jo{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function tU(e){return _n(async()=>S0(e),e.length)}function nU(e){if(!to(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return _n(async()=>{let n=await x0(e,s=>{if(s instanceof Jo)return{value:s.iterator(),recurse:!1};if(to(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return W4(n,1)},t)}function sU(e){if(e===null)return null;let t=e[0];return z4(t)?{value:rU(e),recurse:!1}:{value:null,recurse:!0}}function rU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?Jn(e):fs(e)}var $0=class extends Jo{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Kc='"',Tu=Symbol("out"),ew=Symbol("field"),Xc=Symbol("quote"),qf=Symbol("quoteafterquote"),tw=Symbol("quoteinquote"),_0=class extends Jo{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new $0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Tu;for(let i=0;i<r;i++)switch(a){case Tu:switch(e.charAt(i)){case Kc:s=i+1,a=Xc;break;case this.delimiter:if(s=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Tu;break;default:a=ew,s=i;break}break;case ew:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i)),a=Tu,s=i+1;break;default:}break;case Xc:switch(e.charAt(i)){case Kc:a=qf;break;default:}break;case qf:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i-1)),a=Tu,s=i+1;break;case Kc:a=Xc;break;default:a=tw;break}break;case tw:switch(e.charAt(i)){case Kc:a=Xc;break;default:}break;default:}if(a===qf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},A0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!X().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new A0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},E0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!X().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new E0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Nk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=Pn(ce(e,"float32"),0),n;n=qn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},R0=class{},D0=class extends Ut{split(e){return new aU(this,e)}},aU=class extends D0{constructor(e,t){super(),this.upstream=e,this.impl=new iU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},iU=class extends Qy{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},oU=class extends Ut{decodeUTF8(){return new uU(this)}},uU=class extends D0{constructor(e){super(),this.upstream=e,this.impl=new lU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},lU=class extends Qy{constructor(e){if(super(),this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Ww();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},F0=class extends oU{constructor(e,t={}){super(),this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=i=>{let o=r.result;if(o instanceof 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s=I("elementShape",e,t,n),r=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let i=I(a,e,t,n),o=d4(s,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let s=I("tensorListId",e,t,n),r=I("indices",e,t,n),a=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case"TensorListStack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case"TensorListFromTensor":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),i=c4(s,r,a);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id),a=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(a,i)]}case"TensorListPushBack":{let s=I("tensorListId",e,t,n),r=I("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case"TensorListSplit":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("lengths",e,t,n),i=h4(s,a,r);return n.addTensorList(i),[i.idTensor]}case"TensorListLength":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id);return[we(r.size(),"int32")]}case"TensorListResize":{let s=I("tensorListId",e,t,n),r=I("size",e,t,n),i=n.getTensorList(s.id).resize(r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Yx(e,t,n){let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",i=!a,o=r==="prelu",u=s==="fusedbatchnorm",l=I("numArgs",e,t,n);if(a){if(o&&l!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&a&&l!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d 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s=I("strides",e,t,n),r=sd(e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),i=I("dilations",e,t,n);return[da(I("x",e,t,n),I("filter",e,t,n),[s[1],s[2]],r,a,[i[1],i[2]])]}case"_FusedConv2D":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Yx(e,t,n);return[fa.conv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case"FusedDepthwiseConv2dNative":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=Yx(e,t,n);return[fa.depthwiseConv2d({x:I("x",e,t,n),filter:I("filter",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let 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s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n),i=I("includeBatchInIndex",e,t,n),{result:o,indexes:u}=vD(I("x",e,t,n),[a[1],a[2]],[s[1],s[2]],r,i);return[o,u]}case"AvgPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[eI(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"MaxPool3D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("kernelSize",e,t,n);return[mI(I("x",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case"Dilation2D":{let s=I("strides",e,t,n),r=I("pad",e,t,n),a=I("dilations",e,t,n),i=s[1],o=s[2],u=a[1],l=a[2];return[vR(I("x",e,t,n),I("filter",e,t,n),[i,o],r,[u,l],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},g4=(e,t,n)=>{switch(e.op){case"Fill":{let s=I("shape",e,t,n),r=I("dtype",e,t,n),a=I("value",e,t,n);return[Pl(s,a,r)]}case"LinSpace":{let s=I("start",e,t,n),r=I("stop",e,t,n),a=I("num",e,t,n);return[XR(s,r,a)]}case"Multinomial":{let 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s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,i=gr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(gr(u).shape,i))throw new Error("the input tensors shape does not match");return l?u:G(u,a)});return[es(o,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Fs(r,s)}case"Tile":{let s=I("reps",e,t,n);return[ps(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return Bn(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[eF(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[rF(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[AI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not 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j(()=>g4(a,i,o));case"dynamic":return b4(a,i,o);case"evaluation":return j(()=>y4(a,i,o));case"image":return j(()=>k4(a,i,o));case"graph":return j(()=>v4(a,i,o));case"logical":return j(()=>I4(a,i,o));case"matrices":return j(()=>S4(a,i,o));case"normalization":return j(()=>C4(a,i,o));case"reduction":return j(()=>N4(a,i,o));case"slice_join":return j(()=>T4(a,i,o));case"sparse":return j(()=>$4(a,i,o));case"spectral":return j(()=>_4(a,i,o));case"string":return j(()=>A4(a,i,o));case"transformation":return j(()=>E4(a,i,o));case"hash_table":return w4(a,i,o,s);case"custom":let u=e0(a.op);if(u&&u.customExecutor)return u.customExecutor(new i4(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. 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this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Jx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>An(d)[0]),c=[];s!=null&&(c=s.map(d=>An(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((k0(d)||P4(d)||z4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function R4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>An(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var D4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],F4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],O4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function k0(e){return D4.indexOf(e.op)>=0}function P4(e){return F4.indexOf(e.op)>=0}function z4(e){return O4.indexOf(e.op)>=0}var Lm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Lm(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Jx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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Missing the following inputs: [${s}]`)}return R4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[An(c)[0]]),r=t.map(c=>An(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Zx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=An(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=Qx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. 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t=En.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(En.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=En.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Re(e.value)}}},iU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},oU=class extends Ut{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},ew=class extends Ut{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Jy=class extends Ut{constructor(){super(),this.outputQueue=new $0,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},uU=class extends Jy{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return!0}},A0=class extends Ut{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},E0=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(E0||{}),lU=class extends Ut{constructor(e,t=0){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await C0(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},R0=class extends Ut{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new N0(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},cU=class extends R0{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=G4.alea(n||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},eu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),_n(async()=>(await n.iterator()).columnMajorBatch(e,t,hU),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,_n(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,_n(async()=>(await t.iterator()).filter(s=>j(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return _n(async()=>(await t.iterator()).map(n=>j(()=>e(n))),this.size)}mapAsync(e){let t=this;return _n(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return _n(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,_n(async()=>{let s=Zy(async()=>({value:await t.iterator(),done:!1}));return Q4(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,_n(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=U4.alea(t||w.now().toString());return _n(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,_n(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};eu.MAX_BUFFER_SIZE=1e4;function _n(e,t=null){return new class extends eu{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function dU(e){return _n(async()=>_0(e),e.length)}function pU(e){if(!so(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return _n(async()=>{let n=await C0(e,s=>{if(s instanceof eu)return{value:s.iterator(),recurse:!1};if(so(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Z4(n,1)},t)}function hU(e){if(e===null)return null;let t=e[0];return j4(t)?{value:fU(e),recurse:!1}:{value:null,recurse:!0}}function fU(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?es(e):fs(e)}var D0=class extends eu{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Kc='"',$u=Symbol("out"),tw=Symbol("field"),Xc=Symbol("quote"),jf=Symbol("quoteafterquote"),nw=Symbol("quoteinquote"),F0=class extends eu{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new D0(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=$u;for(let i=0;i<r;i++)switch(a){case $u:switch(e.charAt(i)){case Kc:s=i+1,a=Xc;break;case this.delimiter:if(s=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=$u;break;default:a=tw,s=i;break}break;case tw:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i)),a=$u,s=i+1;break;default:}break;case Xc:switch(e.charAt(i)){case Kc:a=jf;break;default:}break;case jf:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i-1)),a=$u,s=i+1;break;case Kc:a=Xc;break;default:a=nw;break}break;case nw:switch(e.charAt(i)){case Kc:a=Xc;break;default:}break;default:}if(a===jf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},O0=class extends Ut{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!X().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new O0(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),fs(n,t)}},P0=class extends Ut{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Zt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Yi([a,r,o,i],[1,4])}else this.cropBox=Yi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!X().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new P0(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ak.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=Pn(ce(e,"float32"),0),n;n=jn.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},z0=class{},M0=class extends Ut{split(e){return new mU(this,e)}},mU=class extends M0{constructor(e,t){super(),this.upstream=e,this.impl=new gU(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},gU=class extends Jy{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},bU=class extends Ut{decodeUTF8(){return new yU(this)}},yU=class extends M0{constructor(e){super(),this.upstream=e,this.impl=new vU(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},vU=class extends Jy{constructor(e){if(super(),this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=qw();this.decoder=new 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c=S.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,T=c.effectiveFilterWidth,N=x-1-c.padInfo.front,E=T-1-c.padInfo.left,A=k-1-c.padInfo.top,P=De(a.shape,"float32"),R=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<c.batchSize;++$)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let K=0;K<c.inWidth;++K){let Y=W-N,Z=q-A,te=K-E,ee=0;for(let se=0;se<x;se+=b){let ne=(Y+se)/p;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let oe=0;oe<k;oe+=y){let re=(Z+oe)/d;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let le=0;le<T;le+=v){let me=(te+le)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=F.get($,ne,re,me,z)}}}P.set(ee*R,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var aH={kernelName:og,backendName:"cpu",kernelFunc:rH};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var lH={kernelName:Ma,backendName:"cpu",kernelFunc:uH};function cH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;be([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ga({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var dH={kernelName:oo,backendName:"cpu",kernelFunc:cH};function pH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=ev(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var 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XH={kernelName:fg,backendName:"cpu",kernelFunc:KH};function YH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,T]=p,N=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let ke=me-se,Se=Math.max(0,Math.ceil(ke/te)),Ee=Math.min(Y,(F+ke)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-ke;for(let at=Je;at<Ye;++at){let 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c=t.slice();return c[c.length-1]=s,[De(c,n,u),De(c,"int32",l)]}function $C(e,t,n,s){let r=w.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let i={},o=new Int32Array(n[r]),u=new Vt(a,s,e),l=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let b=0;b<a[0];b++)for(let y=0;y<a[2];y++)g.push(u.get(b,f,y));m=g.join(",")}if(i[m]!==void 0)o[f]=i[m];else{let g=Object.keys(i).length;i[m]=g,o[f]=g,l.push(f)}}let p=a.slice();p[1]=Object.keys(i).length;let d=new Vt(p,s);l.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let b=0;b<a[2];b++)d.set(u.get(g,f,b),g,m,b)});let h=n.slice();return h[r]=p[1],{outputValues:d.values,outputShape:h,indices:o}}var Xpe="0.0.0";fp("cpu",()=>new G0,1);var _C=st(Pa,e=>e>=0?e:Math.exp(e)-1),$G={kernelName:Pa,backendName:"cpu",kernelFunc:_C};function AC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;be([r],"leakyRelu");let 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OG={kernelName:Na,backendName:"cpu",kernelFunc:FC};function PG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=FC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=Kl({inputs:{a:d,b:i},backend:n}),m.push(d),d=h),c&&(f=lv(n,d,c,o,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var zG={kernelName:aa,backendName:"cpu",kernelFunc:PG},MG=st(rl,e=>Math.acos(e)),LG={kernelName:rl,backendName:"cpu",kernelFunc:MG},BG=st(al,e=>Math.acosh(e)),VG={kernelName:al,backendName:"cpu",kernelFunc:BG};function WG(e){let{inputs:t,backend:n}=e,s=t;be(t,"addN");let r=s.map(o=>n.data.get(o.dataId).values),a=De(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var UG={kernelName:Ia,backendName:"cpu",kernelFunc:WG};function 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c=S.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,T=c.effectiveFilterWidth,N=x-1-c.padInfo.front,E=T-1-c.padInfo.left,A=k-1-c.padInfo.top,P=De(a.shape,"float32"),R=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<c.batchSize;++$)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let K=0;K<c.inWidth;++K){let Y=W-N,Z=q-A,te=K-E,ee=0;for(let se=0;se<x;se+=b){let ne=(Y+se)/p;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let oe=0;oe<k;oe+=y){let re=(Z+oe)/d;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let le=0;le<T;le+=v){let me=(te+le)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=F.get($,ne,re,me,z)}}}P.set(ee*R,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var mH={kernelName:lg,backendName:"cpu",kernelFunc:fH};function gH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;be([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=De(i.shape,"float32"),T=1/(h*f),N=n.data.get(r.dataId).values,E=De(r.shape,"float32",N);for(let A=0;A<c.batchSize;++A)for(let P=0;P<c.inChannels;++P)for(let R=0;R<c.inHeight;++R)for(let F=0;F<c.inWidth;++F){let $=R-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/p;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Y=0;Y<y;Y+=g){let Z=(z+Y)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(A,K,Z,P)}}k.set(W*T,A,R,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var bH={kernelName:ug,backendName:"cpu",kernelFunc:gH};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var vH={kernelName:Ba,backendName:"cpu",kernelFunc:yH};function xH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;be([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var wH={kernelName:lo,backendName:"cpu",kernelFunc:xH};function kH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=nv(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var 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DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"conv2dBackpropFilter");let p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,N=new Vt(r.shape,r.dtype,k),E=new Vt(a.shape,a.dtype,T);for(let A=0;A<m;++A){let P=Math.max(0,Math.ceil((x-A)/h)),R=Math.min(d.outHeight,(d.inHeight+x-A)/h);for(let F=0;F<g;++F){let $=Math.max(0,Math.ceil((v-F)/f)),z=Math.min(d.outWidth,(d.inWidth+v-F)/f);for(let W=0;W<d.inChannels;++W)for(let q=0;q<d.outChannels;++q){let K=0;for(let Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=N.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=N.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var FH={kernelName:pg,backendName:"cpu",kernelFunc:DH};function OH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;be([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=S.convertConv2DDataFormat(l),f=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:T,filterHeight:N,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=N-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let ke=0;ke<T;++ke)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let Pe=Ee-K,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min($,(N+Pe)/W);for(let Ye=0;Ye<R;++Ye){let tt=Ye-Y,Ce=Math.max(0,Math.ceil(tt/q)),ut=Math.min(z,(E+tt)/q),at=0;for(let Nt=Xe;Nt<Je;++Nt){let Cn=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let en=Et*q-tt,Nn=oe*ke+re*Nt+le*Et,Tn=v*(N-1-Cn)+x*(E-1-en)+k*Se;for(let Yt=0;Yt<F;++Yt){let Dn=b[Nn+me*Yt],tn=y[Tn+Yt];at+=Dn*tn}}}let Jt=te*ke+ee*Ee+se*Ye+ne*Se;g[Jt]=at}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var PH={kernelName:Aa,backendName:"cpu",kernelFunc:OH};function zH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;be([r,a],"conv3d");let l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,T=n.data.get(a.dataId).values,N=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let 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l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=S.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,T,N]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),ke=re*k+oe;for(let Se=0;Se<b;++Se){let Ee=Math.max(0,Math.ceil((Z-Se)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Se)/f),Xe=Se*T+ke;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*N+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let at=ut*z,Jt=ut*A;for(let Nt=se;Nt<ne;++Nt){let Et=(ee+Nt*d-Y)*W+at,en=Nt*P+Jt;for(let Nn=le;Nn<me;++Nn){let 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jH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=De([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),T=w.computeStrides(b.shape);for(let N=0;N<f;N++){let E=N*4,A=y[E],P=y[E+1],R=y[E+2],F=y[E+3],$=v[N];if($>=c)continue;let z=m>1?(R-A)*(p-1)/(m-1):0,W=g>1?(F-P)*(d-1)/(g-1):0;for(let q=0;q<m;q++){let K=m>1?A*(p-1)+q*z:.5*(A+R)*(p-1);if(K<0||K>p-1){for(let Y=0;Y<g;Y++)for(let Z=0;Z<h;Z++){let te=Z+Y*T[2]+q*T[1]+N*T[0];b.values[te]=l}continue}if(u==="bilinear"){let Y=Math.floor(K),Z=Math.ceil(K),te=K-Y;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+F)*(d-1);if(se<0||se>d-1){for(let le=0;le<h;le++){let me=le+ee*T[2]+q*T[1]+N*T[0];b.values[me]=l}continue}let ne=Math.floor(se),oe=Math.ceil(se),re=se-ne;for(let le=0;le<h;le++){let me=le+ne*k[2]+Y*k[1]+$*k[0],ke=x[me];me=le+oe*k[2]+Y*k[1]+$*k[0];let Se=x[me];me=le+ne*k[2]+Z*k[1]+$*k[0];let Ee=x[me];me=le+oe*k[2]+Z*k[1]+$*k[0];let Pe=x[me],Xe=ke+(Se-ke)*re,Je=Ee+(Pe-Ee)*re;me=le+ee*T[2]+q*T[1]+N*T[0],b.values[me]=Xe+(Je-Xe)*te}}}else for(let Y=0;Y<g;++Y){let Z=g>1?P*(d-1)+Y*W:.5*(P+F)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let ne=se+Y*T[2]+q*T[1]+N*T[0];b.values[ne]=l}continue}let te=Math.round(Z),ee=Math.round(K);for(let se=0;se<h;se++){let ne=se+te*k[2]+ee*k[1]+$*k[0],oe=se+Y*T[2]+q*T[1]+N*T[0];b.values[oe]=x[ne]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var KH={kernelName:ho,backendName:"cpu",kernelFunc:jH};function XH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumprod");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,"int32"),d=w.makeOnesTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?1:h[v];else{let x=m(b,y-1);d[v]=i?h[x]*d[x]:h[v]*d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var YH={kernelName:po,backendName:"cpu",kernelFunc:XH};function QH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;be(r,"cumsum");let u=S.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=wn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=S.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=cn(l.dtype,"int32"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=S.getUndoAxesPermutation(u),y=wn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var ZH={kernelName:Da,backendName:"cpu",kernelFunc:QH};function JH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=nv(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=j0(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var e6={kernelName:mg,backendName:"cpu",kernelFunc:JH};function t6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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r6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;be([r,a],"depthwiseConv2dNativeBackpropFilter");let p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),T=n.data.get(a.dataId).values,N=new Vt(a.shape,a.dtype,T);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*N.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var a6={kernelName:gg,backendName:"cpu",kernelFunc:r6};function i6(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;be([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,T]=p,N=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let ke=me-se,Se=Math.max(0,Math.ceil(ke/te)),Ee=Math.min(Y,(F+ke)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-ke;for(let at=Je;at<Ye;++at){let 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       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function yi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join("")}function qp(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join("")}function L5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function B5(e,t,n="index"){let s=e.map((a,i)=>i),r=L5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join("")}function hv(e){let t=w.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function wi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join("")}function Kp(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join("")}function X5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Y5(e,t,n="index"){let s=e.map((a,i)=>i),r=X5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join("")}function mv(e){let t=w.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function fv(){return`
+`}function gv(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
-`}var d1=`
+`}var g1=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -153,22 +153,22 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,{getBroadcastDims:p1}=S;function V5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=mv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
-`),a=e.map(h=>W5(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
-`),i=t.texShape,o=fn(),u=H5(o),l,c,p=K5(o);return t.isPacked?(l=U5(t.logicalShape,i,n.enableShapeUniforms),c=j5(o)):(l=G5(t.logicalShape,i,n.enableShapeUniforms),c=q5(o)),n.packedInputs&&(p+=Z5),[p,u,c,r,l,a,n.userCode].join(`
-`)}function su(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return cK(e,t);case 1:return pK(e,t);case 2:return fK(e,t);case 3:return gK(e,t);case 4:return yK(e,t);case 5:return vK(e);case 6:return xK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function h1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return lK(e);case 1:return dK(e,t);case 2:return hK(e,t);case 3:return mK(e,t);default:return bK(e,t)}}function W5(e,t,n=!1,s){let r="";n?r+=h1(e,s):r+=su(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=wK(e,t):r+=kK(e,t)),r}function U5(e,t,n){switch(e.length){case 0:return f1();case 1:return J5(e,t,n);case 2:return oK(e,t,n);case 3:return tK(e,t,n);default:return sK(e,t,n)}}function G5(e,t,n){switch(e.length){case 0:return f1();case 1:return eK(e,t,n);case 2:return uK(e,t,n);case 3:return nK(e,t,n);case 4:return rK(e,t,n);case 5:return aK(e,t);case 6:return iK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function H5(e){return`
+`,{getBroadcastDims:b1}=S;function Q5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=bv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
+`),a=e.map(h=>Z5(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
+`),i=t.texShape,o=fn(),u=tK(o),l,c,p=rK(o);return t.isPacked?(l=J5(t.logicalShape,i,n.enableShapeUniforms),c=sK(o)):(l=eK(t.logicalShape,i,n.enableShapeUniforms),c=nK(o)),n.packedInputs&&(p+=uK),[p,u,c,r,l,a,n.userCode].join(`
+`)}function ru(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return xK(e,t);case 1:return kK(e,t);case 2:return SK(e,t);case 3:return NK(e,t);case 4:return $K(e,t);case 5:return _K(e);case 6:return AK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function y1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return vK(e);case 1:return wK(e,t);case 2:return IK(e,t);case 3:return CK(e,t);default:return TK(e,t)}}function Z5(e,t,n=!1,s){let r="";n?r+=y1(e,s):r+=ru(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=EK(e,t):r+=RK(e,t)),r}function J5(e,t,n){switch(e.length){case 0:return v1();case 1:return lK(e,t,n);case 2:return bK(e,t,n);case 3:return dK(e,t,n);default:return hK(e,t,n)}}function eK(e,t,n){switch(e.length){case 0:return v1();case 1:return cK(e,t,n);case 2:return yK(e,t,n);case 3:return pK(e,t,n);case 4:return fK(e,t,n);case 5:return mK(e,t);case 6:return gK(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function tK(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function q5(e){return`
+  `}function nK(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function j5(e){return`
+  `}function sK(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function K5(e){return`${e.version}
+  `}function rK(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -223,10 +223,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${X5}
-    ${Y5}
-    ${Q5}
-  `}var X5=`
+    ${aK}
+    ${iK}
+    ${oK}
+  `}var aK=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -238,7 +238,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,Y5=`
+`,iK=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -246,7 +246,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,Q5=`
+`,oK=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -255,7 +255,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,Z5=`
+`,uK=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -266,11 +266,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function f1(){return`
+`;function v1(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function J5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
+  `}function lK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
       }
@@ -299,7 +299,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${s[0]}, ${s[1]}));
       return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
     }
-  `}function eK(e,t,n){return t[0]===1?n?`
+  `}function cK(e,t,n){return t[0]===1?n?`
       int getOutputCoords() {
         return int(resultUV.x * float(outTexShape[1]));
       }
@@ -327,7 +327,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function tK(e,t,n){if(n)return`
+  `}function dK(e,t,n){if(n)return`
     ivec3 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
@@ -358,15 +358,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function nK(e,t,n){if(n)return`
+  `}function pK(e,t,n){if(n)return`
   ivec3 getOutputCoords() {
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${qp(["r","c","d"],e)}
+    ${Kp(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
-`;let s=yi(["r","c","d"],e);return`
+`;let s=wi(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -374,7 +374,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${s}
       return ivec3(r, c, d);
     }
-  `}function sK(e,t,n){if(n)return`
+  `}function hK(e,t,n){if(n)return`
     ivec4 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -415,15 +415,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${u});
     }
-  `}function rK(e,t,n){if(n)return`
+  `}function fK(e,t,n){if(n)return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${qp(["r","c","d","d2"],e)}
+      ${Kp(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
-  `;let s=yi(["r","c","d","d2"],e);return`
+  `;let s=wi(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -431,7 +431,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${s}
       return ivec4(r, c, d, d2);
     }
-  `}function aK(e,t){let n=yi(["r","c","d","d2","d3"],e);return`
+  `}function mK(e,t){let n=wi(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -443,7 +443,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function iK(e,t){let n=yi(["r","c","d","d2","d3","d4"],e);return`
+  `}function gK(e,t){let n=wi(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -454,7 +454,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function oK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
+  `}function bK(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`
       ivec2 getOutputCoords() {
         ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
         return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
@@ -487,7 +487,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function uK(e,t,n){return w.arraysEqual(e,t)?n?`
+  `}function yK(e,t,n){return w.arraysEqual(e,t)?n?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
       }
@@ -541,15 +541,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function vi(e){return`offset${e}`}function lK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`
+  `}function ki(e){return`offset${e}`}function vK(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=fn();return`
     vec4 ${n}() {
       return ${s.texture2D}(${t}, halfCR);
     }
-  `}function cK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function xK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${s}() {
         return sampleTexture(${n}, halfCR);
       }
-    `;let i=vi(n);if(t)return`
+    `;let i=ki(n);if(t)return`
     float ${s}() {
       vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});
       return sampleTexture(${n}, uv);
@@ -559,7 +559,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${o}, ${u}, ${i});
       return sampleTexture(${n}, uv);
     }
-  `}function dK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`
+  `}function wK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=fn();if(t)return`
     vec4 ${s}(int index) {
       ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
       vec2 uv = packedUVfrom1D(
@@ -572,15 +572,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${i[0]}, ${i[1]}, index);
       return ${a.texture2D}(${n}, uv);
     }
-  `}function pK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function kK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
       float ${s}(int index) {
-        ${ru(e)}
+        ${au(e)}
       }
     `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`
       float ${s}(int index) {
         return sampleTexture(${n}, halfCR);
       }
-    `;let o=vi(n);return i===1?t?`
+    `;let o=ki(n);return i===1?t?`
       float ${s}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));
         return sampleTexture(${n}, uv);
@@ -610,7 +610,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});
       return sampleTexture(${n}, uv);
     }
-  `}function hK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`
+  `}function IK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=fn();if(a!=null&&w.arraysEqual(n,a))return t?`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
 
@@ -634,7 +634,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);
       return ${u.texture2D}(${s}, uv);
     }
-  `}function fK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
+  `}function SK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`
       float ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
         return sampleTexture(${s}, uv);
@@ -644,17 +644,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
       return sampleTexture(${s}, uv);
     }
-  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=au(e,u),h=["row","col"];return`
-      ${su(d,t)}
+  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=iu(e,u),h=["row","col"];return`
+      ${ru(d,t)}
       float ${r}(int row, int col) {
-        return ${r}(${iu(h,o)});
+        return ${r}(${ou(h,o)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
-    `;let l=a[0],c=a[1],p=vi(s);return c===1?t?`
+    `;let l=a[0],c=a[1],p=ki(s);return c===1?t?`
       float ${r}(int row, int col) {
         float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
         vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
@@ -692,10 +692,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${s}, uv);
   }
-`}function mK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=au(e,d),m=["b","row","col"];return`
-        ${h1(f,t)}
+`}function CK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=iu(e,d),m=["b","row","col"];return`
+        ${y1(f,t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${iu(m,h)});
+          return ${r}(${ou(m,h)});
         }
       `}let o=fn();if(t)return`
     vec4 ${r}(int b, int row, int col) {
@@ -712,16 +712,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${u}, ${l}, ${p}, ${c}, b, row, col);
       return ${o.texture2D}(${s}, uv);
     }
-  `}function gK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=au(e,l),g=["row","col","depth"];return`
-        ${su(m,t)}
+  `}function NK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=iu(e,l),g=["row","col","depth"];return`
+        ${ru(m,t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${iu(g,u)});
+          return ${r}(${ou(g,u)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${i}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
     `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
       float ${r}(int row, int col, int depth) {
@@ -754,7 +754,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
       return sampleTexture(${s}, uv);
     }
-  `;let f=vi(s);return t?`
+  `;let f=ki(s);return t?`
     float ${r}(int row, int col, int depth) {
       // Explicitly use integer operations as dot() only works on floats.
       int stride0 = ${s}Shape[1] * ${s}Shape[2];
@@ -770,7 +770,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         vec2 uv = uvFromFlat(${p}, ${d}, index);
         return sampleTexture(${s}, uv);
       }
-  `}function bK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`
+  `}function TK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=fn();if(t)return`
     vec4 ${s}(int b2, int b, int row, int col) {
       int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
       int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
@@ -790,16 +790,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
       return ${r.texture2D}(${n}, uv);
     }
-  `}function yK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=au(e,u),v=["row","col","depth","depth2"];return`
-      ${su(y,t)}
+  `}function $K(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=iu(e,u),v=["row","col","depth","depth2"];return`
+      ${ru(y,t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${iu(v,l)});
+        return ${r}(${ou(v,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${a}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`
       float ${r}(int row, int col, int depth, int depth2) {
@@ -841,7 +841,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${s}, uv);
       }
-    `;let b=vi(s);return t?`
+    `;let b=ki(s);return t?`
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       ${f}
@@ -860,10 +860,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
       return sampleTexture(${s}, uv);
     }
-  `}function vK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=au(e,u),g=["row","col","depth","depth2","depth3"];return`
-      ${su(m)}
+  `}function _K(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=iu(e,u),g=["row","col","depth","depth2","depth3"];return`
+      ${ru(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${iu(g,l)});
+        return ${s}(${ou(g,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -871,7 +871,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${a}, ${r})) +
           depth3;
-        ${ru(e)}
+        ${au(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -893,7 +893,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let f=vi(n);return`
+    `;let f=ki(n);return`
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${o} + col * ${i} + depth * ${a} +
@@ -901,11 +901,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function xK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=au(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
-      ${su(g)}
+  `}function AK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=iu(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
+      ${ru(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${iu(b,a)});
+        return ${s}(${ou(b,a)});
       }
     `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
@@ -916,7 +916,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${ru(e)}
+        ${au(e)}
       }
     `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
       float ${s}(int row, int col, int depth,
@@ -942,7 +942,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let m=vi(n);return`
+    `;let m=ki(n);return`
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -951,13 +951,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function ru(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function au(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
       }
     }
-  `}function wK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=p1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(i),l=i-a,c,p=["x","y","z","w","u","v"];a===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`
+  `}function EK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=b1(e.shapeInfo.logicalShape,t.logicalShape),u=rt(i),l=i-a,c,p=["x","y","z","w","u","v"];a===0?c="":i<2&&o.length>=1?c="coords = 0;":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`
 `);let d="";i<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(", ");let h="return outputValue;",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(m&&!b)i===1?h=`
@@ -971,20 +971,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec4 outputValue = get${s}(${d});
       ${h}
     }
-  `}function kK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`
+  `}function RK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`
       float ${r}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let l=rt(u),c=p1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=["x","y","z","w","u","v"];o===0?d="":u<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
+    `;let l=rt(u),c=b1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=["x","y","z","w","u","v"];o===0?d="":u<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
 `);let f="";return u<2&&o>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
     float ${r}() {
       ${l} coords = getOutputCoords();
       ${d}
       return get${s}(${f});
     }
-  `}function rt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function mv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function au(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function iu(e,t){return t.map(n=>e[n]).join(", ")}function IK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=V5(r,i,t),u=GC(e.gl,o),l=e.createProgram(u);return X().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...m1(e,t,l)}}function m1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",d),l=e.getUniformLocation(n,"outShapeStrides",d),u=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function rw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function SK(e,t,n,s,r){t.program.enableShapeUniforms||(rw(t.inShapeInfos,n),rw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=mv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function CK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=mv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function Sn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var NK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+  `}function rt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function bv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function iu(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function ou(e,t){return t.map(n=>e[n]).join(", ")}function DK(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=Q5(r,i,t),u=XC(e.gl,o),l=e.createProgram(u);return X().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:{program:t,fragmentShader:u,source:o,webGLProgram:l,inShapeInfos:a,outShapeInfo:i,...x1(e,t,l)}}function x1(e,t,n){let s={},r={},a={},i=[],o,u,l,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(o=e.getUniformLocation(n,"outShape",d),l=e.getUniformLocation(n,"outShapeStrides",d),u=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{i[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:i,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:o,outShapeStridesLocation:l,outTexShapeLocation:u}}function aw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function FK(e,t,n,s,r){t.program.enableShapeUniforms||(aw(t.inShapeInfos,n),aw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=bv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function OK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=bv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=S.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function Sn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var PK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?qp(["r","c","d"],e):yi(["r","c","d"],e)}
+        ${this.enableShapeUniforms?Kp(["r","c","d"],e):wi(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1002,9 +1002,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},TK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+    `}},zK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?qp(["r","c","d"],e):yi(["r","c","d"],e)}
+        ${this.enableShapeUniforms?Kp(["r","c","d"],e):wi(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1022,23 +1022,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},$K=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`
-      ${d1}
+    `}},MK=class{constructor(e){this.variableNames=["A"],this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`
+      ${g1}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},_K=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`
-      ${d1}
+    `}},LK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=fn();this.outputShape=e,this.userCode=`
+      ${g1}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},AK=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?fv():hv(e)}
+    `}},BK=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
+      ${this.enableShapeUniforms?gv():mv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1067,7 +1067,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${n.output} = vec4(${s}, 0., 0., 0.);
       }
-    `}},EK=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`
+    `}},VK=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=fn();this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`
           localCoords = coords;
           if(localCoords[2] + ${i} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
           localCoords[2] += ${i};
@@ -1096,7 +1096,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?fv():hv(e)}
+        ${this.enableShapeUniforms?gv():mv(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1111,7 +1111,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${n.output} = ${r};
         }
-    `}},RK={};Ae(RK,{bindVertexProgramAttributeStreams:()=>S1,createBufferFromOutputTexture:()=>T1,createFloat16MatrixTexture:()=>x1,createFloat16PackedMatrixTexture:()=>I1,createFloat32MatrixTexture:()=>v1,createIndexBuffer:()=>y1,createPackedMatrixTexture:()=>k1,createUnsignedBytesMatrixTexture:()=>w1,createVertexBuffer:()=>b1,createVertexShader:()=>g1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>_1,downloadFloat32MatrixFromBuffer:()=>$1,downloadMatrixFromPackedOutputTexture:()=>E1,downloadPackedMatrixFromBuffer:()=>A1,getInternalFormatForFloat16MatrixTexture:()=>bv,getInternalFormatForFloat16PackedMatrixTexture:()=>xv,getInternalFormatForFloat32MatrixTexture:()=>gv,getInternalFormatForPackedMatrixTexture:()=>vv,getInternalFormatForUnsignedBytesMatrixTexture:()=>yv,uploadDenseMatrixToTexture:()=>C1,uploadPixelDataToTexture:()=>N1});function g1(e){let t=fn(),n=`${t.version}
+    `}},WK={};Ae(WK,{bindVertexProgramAttributeStreams:()=>_1,createBufferFromOutputTexture:()=>R1,createFloat16MatrixTexture:()=>C1,createFloat16PackedMatrixTexture:()=>$1,createFloat32MatrixTexture:()=>S1,createIndexBuffer:()=>I1,createPackedMatrixTexture:()=>T1,createUnsignedBytesMatrixTexture:()=>N1,createVertexBuffer:()=>k1,createVertexShader:()=>w1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>F1,downloadFloat32MatrixFromBuffer:()=>D1,downloadMatrixFromPackedOutputTexture:()=>P1,downloadPackedMatrixFromBuffer:()=>O1,getInternalFormatForFloat16MatrixTexture:()=>vv,getInternalFormatForFloat16PackedMatrixTexture:()=>kv,getInternalFormatForFloat32MatrixTexture:()=>yv,getInternalFormatForPackedMatrixTexture:()=>wv,getInternalFormatForUnsignedBytesMatrixTexture:()=>xv,uploadDenseMatrixToTexture:()=>A1,uploadPixelDataToTexture:()=>E1});function w1(e){let t=fn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1120,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
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fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function D1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function F1(e,t,n,s){let[r,a]=Yl(t,n),i=4,o=new Uint8Array(z5(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function O1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array(M5(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function P1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Xf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,F5(t,e)):this.gl=vs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),X().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Ou(this.gl,r),Ln(this.gl,a))this.textureHalfFloatExtension=Ou(this.gl,a);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Ln(this.gl,s))this.colorBufferHalfFloatExtension=Ou(this.gl,s);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Ln(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Ln(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=k1(this.gl),this.indexBuffer=I1(this.gl),this.framebuffer=n1(this.gl),this.textureConfig=hv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),S1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),C1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),N1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),E1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),A1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),$1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),T1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Um(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>F1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return O1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return D1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=R1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(X().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>P1(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=w1(t));let n=YC(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),QC(t,n),this.debug&&rd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=_1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&rd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?r1(this.gl,e,t):a1(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),i1(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=nu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&rd(this.gl,this.program),Pu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Ou(this.gl,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=UK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ad(this.gl,e,this.framebuffer),this.debug&&Pu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ad(this.gl,this.outputTexture,this.framebuffer),this.debug&&Pu(this.gl)):Um(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;ad(s,e,this.framebuffer),this.debug&&Pu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function UK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:GK,bincountImpl:z1,bincountReduceImpl:HK,ceilImpl:qK,concatImpl:jK,equalImpl:KK,expImpl:XK,expm1Impl:YK,floorImpl:QK,gatherNdImpl:ZK,gatherV2Impl:JK,greaterImpl:eX,greaterEqualImpl:tX,lessImpl:nX,lessEqualImpl:sX,linSpaceImpl:rX,logImpl:aX,maxImpl:iX,maximumImpl:oX,minimumImpl:uX,multiplyImpl:lX,negImpl:cX,notEqualImpl:dX,prodImpl:pX,rangeImpl:hX,rsqrtImpl:fX,sigmoidImpl:mX,simpleAbsImpl:M1,sliceImpl:gX,sparseFillEmptyRowsImpl:bX,sparseReshapeImpl:yX,sparseSegmentReductionImpl:L1,sqrtImpl:vX,stridedSliceImpl:xX,stringNGramsImpl:wX,stringSplitImpl:kX,stringToHashBucketFastImpl:IX,subImpl:SX,tileImpl:CX,topKImpl:NX,transposeImpl:Iv,uniqueImpl:TX}=ev;function B1(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function ln(e,t){return t===1?[e]:B1(e,t)}function $X(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var _X=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=Sn(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -1147,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
             cEdge ? 0. : getA(${t[1]}),
             rEdge ? 0. : getA(${t[2]}),
-            rEdge || cEdge ? 0. : getA(${t[3]})`}},P1=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},V1=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
         ${r}
         ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1159,8 +1159,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${s>0?"}":""}
       `}this.userCode=`
-      ${xX(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?fv():hv(e)}
+      ${AX(t,this.enableShapeUniforms)}
+      ${this.enableShapeUniforms?gv():mv(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1175,12 +1175,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function xX(e,t){return`
+    `}};function AX(e,t){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t?B5(["r","c","d"],"inputShape"):yi(["r","c","d"],e)}
+      ${t?Y5(["r","c","d"],"inputShape"):wi(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var wX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=iw(t,n),r=ow(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=aw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=iw(n,s),a=ow(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=aw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function kX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function aw(e,t,n,s,r){let a=IX(t,s),i;if(r){let[u,l]=tu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=kX(n,a);return i*o}function IX(e,t){switch(e){case 3:return vv(t);case 4:return xv(t);case 1:return gv(t);case 0:return bv(t);case 2:return yv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function SX(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function iw(e,t){if(e===1)return 3;if(e===0||e==null)return SX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function ow(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+  `}var EX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=ow(t,n),r=uw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=iw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=ow(n,s),a=uw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=iw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function RX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function iw(e,t,n,s,r){let a=DX(t,s),i;if(r){let[u,l]=nu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=RX(n,a);return i*o}function DX(e,t){switch(e){case 3:return wv(t);case 4:return kv(t);case 1:return yv(t);case 0:return vv(t);case 2:return xv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function FX(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function ow(e,t){if(e===1)return 3;if(e===0||e==null)return FX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function uw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1191,11 +1191,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},ss="if (isnan(x)) return x;",CX="return x;",uw="return abs(x);",NX="return (x >= 0.0) ? x : (exp(x) - 1.0);",TX=ss+`
+    `}},ss="if (isnan(x)) return x;",OX="return x;",lw="return abs(x);",PX="return (x >= 0.0) ? x : (exp(x) - 1.0);",zX=ss+`
   return (x < 0.0) ? 0.0 : x;
-`,$X=ss+`
+`,MX=ss+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,Mi="return x;",_X="return 1.0 / (1.0 + exp(-1.0 * x));",AX="return x;",EX=`
+`,Bi="return x;",LX="return 1.0 / (1.0 + exp(-1.0 * x));",BX="return x;",VX=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -1204,7 +1204,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,RX=`
+`,WX=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1214,7 +1214,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,DX=`
+`,UX=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1224,7 +1224,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,FX="return 1.0 / (1.0 + exp(-1.0 * x));",Zr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+`,GX="return 1.0 / (1.0 + exp(-1.0 * x));",Jr=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -1235,17 +1235,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},OX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln("rc",t),s=rt(t),r=yX(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
+    `}},HX=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let t=e.length,n=ln("rc",t),s=rt(t),r=$X(t,n),a=n.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
       void main() {
         ${s} rc = getOutputCoords();
         vec4 packedInput = getA(${r});
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},PX=xs.whereImpl,zX=1e-7,MX=1e-4,Zc={};function LX(e){return e in Zc||(Zc[e]={}),Zc[e]}var BX=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),VX=600;function WX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*VX/1024/1024}var z1=class extends nl{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Kf)t=e;else{let n=vs(X().getNumber("WEBGL_VERSION"),e);t=new Kf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber("WEBGL_VERSION"));t=new Kf(n),this.binaryCache=LX(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new wX(this.gpgpu),this.numMBBeforeWarning=WX(),this.texData=new Ud(this,Ss())}nextDataId(){return z1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Zr(i,Mi):p=new Hs(i,Mi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Zr(s,Mi):h=new Hs(s,Mi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new Zr(r,Mi):d=new Hs(r,Mi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!VC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?id(t):t,o=a?new _K(i):new $K(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=BX){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return PX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=D1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,uw,e.dtype);let t=new Hs(e.shape,uw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new OX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new vX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ba(e.shape),...ya(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ba(t),...ya(t)],a=new P1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=id(r),o;s?o=new TK(i):o=new NK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!el(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=CK(e,l,c),d=this.getAndSaveBinary(p,()=>IK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get("ENGINE_COMPILE_ONLY")||SK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?zX:MX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=r1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=tu(c[0],c[1])),o?d=new EK(p,m):d=new AK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=UX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await OI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(pv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=m1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},M1=z1;M1.nextDataId=0;function UX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ppe="0.0.0";function GX(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}dp.isBrowser()&&pp("webgl",()=>new M1,2);var zpe={forceHalfFloat:GX},L1=`
+    `}},qX=xs.whereImpl,jX=1e-7,KX=1e-4,Zc={};function XX(e){return e in Zc||(Zc[e]={}),Zc[e]}var YX=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),QX=600;function ZX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*QX/1024/1024}var W1=class extends sl{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Xf)t=e;else{let n=vs(X().getNumber("WEBGL_VERSION"),e);t=new Xf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber("WEBGL_VERSION"));t=new Xf(n),this.binaryCache=XX(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new EX(this.gpgpu),this.numMBBeforeWarning=ZX(),this.texData=new Hd(this,Ss())}nextDataId(){return W1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Jr(i,Bi):p=new Hs(i,Bi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=S.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Jr(s,Bi):h=new Hs(s,Bi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=S.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new Jr(r,Bi):d=new Hs(r,Bi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!qC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?id(t):t,o=a?new LK(i):new MK(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=YX){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){S.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return qX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Jr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=M1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,lw,e.dtype);let t=new Hs(e.shape,lw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new HX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new _X(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ya(e.shape),...va(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ya(t),...va(t)],a=new V1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=id(r),o;s?o=new zK(i):o=new PK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!tl(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=OK(e,l,c),d=this.getAndSaveBinary(p,()=>DK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),X().get("ENGINE_COMPILE_ONLY")||FK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?jX:KX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=l1(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=nu(c[0],c[1])),o?d=new VK(p,m):d=new BK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),T=this.texData.get(k.dataId);t.texShape=T.texShape,t.isPacked=T.isPacked,t.usage=T.usage,X().get("ENGINE_COMPILE_ONLY")?this.disposeData(k.dataId):(t.texture=T.texture,t.values=null,this.texData.delete(k.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=JX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await LI(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(fv(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:i,outShapeLocation:o,outShapeStridesLocation:u,outTexShapeLocation:l}=x1(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=i,e.outShapeLocation=o,e.outShapeStridesLocation=u,e.outTexShapeLocation=l}}},U1=W1;U1.nextDataId=0;function JX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var Ype="0.0.0";function e8(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}hp.isBrowser()&&fp("webgl",()=>new U1,2);var Qpe={forceHalfFloat:e8},G1=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,ro=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
+`,io=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Sn(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1255,7 +1255,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},jp=`
+    `}},Xp=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
@@ -1304,21 +1304,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function kn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var HX={kernelName:Ba,backendName:"webgl",kernelFunc:kn};function Rr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=kn({inputs:{x:s},backend:n}),u=kn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var qX={kernelName:jd,backendName:"webgl",kernelFunc:Rr},B1="return (a < 0.) ? b * a : a;",V1=`
+    `}};function kn(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var t8={kernelName:Wa,backendName:"webgl",kernelFunc:kn};function Dr(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=kn({inputs:{x:s},backend:n}),u=kn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var n8={kernelName:Xd,backendName:"webgl",kernelFunc:Dr},H1="return (a < 0.) ? b * a : a;",q1=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function jX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(V1,r.shape,i.shape):new ro(B1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var KX={kernelName:Va,backendName:"webgl",kernelFunc:jX},W1="return (a < 0.) ? b * a : a;",U1=`
+`;function s8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(q1,r.shape,i.shape):new io(H1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var r8={kernelName:Ua,backendName:"webgl",kernelFunc:s8},j1="return (a < 0.) ? b * a : a;",K1=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function XX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(U1,s.shape,r.shape):new ro(W1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var YX={kernelName:Ja,backendName:"webgl",kernelFunc:XX},ou="if (isnan(x)) return x;",QX=`
+`;function a8(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(K1,s.shape,r.shape):new io(j1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var i8={kernelName:ti,backendName:"webgl",kernelFunc:a8},uu="if (isnan(x)) return x;",o8=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,ZX=`
+`,u8=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Zr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new ro(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Rr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(f):f,b=u.dtype==="string"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new ro(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e==="linear")return t?AX:CX;if(e==="relu")return t?RX:TX;if(e==="elu")return t?EX:NX;if(e==="relu6")return t?DX:$X;if(e==="prelu")return t?U1:W1;if(e==="leakyrelu")return t?V1:B1;if(e==="sigmoid")return t?FX:_X;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var G1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Jr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function jt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,T={dataId:x.dataId,dtype:x.dtype,shape:u.shape},N={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new io(e,u.shape,l.shape);return c.runWebGLProgram(E,[T,N],cn(x.dtype,k.dtype))}),y=Dr({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||cn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?S.fromUint8ToStringArray(f):f,b=u.dtype==="string"?S.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new io(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Yp(e,t=!1){if(e==="linear")return t?BX:OX;if(e==="relu")return t?WX:zX;if(e==="elu")return t?VX:PX;if(e==="relu6")return t?UX:MX;if(e==="prelu")return t?K1:j1;if(e==="leakyrelu")return t?q1:H1;if(e==="sigmoid")return t?GX:LX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var X1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=Sn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:u?m=`vec4 activation(vec4 a) {
@@ -1357,7 +1357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},lw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},cw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},cw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},dw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1370,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},dw="return a * b;";function kv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new cw(lw.REAL,s.shape,r.shape),c=new cw(lw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Rr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=JK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Zl(dw,s.shape,r.shape):i=new ro(dw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var JX={kernelName:Ya,backendName:"webgl",kernelFunc:kv};function e8(e,t,n){let s=[ba(e.shape),...ya(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ba(t),...ya(t)],i=new P1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!el(r.shape,u)&&!(c.texture!==null&&el(c.shape,u))?e8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var t8={kernelName:Eo,backendName:"webgl",kernelFunc:he},pw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
+    `}},pw="return a * b;";function Sv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=S.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new dw(cw.REAL,s.shape,r.shape),c=new dw(cw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Dr({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=lX(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Zl(pw,s.shape,r.shape):i=new io(pw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var l8={kernelName:Za,backendName:"webgl",kernelFunc:Sv};function c8(e,t,n){let s=[ya(e.shape),...va(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ya(t),...va(t)],i=new V1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!tl(r.shape,u)&&!(c.texture!==null&&tl(c.shape,u))?c8(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var d8={kernelName:Ro,backendName:"webgl",kernelFunc:he},hw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1423,7 +1423,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},n8=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let l=Math.floor(n/4)*4,c=n%4,p=`
+    `}},p8=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let l=Math.floor(n/4)*4,c=n%4,p=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1515,12 +1515,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${u});
       }
-    `}};function s8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=S.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function xi(e,t,n,s){let r=s8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n==="mean"?c=i===0?new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new pw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new n8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var r8=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=a8(t);this.userCode=`
+    `}};function h8(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=S.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function Ii(e,t,n,s){let r=h8(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n==="mean"?c=i===0?new hw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new hw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new p8({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var f8=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=m8(t);this.userCode=`
     void main() {
       ${s} resRC = getOutputCoords();
       setOutput(getA(${r}));
     }
-    `}};function a8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var i8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=O1("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
+    `}};function m8(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var g8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=rt(this.rank),r=B1("rc",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
     void main() {
       ${s} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1537,14 +1537,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function Xp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new i8(e.shape,t):new r8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function o8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=xi(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return o8(r,a,i,n)}var u8={kernelName:oi,backendName:"webgl",kernelFunc:Yp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=wv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var l8={kernelName:pi,backendName:"webgl",kernelFunc:qt},H1=1e3;function zd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=n?N.shape[1]:N.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?Kp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>H1&&q===!1){let Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=kv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new G1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function c8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var d8={kernelName:ra,backendName:"webgl",kernelFunc:c8},hw="return abs(x);";function p8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=D1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zr(s.shape,hw):r=new Hs(s.shape,hw),n.runWebGLProgram(r,[s],s.dtype)}var h8={kernelName:io,backendName:"webgl",kernelFunc:p8},f8=ss+`
+    `}};function Qp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new g8(e.shape,t):new f8(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function b8(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=S.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Qp(e,u,s),o=S.getInnerMostAxes(o.length,a)),S.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=S.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=pp(e.dtype),v=Ii(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Zp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return b8(r,a,i,n)}var y8={kernelName:ci,backendName:"webgl",kernelFunc:Zp};function qt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=Iv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Qp(r,a,i);return l}var v8={kernelName:mi,backendName:"webgl",kernelFunc:qt},Y1=1e3;function Ld({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ko.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=n?N.shape[1]:N.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?Yp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>Y1&&q===!1){let Z=N,te=E;n&&(Z=qt({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=qt({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=Sv({inputs:{a:ne,b:re},backend:r});K=Zp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=cn(e.dtype,t.dtype),te=new X1(k,T,[P,h,f],n,s,F,W,$,z),ee=[N,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function x8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Ld({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var w8={kernelName:aa,backendName:"webgl",kernelFunc:x8},fw="return abs(x);";function k8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=M1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Jr(s.shape,fw):r=new Hs(s.shape,fw),n.runWebGLProgram(r,[s],s.dtype)}var I8={kernelName:uo,backendName:"webgl",kernelFunc:k8},S8=ss+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,m8=Ke({opSnippet:f8}),g8={kernelName:sl,backendName:"webgl",kernelFunc:m8},b8=ss+`
+`,C8=Ke({opSnippet:S8}),N8={kernelName:rl,backendName:"webgl",kernelFunc:C8},T8=ss+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,backendName:"webgl",kernelFunc:y8},fw="return a + b;",x8=jt({opSnippet:fw,packedOpSnippet:fw,supportsComplex:!0,cpuKernelImpl:FK}),w8={kernelName:Ir,backendName:"webgl",kernelFunc:x8},k8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,$8=Ke({opSnippet:T8}),_8={kernelName:al,backendName:"webgl",kernelFunc:$8},mw="return a + b;",A8=jt({opSnippet:mw,packedOpSnippet:mw,supportsComplex:!0,cpuKernelImpl:GK}),E8={kernelName:Sr,backendName:"webgl",kernelFunc:A8},R8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1552,7 +1552,7 @@ return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,back
         float result = ${s};
         setOutput(result);
       }
-    `}},I8=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+    `}},D8=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1560,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,back
         vec4 result = ${s};
         setOutput(result);
       }
-    `}};function ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return kn({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new I8(s[0].shape,a):new k8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var S8={kernelName:ka,backendName:"webgl",kernelFunc:ld};function C8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("all",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"all",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var N8={kernelName:al,backendName:"webgl",kernelFunc:C8};function T8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("any",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"any",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var $8={kernelName:il,backendName:"webgl",kernelFunc:T8},_8=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return kn({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>cn(u,l)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new D8(s[0].shape,a):new R8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var F8={kernelName:Ia,backendName:"webgl",kernelFunc:ld};function O8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("all",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ii(m,m.dtype,"all",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var P8={kernelName:il,backendName:"webgl",kernelFunc:O8};function z8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,o)),S.assertAxesAreInnerMostDims("any",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ii(m,m.dtype,"any",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var M8={kernelName:ol,backendName:"webgl",kernelFunc:z8},L8=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1580,7 +1580,7 @@ return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,back
         }
         setOutput(float(bestIndex));
       }
-    `}},A8=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,u=rt(o),l=ln("coords",o),c,p;if(a===1){p=o+1;let N=rt(p);c=`
+    `}},B8=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,u=rt(o),l=ln("coords",o),c,p;if(a===1){p=o+1;let N=rt(p);c=`
         ${N} sourceLocR = ${N}(${l.join()}, 0);
         ++${l[o-1]};
         ${N} sourceLocG = ${N}(${l.join()}, 0);
@@ -1642,23 +1642,23 @@ return log(x + sqrt(x * x - 1.0));`,y8=Ke({opSnippet:b8}),v8={kernelName:rl,back
         }
         setOutput(bestIndex);
       }
-    `}};function q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new _8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function j1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new A8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=j1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function K1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return j1(e,t,s)}function E8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=K1(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var R8={kernelName:Ia,backendName:"webgl",kernelFunc:E8};function D8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=K1(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var F8={kernelName:ol,backendName:"webgl",kernelFunc:D8},O8=ss+`
+    `}};function Q1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=S.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new L8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=Q1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function Z1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=S.computeOptimalWindowSize(a),o=new B8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=Z1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function J1(e,t,n,s){let r=[n];if(S.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=S.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=Q1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return Z1(e,t,s)}function V8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=J1(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var W8={kernelName:Sa,backendName:"webgl",kernelFunc:V8};function U8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=qt({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=J1(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var G8={kernelName:ul,backendName:"webgl",kernelFunc:U8},H8=ss+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,P8=Ke({opSnippet:O8}),z8={kernelName:ul,backendName:"webgl",kernelFunc:P8},M8=ss+"return log(x + sqrt(x * x + 1.0));",L8=Ke({opSnippet:M8}),B8={kernelName:ll,backendName:"webgl",kernelFunc:L8},V8=ss+`
+`,q8=Ke({opSnippet:H8}),j8={kernelName:ll,backendName:"webgl",kernelFunc:q8},K8=ss+"return log(x + sqrt(x * x + 1.0));",X8=Ke({opSnippet:K8}),Y8={kernelName:cl,backendName:"webgl",kernelFunc:X8},Q8=ss+`
   return atan(x);
-`,W8=Ke({opSnippet:V8}),U8={kernelName:cl,backendName:"webgl",kernelFunc:W8},G8=QX+`
+`,Z8=Ke({opSnippet:Q8}),J8={kernelName:dl,backendName:"webgl",kernelFunc:Z8},eY=o8+`
   return atan(a, b);
-`,H8=`
+`,tY=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+ZX+`
+  `+u8+`
   return result;
-`,q8=jt({opSnippet:G8,packedOpSnippet:H8}),j8={kernelName:pl,backendName:"webgl",kernelFunc:q8},K8=ss+`
+`,nY=jt({opSnippet:eY,packedOpSnippet:tY}),sY={kernelName:hl,backendName:"webgl",kernelFunc:nY},rY=ss+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelName:dl,backendName:"webgl",kernelFunc:X8},tl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,aY=Ke({opSnippet:rY}),iY={kernelName:pl,backendName:"webgl",kernelFunc:aY},nl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${h});
 
@@ -1799,7 +1799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(${v});
       }
-    `}},Iv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let A=">=";this.userCode=`
+    `}},Cv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let A=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${u});
         const ivec3 pads = ivec3(${m}, ${g}, ${b});
@@ -1962,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           setOutput(${k});
         }
       }
-    `}};function Q8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Z8={kernelName:Sa,backendName:"webgl",kernelFunc:Q8};function J8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Iv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var eY={kernelName:qd,backendName:"webgl",kernelFunc:J8},tY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
+    `}};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;su(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new nl(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var uY={kernelName:Ca,backendName:"webgl",kernelFunc:oY};function lY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new Cv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var cY={kernelName:Kd,backendName:"webgl",kernelFunc:lY},dY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${l}, ${c});
       const float avgMultiplier = float(${p});
 
@@ -2004,7 +2004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},nY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
+    `}},pY=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
       const ivec3 pads = ivec3(${h}, ${f}, ${m});
       const float avgMultiplier = float(${g});
 
@@ -2060,7 +2060,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function sY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new nY(d);return n.runWebGLProgram(h,[r],i.dtype)}var rY={kernelName:og,backendName:"webgl",kernelFunc:sY};function aY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;nu([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new tY(c);return n.runWebGLProgram(p,[r],i.dtype)}var iY={kernelName:ig,backendName:"webgl",kernelFunc:aY};function oY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return zd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var uY={kernelName:Ca,backendName:"webgl",kernelFunc:oY},lY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function hY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new pY(d);return n.runWebGLProgram(h,[r],i.dtype)}var fY={kernelName:lg,backendName:"webgl",kernelFunc:hY};function mY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;su([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=S.computePool2DInfo(i.shape,o,u,1,l),p=new dY(c);return n.runWebGLProgram(p,[r],i.dtype)}var gY={kernelName:ug,backendName:"webgl",kernelFunc:mY};function bY(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Ld({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var yY={kernelName:Na,backendName:"webgl",kernelFunc:bY},vY=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2070,7 +2070,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         float inv = scale * inversesqrt(variance + float(${a}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},cY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},xY=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${i};
         vec4 scale = ${o};
@@ -2083,7 +2083,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},dY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let d=X().getBool("WEBGL_PACK_NORMALIZATION")?new cY(s.shape,r.shape,a.shape,c,p,u):new lY(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},pY={kernelName:Ma,backendName:"webgl",kernelFunc:dY},hY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=rt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=fY(this.rank),s,r=e.map((a,i)=>`sourceLoc.${Gm[i]} = start[${i}] + coords.${Gm[i]};`);s=`
+    `}},wY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let d=X().getBool("WEBGL_PACK_NORMALIZATION")?new xY(s.shape,r.shape,a.shape,c,p,u):new vY(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},kY={kernelName:Ba,backendName:"webgl",kernelFunc:wY},IY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=rt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=SY(this.rank),s,r=e.map((a,i)=>`sourceLoc.${qm[i]} = start[${i}] + coords.${qm[i]};`);s=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${r.join(`
@@ -2093,7 +2093,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${s}
         setOutput(getSource(${n}));
       }
-    `}},Gm=["x","y","z","w","u","v"];function fY(e){if(e===1)return"sourceLoc";if(e<=6)return Gm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var mY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=rt(this.rank),n=ln("coords",this.rank),s=ln("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`
+    `}},qm=["x","y","z","w","u","v"];function SY(e){if(e===1)return"sourceLoc";if(e<=6)return qm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var CY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=rt(this.rank),n=ln("coords",this.rank),s=ln("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`
       result.x = ${a};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${s[this.rank-1]};
@@ -2122,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${o}
         setOutput(result);
       }
-    `}};function gY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=wt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function uu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=iX(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new mY(u):new hY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),gY(r,o,u,n)}var bY={kernelName:Po,backendName:"webgl",kernelFunc:uu},yY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=uu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},vY={kernelName:oo,backendName:"webgl",kernelFunc:yY};function xY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=R1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var wY={kernelName:ug,backendName:"webgl",kernelFunc:xY};function kY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var IY={kernelName:lg,backendName:"webgl",kernelFunc:kY},SY="return float(a != b);",X1=jt({opSnippet:SY,cpuKernelImpl:tX,dtype:"bool"}),CY={kernelName:So,backendName:"webgl",kernelFunc:X1};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.real},backend:n})}var NY={kernelName:np,backendName:"webgl",kernelFunc:Jl},TY="return float(int(x));";function $Y(e,t){let n=new Hs(e.shape,TY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Hm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return kn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Hm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Rr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Jl({inputs:{input:r},backend:n}),o=Hm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=kn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return $Y(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=X1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var _Y={kernelName:Na,backendName:"webgl",kernelFunc:Hm},mw="return ceil(x);",AY=Ke({opSnippet:mw,packedOpSnippet:mw,cpuKernelImpl:PK}),EY={kernelName:Ta,backendName:"webgl",kernelFunc:AY},RY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function NY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=wt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function lu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=gX(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new CY(u):new IY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),NY(r,o,u,n)}var TY={kernelName:zo,backendName:"webgl",kernelFunc:lu},$Y=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=lu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},_Y={kernelName:lo,backendName:"webgl",kernelFunc:$Y};function AY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=z1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var EY={kernelName:cg,backendName:"webgl",kernelFunc:AY};function RY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=S.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var DY={kernelName:dg,backendName:"webgl",kernelFunc:RY},FY="return float(a != b);",e2=jt({opSnippet:FY,cpuKernelImpl:dX,dtype:"bool"}),OY={kernelName:No,backendName:"webgl",kernelFunc:e2};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.real},backend:n})}var PY={kernelName:rp,backendName:"webgl",kernelFunc:Jl},zY="return float(int(x));";function MY(e,t){let n=new Hs(e.shape,zY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function jm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return kn({inputs:{x:r},backend:n});let i=$t(r.shape),o=jm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Dr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Jl({inputs:{input:r},backend:n}),o=jm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=kn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return MY(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=e2({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var LY={kernelName:Ta,backendName:"webgl",kernelFunc:jm},gw="return ceil(x);",BY=Ke({opSnippet:gw,packedOpSnippet:gw,cpuKernelImpl:qK}),VY={kernelName:$a,backendName:"webgl",kernelFunc:BY},WY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
 
       void main() {
         float value = getAAtOutCoords();
@@ -2133,7 +2133,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}},DY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}},UY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
       void main() {
         vec4 value = getAAtOutCoords();
 
@@ -2144,7 +2144,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}};function FY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool("WEBGL_PACK_CLIP")?o=new DY(r.shape):o=new RY(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var OY={kernelName:Sr,backendName:"webgl",kernelFunc:FY},PY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}};function GY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o;X().getBool("WEBGL_PACK_CLIP")?o=new UY(r.shape):o=new WY(r.shape);let u=[[a],[i]];return n.runWebGLProgram(o,[r],r.dtype,u)}var HY={kernelName:Cr,backendName:"webgl",kernelFunc:GY},qY=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -2157,7 +2157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function gw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function zY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new PY(s.shape),i=[gw(s,r.complexTensorInfos.real),gw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var MY={kernelName:Kd,backendName:"webgl",kernelFunc:zY},LY=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
+    `}};function bw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function jY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new qY(s.shape),i=[bw(s,r.complexTensorInfos.real),bw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var KY={kernelName:Yd,backendName:"webgl",kernelFunc:jY},XY=class{constructor(e){this.outputShape=[],this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2166,7 +2166,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${n.join(`
         `)}
       }
-    `}},BY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln("coords",s),i=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {
+    `}},YY=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=S.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=rt(s),a=ln("coords",s),i=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {
         return getChannel(
             getT0(${c}), vec2(${l.join()}));
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`
@@ -2203,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(result);
       }
-    `}};function Jc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var VY={kernelName:Zd,backendName:"webgl",kernelFunc:Qp};function Gi(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Jl({inputs:{input:m},backend:n})),p=e.map(m=>Qp({inputs:{input:m},backend:n})),d=Gi(c,t,n),h=Gi(p,t,n),f=Rr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=S.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=zK(p,d,s,h),m=S.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=Gi(e.slice(0,c),t,n),d=Gi(e.slice(c),t,n),h=Gi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new BY(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=WY(e,t,n),o=new LY(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function WY(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return kn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Gi(o,a,n)}var UY={kernelName:uo,backendName:"webgl",kernelFunc:Y1},Q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
+    `}};function Jc(e,t,n){let s=e.indexOf(t);return e.map((a,i)=>i===s?`${a} - ${n}`:a).join()}function Jp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return kn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var QY={kernelName:ep,backendName:"webgl",kernelFunc:Jp};function qi(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Jl({inputs:{input:m},backend:n})),p=e.map(m=>Jp({inputs:{input:m},backend:n})),d=qi(c,t,n),h=qi(p,t,n),f=Dr({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(b=>{let y=w.sizeFromShape(b.shape.slice(t));return he({inputs:{x:b},backend:n,attrs:{shape:[-1,y]}})}),p=c.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),d=S.computeOutShape(c.map(b=>b.shape),1),h=c[0].shape[0]===1,f=jK(p,d,s,h),m=S.computeOutShape(e.map(b=>b.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(b=>n.disposeIntermediateTensorInfo(b)),g}if(e.length>X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=qi(e.slice(0,c),t,n),d=qi(e.slice(c),t,n),h=qi([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new YY(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:i}=ZY(e,t,n),o=new XY(a.map(c=>c.shape)),u=n.runWebGLProgram(o,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let l=he({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function ZY(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>he({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function t2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return kn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),qi(o,a,n)}var JY={kernelName:co,backendName:"webgl",kernelFunc:t2},n2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,b=m?2:3,y=m?3:1,v="",x="";n&&(s?v=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?v=`float activation(float a) {
@@ -2337,7 +2337,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${x}
         setOutput(result);
       }
-    `}},GY=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},e9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${r}, ${a}, ${i});
       const ivec3 pads = ivec3(${t}, ${n}, ${s});
 
@@ -2425,7 +2425,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},HY=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n==="channelsLast",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u="";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`
+    `}},t9=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=Sn(this.outputShape.length);let{dataFormat:n}=t,s=fn(),r=n==="channelsLast",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u="";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`
           blockIndex = rc.y + ${c};
           pos = rc.x + ${l};
 
@@ -2472,7 +2472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
 
         ${s.output} = result;
       }
-    `}};function Z1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>H1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(el(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=zd({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=zd({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function J1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new HY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],"float32",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o==="leakyrelu",z=o?Kp(o,!0):null,W=new G1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,"float32"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function qY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=Z1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=J1({x:r,filter:a,convInfo:d,backend:n});else{let m=new Q1(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var jY={kernelName:$a,backendName:"webgl",kernelFunc:qY},KY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function s2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>Y1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},T=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(tl(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let N=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(N);let E=Ld({a:k,b:N,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=T,A.shape=n.outShape,g=kn({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=Ld({a:k,b:T,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:N},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(T),b.push(N)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function r2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),T=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(T);let N=new t9(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(N,[k],"float32",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o==="leakyrelu",z=o?Yp(o,!0):null,W=new X1(P.shape,T.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,T];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,"float32"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=s2({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=r2({x:r,filter:a,convInfo:d,backend:n});else{let m=new n2(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var s9={kernelName:_a,backendName:"webgl",kernelFunc:n9},r9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2514,7 +2514,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},XY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`
+    `}},a9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -2567,7 +2567,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},YY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
+    `}},i9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2609,7 +2609,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},QY=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`
+    `}},o9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${o}, ${u}, ${l});
 
       void main() {
@@ -2666,12 +2666,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function ZY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new KY(d);return n.runWebGLProgram(h,[r,a],"float32")}var JY={kernelName:cg,backendName:"webgl",kernelFunc:ZY};function e9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new XY(d);return n.runWebGLProgram(h,[r,a],"float32")}var t9={kernelName:_a,backendName:"webgl",kernelFunc:e9};function n9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new GY(l);return n.runWebGLProgram(c,[r,a],"float32")}var s9={kernelName:Xd,backendName:"webgl",kernelFunc:n9};function r9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new YY(l);return n.runWebGLProgram(c,[r,a],"float32")}var a9={kernelName:dg,backendName:"webgl",kernelFunc:r9};function i9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new QY(l);return n.runWebGLProgram(c,[r,a],"float32")}var o9={kernelName:pg,backendName:"webgl",kernelFunc:i9},u9=ou+`
+    `}};function u9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new r9(d);return n.runWebGLProgram(h,[r,a],"float32")}var l9={kernelName:pg,backendName:"webgl",kernelFunc:u9};function c9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new a9(d);return n.runWebGLProgram(h,[r,a],"float32")}var d9={kernelName:Aa,backendName:"webgl",kernelFunc:c9};function p9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new e9(l);return n.runWebGLProgram(c,[r,a],"float32")}var h9={kernelName:Qd,backendName:"webgl",kernelFunc:p9};function f9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=S.computeConv3DInfo(r.shape,u,i,1,o),c=new i9(l);return n.runWebGLProgram(c,[r,a],"float32")}var m9={kernelName:hg,backendName:"webgl",kernelFunc:f9};function g9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=S.computeConv3DInfo(u,a.shape,o,1,i),c=new o9(l);return n.runWebGLProgram(c,[r,a],"float32")}var b9={kernelName:fg,backendName:"webgl",kernelFunc:g9},y9=uu+`
   return cos(x);
-`,l9=Ke({opSnippet:u9}),c9={kernelName:Aa,backendName:"webgl",kernelFunc:l9},d9=`
+`,v9=Ke({opSnippet:y9}),x9={kernelName:Ea,backendName:"webgl",kernelFunc:v9},w9=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,p9=Ke({opSnippet:d9}),h9={kernelName:Ea,backendName:"webgl",kernelFunc:p9},f9=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,k9=Ke({opSnippet:w9}),I9={kernelName:Ra,backendName:"webgl",kernelFunc:k9},S9=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${y});
       void main() {
@@ -2732,33 +2732,33 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           setOutput(newValue);
         }
       }
-    `}},m9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new f9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},g9={kernelName:co,backendName:"webgl",kernelFunc:m9},bw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"1.0":`getX(${yw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},C9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new S9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},N9={kernelName:ho,backendName:"webgl",kernelFunc:C9},yw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"1.0":`getX(${vw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${rt(s)} coords = getOutputCoords();
-        int end = ${vw(s,"coords")};
+        int end = ${xw(s,"coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${vw(s,"coords")} = idx;
-          val *= getX(${yw(s,"coords")});
+          ${xw(s,"coords")} = idx;
+          val *= getX(${vw(s,"coords")});
         }
         setOutput(val);
       }
-    `}};function yw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new bw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new bw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var y9={kernelName:hl,backendName:"webgl",kernelFunc:b9},xw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${ww(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}};function vw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function xw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative product for rank ${e} is not yet supported`)}function T9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new yw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new yw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var $9={kernelName:po,backendName:"webgl",kernelFunc:T9},ww=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${kw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${rt(s)} coords = getOutputCoords();
-        int end = ${kw(s,"coords")};
+        int end = ${Iw(s,"coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${kw(s,"coords")} = idx;
-          val += getX(${ww(s,"coords")});
+          ${Iw(s,"coords")} = idx;
+          val += getX(${kw(s,"coords")});
         }
         setOutput(val);
       }
-    `}};function ww(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function v9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new xw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new xw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var x9={kernelName:lo,backendName:"webgl",kernelFunc:v9};function w9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=R1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var k9={kernelName:hg,backendName:"webgl",kernelFunc:w9},I9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}};function kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Iw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function _9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=S.getAxesPermutation([a],u),c=r;l!=null&&(c=qt({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=S.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=kn({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new ww(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new ww(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=S.getUndoAxesPermutation(l),m=qt({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var A9={kernelName:Da,backendName:"webgl",kernelFunc:_9};function E9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=z1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=HK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var R9={kernelName:mg,backendName:"webgl",kernelFunc:E9},D9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2777,7 +2777,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function S9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new I9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var C9={kernelName:po,backendName:"webgl",kernelFunc:S9},e2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function F9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new D9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var O9={kernelName:fo,backendName:"webgl",kernelFunc:F9},a2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?u=`float activation(float a) {
@@ -2830,7 +2830,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${l}
         setOutput(result);
       }
-    `}},t2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`
+    `}},i2=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Sn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`
           vec4 xTexelC${g*2};
@@ -3017,7 +3017,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${f}
         setOutput(result);
       }
-    `}};function N9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new t2(p):d=new e2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var T9={kernelName:Ra,backendName:"webgl",kernelFunc:N9},$9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+    `}};function P9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new i2(p):d=new a2(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var z9={kernelName:Fa,backendName:"webgl",kernelFunc:P9},M9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3052,7 +3052,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},_9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},L9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${a}, ${i});
 
       void main() {
@@ -3097,13 +3097,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function A9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new $9(p);return n.runWebGLProgram(d,[r,a],"float32")}var E9={kernelName:fg,backendName:"webgl",kernelFunc:A9};function R9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new _9(p);return n.runWebGLProgram(d,[r,a],"float32")}var D9={kernelName:mg,backendName:"webgl",kernelFunc:R9},F9=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=S.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new M9(p);return n.runWebGLProgram(d,[r,a],"float32")}var V9={kernelName:gg,backendName:"webgl",kernelFunc:B9};function W9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=S.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new L9(p);return n.runWebGLProgram(d,[r,a],"float32")}var U9={kernelName:bg,backendName:"webgl",kernelFunc:W9},G9=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function O9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new F9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var P9={kernelName:gg,backendName:"webgl",kernelFunc:O9},z9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
+    `}};function H9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new G9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var q9={kernelName:yg,backendName:"webgl",kernelFunc:H9},j9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
       const ivec2 strides = ivec2(${r}, ${a});
       const ivec2 pads = ivec2(${c}, ${p});
       const float neg_infinity = -3.4e38;
@@ -3141,7 +3141,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function M9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",u),c,p=new z9(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var L9={kernelName:Yd,backendName:"webgl",kernelFunc:M9};function B9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=qt({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=kv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Yp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var V9={kernelName:Qd,backendName:"webgl",kernelFunc:B9},W9="return (x >= 0.0) ? x : (exp(x) - 1.0);",U9=`
+    `}};function K9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=S.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",u),c,p=new j9(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var X9={kernelName:Zd,backendName:"webgl",kernelFunc:K9};function Y9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=qt({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=Sv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Zp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var Q9={kernelName:Jd,backendName:"webgl",kernelFunc:Y9},Z9="return (x >= 0.0) ? x : (exp(x) - 1.0);",J9=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3150,12 +3150,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,G9=Ke({opSnippet:W9,packedOpSnippet:U9}),H9={kernelName:Fa,backendName:"webgl",kernelFunc:G9},q9="return (b >= 1.0) ? a : a * (b + 1.0);",j9=`
+`,eQ=Ke({opSnippet:Z9,packedOpSnippet:J9}),tQ={kernelName:Pa,backendName:"webgl",kernelFunc:eQ},nQ="return (b >= 1.0) ? a : a * (b + 1.0);",sQ=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,K9=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(j9,s.shape,r.shape):new ro(q9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},X9={kernelName:bg,backendName:"webgl",kernelFunc:K9},Y9=`
+`,rQ=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(sQ,s.shape,r.shape):new io(nQ,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},aQ={kernelName:vg,backendName:"webgl",kernelFunc:rQ},iQ=`
   return vec4(equal(a, b));
-`,Q9="return float(a == b);",Z9=jt({opSnippet:Q9,packedOpSnippet:Y9,dtype:"bool",cpuKernelImpl:MK}),J9={kernelName:ho,backendName:"webgl",kernelFunc:Z9},eQ=`
+`,oQ="return float(a == b);",uQ=jt({opSnippet:oQ,packedOpSnippet:iQ,dtype:"bool",cpuKernelImpl:KK}),lQ={kernelName:mo,backendName:"webgl",kernelFunc:uQ},cQ=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3170,9 +3170,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,tQ=Ke({opSnippet:eQ}),nQ={kernelName:fl,backendName:"webgl",kernelFunc:tQ},sQ=ou+`
+`,dQ=Ke({opSnippet:cQ}),pQ={kernelName:fl,backendName:"webgl",kernelFunc:dQ},hQ=uu+`
   return exp(x);
-`,rQ=`
+`,fQ=`
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -3181,7 +3181,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,n2=Ke({opSnippet:sQ,packedOpSnippet:rQ,cpuKernelImpl:LK,dtype:"float32"}),aQ={kernelName:Oa,backendName:"webgl",kernelFunc:n2};function qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var iQ={kernelName:fo,backendName:"webgl",kernelFunc:qm},Iw="return exp(x) - 1.0;",oQ=Ke({opSnippet:Iw,packedOpSnippet:Iw,cpuKernelImpl:BK}),uQ={kernelName:mo,backendName:"webgl",kernelFunc:oQ},Sw=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,o2=Ke({opSnippet:hQ,packedOpSnippet:fQ,cpuKernelImpl:XK,dtype:"float32"}),mQ={kernelName:za,backendName:"webgl",kernelFunc:o2};function Km(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var gQ={kernelName:go,backendName:"webgl",kernelFunc:Km},Sw="return exp(x) - 1.0;",bQ=Ke({opSnippet:Sw,packedOpSnippet:Sw,cpuKernelImpl:YK}),yQ={kernelName:bo,backendName:"webgl",kernelFunc:bQ},Cw=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${r};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3214,12 +3214,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function s2(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new Sw("real",u,t),c=new Sw("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Rr({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function lQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return s2(s,!1,n)}var cQ={kernelName:yg,backendName:"webgl",kernelFunc:lQ},dQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function u2(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new Cw("real",u,t),c=new Cw("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Dr({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function vQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return u2(s,!1,n)}var xQ={kernelName:xg,backendName:"webgl",kernelFunc:vQ},wQ=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new dQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var pQ={kernelName:ml,backendName:"webgl",kernelFunc:ec},hQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new wQ(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var kQ={kernelName:ml,backendName:"webgl",kernelFunc:ec},IQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3233,7 +3233,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},fQ={kernelName:go,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new hQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Cw="return floor(x);",mQ=Ke({opSnippet:Cw,packedOpSnippet:Cw,cpuKernelImpl:VK}),gQ={kernelName:Pa,backendName:"webgl",kernelFunc:mQ},bQ=`
+    `}},SQ={kernelName:yo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new IQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Nw="return floor(x);",CQ=Ke({opSnippet:Nw,packedOpSnippet:Nw,cpuKernelImpl:QK}),NQ={kernelName:Ma,backendName:"webgl",kernelFunc:CQ},TQ=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3243,7 +3243,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   } else {
     return NAN;
   }
-`,yQ=`
+`,$Q=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -3264,7 +3264,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,vQ=jt({opSnippet:bQ,packedOpSnippet:yQ,dtype:"int32"}),xQ={kernelName:za,backendName:"webgl",kernelFunc:vQ},wQ=class{constructor(e){this.variableNames=["A"];let t=fn(),[n,s]=e;this.outputShape=e,this.userCode=`
+`,_Q=jt({opSnippet:TQ,packedOpSnippet:$Q,dtype:"int32"}),AQ={kernelName:La,backendName:"webgl",kernelFunc:_Q},EQ=class{constructor(e){this.variableNames=["A"];let t=fn(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3286,7 +3286,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},kQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,s]=e;this.outputShape=e,this.userCode=`
+    `}},RQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=fn(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3320,7 +3320,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
 
         ${t.output} = result;
       }
-    `}},IQ={kernelName:fd,backendName:"webgl",kernelFunc:SQ},Li;function SQ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(o||i)&&(Li==null&&(Li=document.createElement("canvas").getContext("2d")),Li.canvas.width=u,Li.canvas.height=l,Li.drawImage(r,0,0,u,l),r=Li.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=X().getBool("WEBGL_PACK")?new kQ(p):new wQ(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function CQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=Z1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)b=J1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,T=h==="leakyrelu",N=h?Kp(h,!1):null,E=new Q1(g,x,N,k,T),A=[r,a];if(i&&A.push(i),o&&A.push(o),T){let P=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));A.push(P),y.push(P)}b=n.runWebGLProgram(E,A,"float32")}let v=he({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var NQ={kernelName:aa,backendName:"webgl",kernelFunc:CQ};function TQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=S.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?Kp(d,b):null,v=[r,a],x=i!=null,k=o!=null,T=d==="leakyrelu";if(x&&v.push(i),k&&v.push(o),T){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let N;b?N=new t2(g,x,y,k,T):N=new e2(g,x,y,k,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],A=n.runWebGLProgram(N,v,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),A}var $Q={kernelName:ia,backendName:"webgl",kernelFunc:TQ},_Q=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=rt(t.length),r=rt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},DQ={kernelName:fd,backendName:"webgl",kernelFunc:FQ},Vi;function FQ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(o||i)&&(Vi==null&&(Vi=document.createElement("canvas").getContext("2d")),Vi.canvas.width=u,Vi.canvas.height=l,Vi.drawImage(r,0,0,u,l),r=Vi.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=X().getBool("WEBGL_PACK")?new RQ(p):new EQ(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function OQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=s2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)b=r2({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,T=h==="leakyrelu",N=h?Yp(h,!1):null,E=new n2(g,x,N,k,T),A=[r,a];if(i&&A.push(i),o&&A.push(o),T){let P=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));A.push(P),y.push(P)}b=n.runWebGLProgram(E,A,"float32")}let v=he({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var PQ={kernelName:ia,backendName:"webgl",kernelFunc:OQ};function zQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=S.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?Yp(d,b):null,v=[r,a],x=i!=null,k=o!=null,T=d==="leakyrelu";if(x&&v.push(i),k&&v.push(o),T){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let N;b?N=new i2(g,x,y,k,T):N=new a2(g,x,y,k,T);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],A=n.runWebGLProgram(N,v,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),A}var MQ={kernelName:oa,backendName:"webgl",kernelFunc:zQ},LQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=rt(t.length),r=rt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${s} strides = ${s}(${this.strides});
          void main() {
           ${r} coords = getOutputCoords();
@@ -3331,24 +3331,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function AQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=WK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new _Q(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var EQ={kernelName:yo,backendName:"webgl",kernelFunc:AQ},RQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=DQ(e,2);this.userCode=`
+      `}};function BQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=ZK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new LQ(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var VQ={kernelName:xo,backendName:"webgl",kernelFunc:BQ},WQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=rt(this.rank),s=UQ(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${s}));
       }
-    `}};function DQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function r2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=UK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new RQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var FQ={kernelName:bo,backendName:"webgl",kernelFunc:r2},OQ="return float(a > b);",PQ=`
+    `}};function UQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function l2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=JK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new WQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var GQ={kernelName:vo,backendName:"webgl",kernelFunc:l2},HQ="return float(a > b);",qQ=`
   return vec4(greaterThan(a, b));
-`,zQ=jt({opSnippet:OQ,packedOpSnippet:PQ,cpuKernelImpl:GK,dtype:"bool"}),MQ={kernelName:vo,backendName:"webgl",kernelFunc:zQ},LQ="return float(a >= b);",BQ=`
+`,jQ=jt({opSnippet:HQ,packedOpSnippet:qQ,cpuKernelImpl:eX,dtype:"bool"}),KQ={kernelName:wo,backendName:"webgl",kernelFunc:jQ},XQ="return float(a >= b);",YQ=`
   return vec4(greaterThanEqual(a, b));
-`,VQ=jt({opSnippet:LQ,packedOpSnippet:BQ,dtype:"bool",cpuKernelImpl:HK}),WQ={kernelName:La,backendName:"webgl",kernelFunc:VQ};function UQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return s2(s,!0,n)}var GQ={kernelName:vg,backendName:"webgl",kernelFunc:UQ},HQ="return float(!isnan(x) && !isinf(x));",qQ=Ke({opSnippet:HQ,dtype:"bool"}),jQ={kernelName:gl,backendName:"webgl",kernelFunc:qQ},KQ="return float(isinf(x));",XQ=Ke({opSnippet:KQ,dtype:"bool"}),YQ={kernelName:bl,backendName:"webgl",kernelFunc:XQ},QQ="return float(isnan(x));",ZQ=Ke({opSnippet:QQ,dtype:"bool"}),JQ={kernelName:yl,backendName:"webgl",kernelFunc:ZQ},eZ="return float(a < b);",tZ=`
+`,QQ=jt({opSnippet:XQ,packedOpSnippet:YQ,dtype:"bool",cpuKernelImpl:tX}),ZQ={kernelName:Va,backendName:"webgl",kernelFunc:QQ};function JQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return u2(s,!0,n)}var eZ={kernelName:wg,backendName:"webgl",kernelFunc:JQ},tZ="return float(!isnan(x) && !isinf(x));",nZ=Ke({opSnippet:tZ,dtype:"bool"}),sZ={kernelName:gl,backendName:"webgl",kernelFunc:nZ},rZ="return float(isinf(x));",aZ=Ke({opSnippet:rZ,dtype:"bool"}),iZ={kernelName:bl,backendName:"webgl",kernelFunc:aZ},oZ="return float(isnan(x));",uZ=Ke({opSnippet:oZ,dtype:"bool"}),lZ={kernelName:yl,backendName:"webgl",kernelFunc:uZ},cZ="return float(a < b);",dZ=`
   return vec4(lessThan(a, b));
-`,nZ=jt({opSnippet:eZ,packedOpSnippet:tZ,cpuKernelImpl:qK,dtype:"bool"}),sZ={kernelName:xo,backendName:"webgl",kernelFunc:nZ},rZ="return float(a <= b);",aZ=`
+`,pZ=jt({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:nX,dtype:"bool"}),hZ={kernelName:ko,backendName:"webgl",kernelFunc:pZ},fZ="return float(a <= b);",mZ=`
   return vec4(lessThanEqual(a, b));
-`,iZ=jt({opSnippet:rZ,packedOpSnippet:aZ,cpuKernelImpl:jK,dtype:"bool"}),oZ={kernelName:wo,backendName:"webgl",kernelFunc:iZ};function uZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=KK(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var lZ={kernelName:xg,backendName:"webgl",kernelFunc:uZ},cZ=ou+`
+`,gZ=jt({opSnippet:fZ,packedOpSnippet:mZ,cpuKernelImpl:sX,dtype:"bool"}),bZ={kernelName:Io,backendName:"webgl",kernelFunc:gZ};function yZ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=rX(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var vZ={kernelName:kg,backendName:"webgl",kernelFunc:yZ},xZ=uu+`
   return x < 0.0 ? 0./0. : log(x);
-`,dZ=`
+`,wZ=`
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -3356,18 +3356,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
   result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
-`,pZ=Ke({opSnippet:cZ,packedOpSnippet:dZ,cpuKernelImpl:XK}),hZ={kernelName:Wa,backendName:"webgl",kernelFunc:pZ},fZ=ou+`
+`,kZ=Ke({opSnippet:xZ,packedOpSnippet:wZ,cpuKernelImpl:aX}),IZ={kernelName:Ga,backendName:"webgl",kernelFunc:kZ},SZ=uu+`
   return log(1.0 + x);
-`,mZ=Ke({opSnippet:fZ}),gZ={kernelName:vl,backendName:"webgl",kernelFunc:mZ},bZ="return float(a >= 1.0 && b >= 1.0);",yZ=`
+`,CZ=Ke({opSnippet:SZ}),NZ={kernelName:vl,backendName:"webgl",kernelFunc:CZ},TZ="return float(a >= 1.0 && b >= 1.0);",$Z=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,vZ=jt({opSnippet:bZ,packedOpSnippet:yZ,dtype:"bool"}),xZ={kernelName:ko,backendName:"webgl",kernelFunc:vZ},wZ="return float(!(x >= 1.0));",kZ=Ke({opSnippet:wZ}),IZ={kernelName:xl,backendName:"webgl",kernelFunc:kZ},SZ="return float(a >= 1.0 || b >= 1.0);",CZ=`
+`,_Z=jt({opSnippet:TZ,packedOpSnippet:$Z,dtype:"bool"}),AZ={kernelName:So,backendName:"webgl",kernelFunc:_Z},EZ="return float(!(x >= 1.0));",RZ=Ke({opSnippet:EZ}),DZ={kernelName:xl,backendName:"webgl",kernelFunc:RZ},FZ="return float(a >= 1.0 || b >= 1.0);",OZ=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,NZ=jt({opSnippet:SZ,packedOpSnippet:CZ,dtype:"bool"}),TZ={kernelName:Jd,backendName:"webgl",kernelFunc:NZ},$Z=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
+`,PZ=jt({opSnippet:FZ,packedOpSnippet:OZ,dtype:"bool"}),zZ={kernelName:tp,backendName:"webgl",kernelFunc:PZ},MZ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3386,7 +3386,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},_Z=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
+    `}},LZ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3448,7 +3448,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},AZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool("WEBGL_PACK_NORMALIZATION")?new _Z(r.shape,a,i,o,u):new $Z(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},EZ={kernelName:ep,backendName:"webgl",kernelFunc:AZ},RZ=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
+    `}},BZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool("WEBGL_PACK_NORMALIZATION")?new LZ(r.shape,a,i,o,u):new MZ(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},VZ={kernelName:np,backendName:"webgl",kernelFunc:BZ},WZ=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3503,14 +3503,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
       }
       setOutput(result);
       }
-    `}},DZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new RZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},FZ={kernelName:wg,backendName:"webgl",kernelFunc:DZ};function OZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=xi(o,e.dtype,"max",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function a2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[c[N]];let k=wv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Xp(r,c,n);l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims("max",l,o);let[f,m]=S.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=S.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=YK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=OZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var PZ={kernelName:Ua,backendName:"webgl",kernelFunc:a2},zZ=L1+`
+    `}},UZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new WZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},GZ={kernelName:Ig,backendName:"webgl",kernelFunc:UZ};function HZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=Ii(o,e.dtype,"max",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function c2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let N=0;N<x.length;N++)x[N]=r.shape[c[N]];let k=Iv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let T=n.texData.get(h.dataId);T.values=k}else h=Qp(r,c,n);l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims("max",l,o);let[f,m]=S.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=S.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=iX(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=HZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var qZ={kernelName:Ha,backendName:"webgl",kernelFunc:c2},jZ=G1+`
   return max(a, b);
-`,MZ=`
+`,KZ=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+jp+`
+  `+Xp+`
   return result;
-`,LZ=jt({opSnippet:zZ,packedOpSnippet:MZ,cpuKernelImpl:QK}),BZ={kernelName:Ga,backendName:"webgl",kernelFunc:LZ};function VZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new tl(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var WZ={kernelName:Ha,backendName:"webgl",kernelFunc:VZ};function UZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Iv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var GZ={kernelName:tp,backendName:"webgl",kernelFunc:UZ},HZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
+`,XZ=jt({opSnippet:jZ,packedOpSnippet:KZ,cpuKernelImpl:oX}),YZ={kernelName:qa,backendName:"webgl",kernelFunc:XZ};function QZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;su(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(S.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return kn({inputs:{x:r},backend:n});let p=new nl(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var ZZ={kernelName:ja,backendName:"webgl",kernelFunc:QZ};function JZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=S.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new Cv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var e7={kernelName:sp,backendName:"webgl",kernelFunc:JZ},t7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3556,7 +3556,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},qZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
+    `}},n7=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
       const ivec3 pads = ivec3(${c}, ${p}, ${d});
 
       void main() {
@@ -3620,14 +3620,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function jZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Iv(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new qZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var KZ={kernelName:Ig,backendName:"webgl",kernelFunc:jZ};function XZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;nu([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new tl(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new HZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var YZ={kernelName:kg,backendName:"webgl",kernelFunc:XZ};function QZ(e,t,n,s){let r=new tl(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new tl(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var ZZ={kernelName:Sg,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=S.computePool2DInfo(s.shape,r,a,l,i),[p,d]=QZ(s,o,c,u);return[p,d]}};function JZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=xi(o,"float32","mean",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var e7={kernelName:qa,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let E=0;E<k.length;E++)k[E]=s.shape[c[E]];let T=wv(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let N=i.texData.get(f.dataId);N.values=T}else f=Xp(s,c,i);h.push(f),l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=S.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=S.expandShapeToKeepDim(m,u));let y=JZ(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function t7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,r.shape.length)),S.assertAxesAreInnerMostDims("min",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=xi(m,m.dtype,"min",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var n7={kernelName:ja,backendName:"webgl",kernelFunc:t7},s7=L1+`
+    `}};function s7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=S.computePool3DInfo(i.shape,o,u,p,l,c),h=new Cv(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new n7(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var r7={kernelName:Cg,backendName:"webgl",kernelFunc:s7};function a7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;su([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=S.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new nl(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new t7(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var i7={kernelName:Sg,backendName:"webgl",kernelFunc:a7};function o7(e,t,n,s){let r=new nl(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new nl(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var u7={kernelName:Ng,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(S.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=S.computePool2DInfo(s.shape,r,a,l,i),[p,d]=o7(s,o,c,u);return[p,d]}};function l7(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=Ii(o,"float32","mean",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var c7={kernelName:Ka,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=S.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let E=0;E<k.length;E++)k[E]=s.shape[c[E]];let T=Iv(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let N=i.texData.get(f.dataId);N.values=T}else f=Qp(s,c,i);h.push(f),l=S.getInnerMostAxes(l.length,o)}S.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=S.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=S.expandShapeToKeepDim(m,u));let y=l7(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function d7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=S.getAxesPermutation(l,o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),l=S.getInnerMostAxes(l.length,r.shape.length)),S.assertAxesAreInnerMostDims("min",l,o);let[d,h]=S.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=Ii(m,m.dtype,"min",n),b;if(i){let y=S.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var p7={kernelName:Xa,backendName:"webgl",kernelFunc:d7},h7=G1+`
   return min(a, b);
-`,r7=`
+`,f7=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+jp+`
+  `+Xp+`
   return result;
-`,a7=jt({opSnippet:s7,packedOpSnippet:r7,cpuKernelImpl:ZK}),i7={kernelName:Ka,backendName:"webgl",kernelFunc:a7},o7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=rt(s),a=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
+`,m7=jt({opSnippet:h7,packedOpSnippet:f7,cpuKernelImpl:uX}),g7={kernelName:Ya,backendName:"webgl",kernelFunc:m7},b7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=rt(s),a=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3656,7 +3656,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${r} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},u7=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=rt(s),a=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=ln("rc",s),u=ln("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
+    `}},y7=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=rt(s),a=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=ln("rc",s),u=ln("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
         ${r} source = rc;
         if (source < start) {
           source = start * 2 - source - ${p};
@@ -3712,13 +3712,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},l7=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new u7(s.shape,r,a):new o7(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},c7={kernelName:Xa,backendName:"webgl",kernelFunc:l7},d7=`if (b == 0.0) return NAN;
-  return mod(a, b);`,p7=`
+    `}},v7=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new y7(s.shape,r,a):new b7(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},x7={kernelName:Qa,backendName:"webgl",kernelFunc:v7},w7=`if (b == 0.0) return NAN;
+  return mod(a, b);`,k7=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+jp+`
+  `+Xp+`
   return result;
-`,h7=jt({opSnippet:d7,packedOpSnippet:p7}),f7={kernelName:wl,backendName:"webgl",kernelFunc:h7},m7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
+`,I7=jt({opSnippet:w7,packedOpSnippet:k7}),S7={kernelName:wl,backendName:"webgl",kernelFunc:I7},C7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3738,11 +3738,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,X8=Ke({opSnippet:K8}),Y8={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}},g7=`
+    `}},N7=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,b7=`
+return a / b;`,T7=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3760,9 +3760,9 @@ return a / b;`,b7=`
   }
 
   return result;
-`,i2=jt({opSnippet:g7,packedOpSnippet:b7,checkOutOfBounds:!0}),y7={kernelName:Da,backendName:"webgl",kernelFunc:i2},Nw="return a - b;",o2=jt({opSnippet:Nw,packedOpSnippet:Nw,supportsComplex:!0,cpuKernelImpl:fX}),v7={kernelName:ci,backendName:"webgl",kernelFunc:o2};function u2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=a2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=he({inputs:{x:o},backend:n,attrs:{shape:u}}),c=o2({inputs:{a:r,b:l},backend:n}),p=n2({inputs:{x:c},backend:n}),d=Yp({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:d},backend:n,attrs:{shape:u}}),f=i2({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var x7={kernelName:ui,backendName:"webgl",kernelFunc:u2};function w7(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,u=o?r:u2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new m7(l,c,a),d=[[i]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}var k7={kernelName:Cg,backendName:"webgl",kernelFunc:w7},I7=ss+`
+`,d2=jt({opSnippet:N7,packedOpSnippet:T7,checkOutOfBounds:!0}),$7={kernelName:Oa,backendName:"webgl",kernelFunc:d2},Tw="return a - b;",p2=jt({opSnippet:Tw,packedOpSnippet:Tw,supportsComplex:!0,cpuKernelImpl:SX}),_7={kernelName:hi,backendName:"webgl",kernelFunc:p2};function h2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=c2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=he({inputs:{x:o},backend:n,attrs:{shape:u}}),c=p2({inputs:{a:r,b:l},backend:n}),p=o2({inputs:{x:c},backend:n}),d=Zp({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:d},backend:n,attrs:{shape:u}}),f=d2({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var A7={kernelName:di,backendName:"webgl",kernelFunc:h2};function E7(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,u=o?r:h2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new C7(l,c,a),d=[[i]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}var R7={kernelName:Tg,backendName:"webgl",kernelFunc:E7},D7=ss+`
   return -x;
-`,S7=`
+`,F7=`
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -3772,14 +3772,14 @@ return a / b;`,b7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function C7(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=eX(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zr(s.shape,S7):r=new Hs(s.shape,I7),n.runWebGLProgram(r,[s],s.dtype)}var N7={kernelName:Io,backendName:"webgl",kernelFunc:C7},T7=xs.nonMaxSuppressionV3Impl;function $7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=T7(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var _7={kernelName:Co,backendName:"webgl",kernelFunc:$7},A7=xs.nonMaxSuppressionV4Impl;function E7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=A7(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var R7={kernelName:kl,backendName:"webgl",kernelFunc:E7},D7=xs.nonMaxSuppressionV5Impl;function F7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=D7(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var O7={kernelName:No,backendName:"webgl",kernelFunc:F7},P7=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function O7(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=cX(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Jr(s.shape,F7):r=new Hs(s.shape,D7),n.runWebGLProgram(r,[s],s.dtype)}var P7={kernelName:Co,backendName:"webgl",kernelFunc:O7},z7=xs.nonMaxSuppressionV3Impl;function M7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=z7(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var L7={kernelName:To,backendName:"webgl",kernelFunc:M7},B7=xs.nonMaxSuppressionV4Impl;function V7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=B7(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var W7={kernelName:kl,backendName:"webgl",kernelFunc:V7},U7=xs.nonMaxSuppressionV5Impl;function G7(e){S.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=U7(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var H7={kernelName:$o,backendName:"webgl",kernelFunc:G7},q7=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${s}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},z7=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new P7(u,a,i,o),c=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=he({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},M7={kernelName:$o,backendName:"webgl",kernelFunc:z7};function Md(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=Md({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Rr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var L7={kernelName:Ho,backendName:"webgl",kernelFunc:Md};function l2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=l2({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Rr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var B7={kernelName:To,backendName:"webgl",kernelFunc:l2};function V7(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Y1({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var W7={kernelName:_o,backendName:"webgl",kernelFunc:V7},U7=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=rt(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
+    `}},j7=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new q7(u,a,i,o),c=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=he({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},K7={kernelName:Ao,backendName:"webgl",kernelFunc:j7};function Bd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=Bd({inputs:{x:r},backend:n}),i=Jp({inputs:{input:s},backend:n}),o=Bd({inputs:{x:i},backend:n}),u=Dr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var X7={kernelName:qo,backendName:"webgl",kernelFunc:Bd};function f2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=f2({inputs:{x:r},backend:n}),i=Jp({inputs:{input:s},backend:n}),o=Bd({inputs:{x:i},backend:n}),u=Dr({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Y7={kernelName:_o,backendName:"webgl",kernelFunc:f2};function Q7(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Km({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Km({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=t2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var Z7={kernelName:Eo,backendName:"webgl",kernelFunc:Q7},J7=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=rt(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3804,7 +3804,7 @@ return a / b;`,b7=`
           setOutput(getX(${o}));
         }
       }
-    `}},G7=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=ln("rc",s),u=ln("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;
+    `}},eJ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=rt(s),a=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=ln("rc",s),u=ln("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;
        if(${l}) {
       `,s===1?"":`}
        rc = outputLoc;
@@ -3828,7 +3828,7 @@ return a / b;`,b7=`
         ${h}
         setOutput(result);
       }
-    `}},c2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return ec({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new G7(r.shape,a,i):new U7(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},H7={kernelName:Qa,backendName:"webgl",kernelFunc:c2},q7=`
+    `}},m2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return ec({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new eJ(r.shape,a,i):new J7(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},tJ={kernelName:Ja,backendName:"webgl",kernelFunc:m2},nJ=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3837,7 +3837,7 @@ return a / b;`,b7=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,j7=`
+`,sJ=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3851,11 +3851,11 @@ return a / b;`,b7=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+jp+`
+  `+Xp+`
   return result;
-`,K7=jt({opSnippet:q7,packedOpSnippet:j7}),X7={kernelName:Za,backendName:"webgl",kernelFunc:K7};function Y7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=nX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=cp(r.dtype),v=xi(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Q7={kernelName:Ao,backendName:"webgl",kernelFunc:Y7},d2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Z7={kernelName:Il,backendName:"webgl",kernelFunc:d2},J7="return 1.0 / x;",eJ=Ke({opSnippet:J7}),tJ={kernelName:Sl,backendName:"webgl",kernelFunc:eJ},nJ=ss+`
+`,rJ=jt({opSnippet:nJ,packedOpSnippet:sJ}),aJ={kernelName:ei,backendName:"webgl",kernelFunc:rJ};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=S.getAxesPermutation(c,o),d=r;p!=null&&(d=qt({inputs:{x:r},backend:n,attrs:{perm:p}}),c=S.getInnerMostAxes(c.length,o),u.push(d)),S.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=pX(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=S.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=pp(r.dtype),v=Ii(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=S.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var oJ={kernelName:ni,backendName:"webgl",kernelFunc:iJ},g2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=hX(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},uJ={kernelName:Il,backendName:"webgl",kernelFunc:g2},lJ="return 1.0 / x;",cJ=Ke({opSnippet:lJ}),dJ={kernelName:Sl,backendName:"webgl",kernelFunc:cJ},pJ=ss+`
   return (x < 0.0) ? 0.0 : x;
-`,sJ=`
+`,hJ=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3865,9 +3865,9 @@ return a / b;`,b7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,rJ=Ke({opSnippet:nJ,packedOpSnippet:sJ}),aJ={kernelName:ei,backendName:"webgl",kernelFunc:rJ},iJ=ss+`
+`,fJ=Ke({opSnippet:pJ,packedOpSnippet:hJ}),mJ={kernelName:si,backendName:"webgl",kernelFunc:fJ},gJ=ss+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,oJ=`
+`,bJ=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3877,7 +3877,7 @@ return a / b;`,b7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,uJ=Ke({opSnippet:iJ,packedOpSnippet:oJ}),lJ={kernelName:ni,backendName:"webgl",kernelFunc:uJ},cJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,yJ=Ke({opSnippet:gJ,packedOpSnippet:bJ}),vJ={kernelName:ai,backendName:"webgl",kernelFunc:yJ},xJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0]/c[0]},
           ${l[1]/c[1]});
@@ -3910,7 +3910,7 @@ return a / b;`,b7=`
 
         setOutput(newValue);
       }
-    `}},dJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},wJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${l[0]/c[0]},
           ${l[1]/c[1]},
@@ -3987,7 +3987,7 @@ return a / b;`,b7=`
 
         setOutput(newValue);
       }
-    `}};function pJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new dJ(r.shape,u,l,a,i):new cJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],"float32")}var hJ={kernelName:ti,backendName:"webgl",kernelFunc:pJ},fJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function kJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new wJ(r.shape,u,l,a,i):new xJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],"float32")}var IJ={kernelName:ri,backendName:"webgl",kernelFunc:kJ},SJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4068,7 +4068,7 @@ return a / b;`,b7=`
 
         setOutput(accumulator);
       }
-    `}};function mJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new fJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var gJ={kernelName:Tg,backendName:"webgl",kernelFunc:mJ},bJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function CJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new SJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var NJ={kernelName:_g,backendName:"webgl",kernelFunc:CJ},TJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0]/c[0]},
           ${l[1]/c[1]});
@@ -4090,7 +4090,7 @@ return a / b;`,b7=`
 
         setOutput(newValue);
       }
-    `}},yJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},$J=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${l[0]/c[0]},
           ${l[1]/c[1]},
@@ -4131,7 +4131,7 @@ return a / b;`,b7=`
 
         setOutput(newValue);
       }
-    `}};function vJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new yJ(r.shape,u,l,a,i):new bJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var xJ={kernelName:Cl,backendName:"webgl",kernelFunc:vJ},wJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function _J(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new $J(r.shape,u,l,a,i):new TJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var AJ={kernelName:Cl,backendName:"webgl",kernelFunc:_J},EJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4201,7 +4201,7 @@ return a / b;`,b7=`
 
         setOutput(accumulator);
       }
-    `}};function kJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new wJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var IJ={kernelName:Ng,backendName:"webgl",kernelFunc:kJ},SJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function RJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new EJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var DJ={kernelName:$g,backendName:"webgl",kernelFunc:RJ},FJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -4211,7 +4211,7 @@ return a / b;`,b7=`
         ${a} coords = getOutputCoords();
         setOutput(getX(${r}));
       }
-    `}},CJ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=ln("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=rt(n);n===1?this.userCode=`
+    `}},OJ=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=ln("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=rt(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -4239,7 +4239,7 @@ return a / b;`,b7=`
           }
           setOutput(result);
         }
-    `;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function NJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return kn({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new CJ(r.shape,o):new SJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var TJ={kernelName:Ro,backendName:"webgl",kernelFunc:NJ},$J=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
+    `;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function PJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return kn({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new OJ(r.shape,o):new FJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var zJ={kernelName:Do,backendName:"webgl",kernelFunc:PJ},MJ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4258,7 +4258,7 @@ return a / b;`,b7=`
           }
           setOutput(outputValue);
         }
-    `}},_J={kernelName:qo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new $J(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},AJ=`
+    `}},LJ={kernelName:jo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new MJ(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},BJ=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4273,7 +4273,7 @@ return a / b;`,b7=`
       return base + 1.0;
     }
   }
-`,EJ=Ke({opSnippet:AJ}),RJ={kernelName:Do,backendName:"webgl",kernelFunc:EJ},DJ="return inversesqrt(x);",FJ=Ke({opSnippet:DJ,cpuKernelImpl:rX}),OJ={kernelName:si,backendName:"webgl",kernelFunc:FJ},p2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=rt(r.length),u=rt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+`,VJ=Ke({opSnippet:BJ}),WJ={kernelName:Fo,backendName:"webgl",kernelFunc:VJ},UJ="return inversesqrt(x);",GJ=Ke({opSnippet:UJ,cpuKernelImpl:fX}),HJ={kernelName:ii,backendName:"webgl",kernelFunc:GJ},b2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=rt(r.length),u=rt(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${r});
 
         void main() {
@@ -4293,7 +4293,7 @@ return a / b;`,b7=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function PJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new p2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var zJ={kernelName:Fo,backendName:"webgl",kernelFunc:PJ},MJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=rt(n);this.userCode=`
+      `}};function qJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new b2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var jJ={kernelName:Oo,backendName:"webgl",kernelFunc:qJ},KJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=rt(n);this.userCode=`
       void main() {
         ${a} resRC = getOutputCoords();
         float cVal = getC(${s});
@@ -4303,15 +4303,15 @@ return a / b;`,b7=`
           setOutput(getB(${r}));
         }
       }
-    `}};function LJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new MJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var BJ={kernelName:Oo,backendName:"webgl",kernelFunc:LJ},VJ=`
+    `}};function XJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new KJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var YJ={kernelName:Po,backendName:"webgl",kernelFunc:XJ},QJ=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${S.SELU_SCALEALPHA};
   float scale = ${S.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:Nl,backendName:"webgl",kernelFunc:WJ},GJ=ou+`
+`,ZJ=Ke({opSnippet:QJ}),JJ={kernelName:Nl,backendName:"webgl",kernelFunc:ZJ},eee=uu+`
   return 1.0 / (1.0 + exp(-1.0 * x));
-`,HJ=`
+`,tee=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
   bvec4 isNaN = isnan(x);
 
@@ -4321,15 +4321,15 @@ return a / b;`,b7=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,qJ=Ke({opSnippet:GJ,packedOpSnippet:HJ,cpuKernelImpl:aX}),jJ={kernelName:ai,backendName:"webgl",kernelFunc:qJ},KJ=`
+`,nee=Ke({opSnippet:eee,packedOpSnippet:tee,cpuKernelImpl:mX}),see={kernelName:ui,backendName:"webgl",kernelFunc:nee},ree=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,XJ=Ke({opSnippet:KJ}),YJ={kernelName:Tl,backendName:"webgl",kernelFunc:XJ},QJ=ou+`
+`,aee=Ke({opSnippet:ree}),iee={kernelName:Tl,backendName:"webgl",kernelFunc:aee},oee=uu+`
   return sin(x);
-`,ZJ=Ke({opSnippet:QJ}),JJ={kernelName:ri,backendName:"webgl",kernelFunc:ZJ},eee=`
+`,uee=Ke({opSnippet:oee}),lee={kernelName:oi,backendName:"webgl",kernelFunc:uee},cee=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,tee=Ke({opSnippet:eee}),nee={kernelName:zo,backendName:"webgl",kernelFunc:tee},see=`
+`,dee=Ke({opSnippet:cee}),pee={kernelName:Mo,backendName:"webgl",kernelFunc:dee},hee=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4349,17 +4349,17 @@ return a / b;`,b7=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,ree=Ke({opSnippet:see}),aee={kernelName:$l,backendName:"webgl",kernelFunc:ree},iee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=c2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},oee={kernelName:Mo,backendName:"webgl",kernelFunc:iee};function uee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,fee=Ke({opSnippet:hee}),mee={kernelName:$l,backendName:"webgl",kernelFunc:fee},gee=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=m2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=qt({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},bee={kernelName:Lo,backendName:"webgl",kernelFunc:gee};function yee(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
          ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
          ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
          ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=oX(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var lee={kernelName:sp,backendName:"webgl",kernelFunc:uee};function cee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=uX(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var dee={kernelName:_l,backendName:"webgl",kernelFunc:cee};function pee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=bX(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var vee={kernelName:ap,backendName:"webgl",kernelFunc:yee};function xee(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=yX(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var wee={kernelName:_l,backendName:"webgl",kernelFunc:xee};function kee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-              ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=F1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var hee={kernelName:rp,backendName:"webgl",kernelFunc:pee};function fee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=L1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var Iee={kernelName:ip,backendName:"webgl",kernelFunc:kee};function See(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=F1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var mee={kernelName:ap,backendName:"webgl",kernelFunc:fee};function gee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=new p2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var bee={kernelName:ip,backendName:"webgl",kernelFunc:gee};function yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=uu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var vee={kernelName:Lo,backendName:"webgl",kernelFunc:yee},Tw="return sqrt(x);",xee=Ke({opSnippet:Tw,packedOpSnippet:Tw,cpuKernelImpl:lX}),wee={kernelName:ii,backendName:"webgl",kernelFunc:xee},kee="return x * x;",Iee=Ke({opSnippet:kee}),See={kernelName:Al,backendName:"webgl",kernelFunc:Iee},$w="return (a - b) * (a - b);",Cee=jt({opSnippet:$w,packedOpSnippet:$w}),Nee={kernelName:li,backendName:"webgl",kernelFunc:Cee};function Tee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ss+`
+             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=L1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var Cee={kernelName:op,backendName:"webgl",kernelFunc:See};function Nee(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=new b2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var Tee={kernelName:up,backendName:"webgl",kernelFunc:Nee};function $ee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=lu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var _ee={kernelName:Bo,backendName:"webgl",kernelFunc:$ee},$w="return sqrt(x);",Aee=Ke({opSnippet:$w,packedOpSnippet:$w,cpuKernelImpl:vX}),Eee={kernelName:li,backendName:"webgl",kernelFunc:Aee},Ree="return x * x;",Dee=Ke({opSnippet:Ree}),Fee={kernelName:Al,backendName:"webgl",kernelFunc:Dee},_w="return (a - b) * (a - b);",Oee=jt({opSnippet:_w,packedOpSnippet:_w}),Pee={kernelName:pi,backendName:"webgl",kernelFunc:Oee};function zee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ss+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,a=new Hs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var $ee={kernelName:hi,backendName:"webgl",kernelFunc:Tee},_ee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=rt(n.length),a=rt(n.length),i="";if(s===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
+  `,a=new Hs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Mee={kernelName:gi,backendName:"webgl",kernelFunc:zee},Lee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=rt(n.length),a=rt(n.length),i="";if(s===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
       ${r} begin = ${r}(${e});
       ${r} strides = ${r}(${t});
 
@@ -4367,15 +4367,15 @@ return a / b;`,b7=`
         ${a} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function Aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let N=wt.computeOutShape(y,v,x),E=uu({inputs:{x:r},backend:n,attrs:{begin:y,size:N}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=cX(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new _ee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let T=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),T}var Eee={kernelName:Bo,backendName:"webgl",kernelFunc:Aee};function Ree(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=dX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Dee={kernelName:op,backendName:"webgl",kernelFunc:Ree};function Fee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=pX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var Oee={kernelName:$g,backendName:"webgl",kernelFunc:Fee};function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=hX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var zee={kernelName:_g,backendName:"webgl",kernelFunc:Pee},Mee="return tan(x);",Lee=Ke({opSnippet:Mee}),Bee={kernelName:Vo,backendName:"webgl",kernelFunc:Lee},Vee=`
+    `}};function Bee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let N=wt.computeOutShape(y,v,x),E=lu({inputs:{x:r},backend:n,attrs:{begin:y,size:N}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=xX(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new Lee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let T=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),T}var Vee={kernelName:Vo,backendName:"webgl",kernelFunc:Bee};function Wee(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=wX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Uee={kernelName:lp,backendName:"webgl",kernelFunc:Wee};function Gee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=kX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var Hee={kernelName:Ag,backendName:"webgl",kernelFunc:Gee};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=IX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var jee={kernelName:Eg,backendName:"webgl",kernelFunc:qee},Kee="return tan(x);",Xee=Ke({opSnippet:Kee}),Yee={kernelName:Wo,backendName:"webgl",kernelFunc:Xee},Qee=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,Wee=Ke({opSnippet:Vee}),Uee={kernelName:di,backendName:"webgl",kernelFunc:Wee},Gee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=Hee(e);this.userCode=`
+`,Zee=Ke({opSnippet:Qee}),Jee={kernelName:fi,backendName:"webgl",kernelFunc:Zee},ete=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=rt(this.rank),r=tte(e);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function Hee(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function h2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=mX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Gee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var qee={kernelName:Cr,backendName:"webgl",kernelFunc:h2},jee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
+    `}};function tte(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function y2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=CX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new ete(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var nte={kernelName:Nr,backendName:"webgl",kernelFunc:y2},ste=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
        void main() {
          ivec2 coords = getOutputCoords();
          int batch = coords[0];
@@ -4415,7 +4415,7 @@ return a / b;`,b7=`
             setOutput(float(i1));
          }
        }
-     `}},Kee=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
+     `}},rte=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
     void main() {
          // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
          ivec2 coords = getOutputCoords();
@@ -4449,7 +4449,7 @@ return a / b;`,b7=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function Ur(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function _w(e){let t=1;for(;t<e;)t*=2;return t}function Xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=gX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Ur(n,h);let b=_w(a),y=_w(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new jee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Ur(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Kee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,"int32",z),Ur(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=uu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Ur(n,T);let N=r2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Ur(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Ur(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Ur(n,A),[N,v]}var Yee={kernelName:Wo,backendName:"webgl",kernelFunc:Xee},Qee=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function Gr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Aw(e){let t=1;for(;t<e;)t*=2;return t}function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=NX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Gr(n,h);let b=Aw(a),y=Aw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new ste(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Gr(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new rte([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,"int32",z),Gr(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let T=v;v=lu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Gr(n,T);let N=l2({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Gr(n,g);let E=l.slice(0,-1);E.push(a),T=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Gr(n,T);let A=N;return N=he({inputs:{x:N},attrs:{shape:E},backend:n}),Gr(n,A),[N,v]}var ite={kernelName:Uo,backendName:"webgl",kernelFunc:ate},ote=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4561,7 +4561,7 @@ return a / b;`,b7=`
               }
               setOutput(outputValue);
             }
-        `}};function Zee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Qee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Jee={kernelName:Uo,backendName:"webgl",kernelFunc:Zee};function ete(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;nu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=bX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var tte={kernelName:Ag,backendName:"webgl",kernelFunc:ete};function nte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=uu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var ste={kernelName:Go,backendName:"webgl",kernelFunc:nte},rte=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
+        `}};function ute(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new ote(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var lte={kernelName:Go,backendName:"webgl",kernelFunc:ute};function cte(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;su(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=TX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var dte={kernelName:Rg,backendName:"webgl",kernelFunc:cte};function pte(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=lu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var hte={kernelName:Ho,backendName:"webgl",kernelFunc:pte},fte=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
         sumValue += dot(values, segFilter);
     `,d="";r%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${r}) {
@@ -4667,11 +4667,11 @@ return a / b;`,b7=`
         }
         setOutput(${u});
       }
-    `}};function ate(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new rte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=d2({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=h2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var ite={kernelName:up,backendName:"webgl",kernelFunc:ate},ote=[d8,h8,g8,v8,w8,S8,N8,$8,R8,F8,z8,B8,U8,j8,Y8,Z8,eY,rY,iY,uY,pY,vY,wY,IY,_Y,EY,OY,qX,MY,UY,jY,JY,t9,s9,a9,o9,c9,h9,g9,y9,x9,k9,C9,T9,E9,D9,P9,L9,V9,H9,X9,J9,nQ,aQ,iQ,uQ,cQ,pQ,fQ,gQ,xQ,IQ,NQ,$Q,EQ,FQ,MQ,WQ,HX,GQ,VY,jQ,YQ,JQ,KX,sZ,oZ,lZ,hZ,gZ,xZ,IZ,TZ,EZ,FZ,PZ,BZ,WZ,GZ,KZ,YZ,ZZ,e7,n7,i7,c7,f7,k7,JX,N7,_7,R7,O7,CY,M7,B7,W7,H7,X7,YX,Q7,Z7,NY,y7,tJ,aJ,lJ,t8,hJ,gJ,xJ,IJ,TJ,_J,RJ,OJ,zJ,BJ,UJ,jJ,YJ,JJ,nee,bY,x7,aee,oee,lee,dee,hee,mee,bee,vee,wee,See,Nee,$ee,Eee,Dee,Oee,zee,v7,u8,Bee,Uee,qee,Yee,Jee,l8,tte,ste,ite,L7];for(let e of ote)El(e);var zs=X();zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var ute="return a + b;",lte="return areal * breal - aimag * bimag;",cte="return areal * bimag + aimag * breal;",dte="return a / b;",pte="return a * b;",hte="return (a - b) * (a - b);",fte="return a - b;",mte="return f32(a == b);",gte="return vec4<f32>(a == b);",bte="return f32(a > b);",yte="return vec4<f32>(a > b);",vte="return f32(a >= b);",xte="return vec4<f32>(a >= b);",wte="return f32(a < b);",kte="return vec4<f32>(a < b);",Ite="return f32(a <= b);",Ste="return vec4<f32>(a <= b);",Cte="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Nte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
-  vec4<f32>(b >= vec4<f32>(1.0)));`,Tte=`
+    `}};function mte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=S.getAxesPermutation([l],o),p=r;c!=null&&(p=qt({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=S.getInnerMostAxes(1,o)[0]);let d=S.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=pp(r.dtype),g=(x,k,T,N,E)=>{let A=x.shape[0],P=x.shape[1],R=S.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new fte(F,k),z=n.compileAndRun($,[x,T],N);if(u.push(z),z.shape[1]===E)return z;let W=g2({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=y2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,N,E)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=S.getUndoAxesPermutation(c);v=qt({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var gte={kernelName:cp,backendName:"webgl",kernelFunc:mte},bte=[w8,I8,N8,_8,E8,F8,P8,M8,W8,G8,j8,Y8,J8,sY,iY,uY,cY,fY,gY,yY,kY,_Y,EY,DY,LY,VY,HY,n8,KY,JY,s9,l9,d9,h9,m9,b9,x9,I9,N9,$9,A9,R9,O9,z9,V9,U9,q9,X9,Q9,tQ,aQ,lQ,pQ,mQ,gQ,yQ,xQ,kQ,SQ,NQ,AQ,DQ,PQ,MQ,VQ,GQ,KQ,ZQ,t8,eZ,QY,sZ,iZ,lZ,r8,hZ,bZ,vZ,IZ,NZ,AZ,DZ,zZ,VZ,GZ,qZ,YZ,ZZ,e7,r7,i7,u7,c7,p7,g7,x7,S7,R7,l8,P7,L7,W7,H7,OY,K7,Y7,Z7,tJ,aJ,i8,oJ,uJ,PY,$7,dJ,mJ,vJ,d8,IJ,NJ,AJ,DJ,zJ,LJ,WJ,HJ,jJ,YJ,JJ,see,iee,lee,pee,TY,A7,mee,bee,vee,wee,Iee,Cee,Tee,_ee,Eee,Fee,Pee,Mee,Vee,Uee,Hee,jee,_7,y8,Yee,Jee,nte,ite,lte,v8,dte,hte,gte,X7];for(let e of bte)El(e);var zs=X();zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);var yte="return a + b;",vte="return areal * breal - aimag * bimag;",xte="return areal * bimag + aimag * breal;",wte="return a / b;",kte="return a * b;",Ite="return (a - b) * (a - b);",Ste="return a - b;",Cte="return f32(a == b);",Nte="return vec4<f32>(a == b);",Tte="return f32(a > b);",$te="return vec4<f32>(a > b);",_te="return f32(a >= b);",Ate="return vec4<f32>(a >= b);",Ete="return f32(a < b);",Rte="return vec4<f32>(a < b);",Dte="return f32(a <= b);",Fte="return vec4<f32>(a <= b);",Ote="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Pte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  vec4<f32>(b >= vec4<f32>(1.0)));`,zte=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
-  `,f2=`
+  `,v2=`
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -4684,12 +4684,12 @@ return a / b;`,b7=`
   if (isNaN.a) {
     resultTemp.a = uniforms.NAN;
   }
-  `,$te=`
+  `,Mte=`
   let s = sign(a) * sign(b);
   let ia = i32(round(a));
   let ib = i32(round(b));
   return f32(idiv(ia, ib, s));
-  `,_te=`
+  `,Lte=`
   let ia = vec4<i32>(round(a));
   let ib = vec4<i32>(round(b));
   let cond = ib != vec4<i32>(0);
@@ -4710,7 +4710,7 @@ return a / b;`,b7=`
     resultTemp[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4<f32>(resultTemp);
-  `,Ate="return f32(a != b);",Ete="return vec4<f32>(a != b);",Rte=`
+  `,Bte="return f32(a != b);",Vte="return vec4<f32>(a != b);",Wte=`
   if(a < 0.0 && floor(b) < b) {
     return uniforms.NAN;
   }
@@ -4721,7 +4721,7 @@ return a / b;`,b7=`
     return pow(abs(a), b);
   }
   return sign(a) * pow(abs(a), b);
-  `,Dte=`
+  `,Ute=`
   let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
   let isModRound1 = vec4<f32>(isModRound1Bool);
   let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
@@ -4742,22 +4742,22 @@ return a / b;`,b7=`
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${f2}
+  ${v2}
   return resultTemp;
-  `,Fte="if (a < 0.0) { return b * a; }  return a;",Ote=`
+  `,Gte="if (a < 0.0) { return b * a; }  return a;",Hte=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function Aw(e,t){let n=t?f2:Tte;return t?`
+  `;function Ew(e,t){let n=t?v2:zte;return t?`
     var resultTemp = vec4<f32>(${e}(a, b));
     let isNaN = isnanVec4(a) | isnanVec4(b);
     `+n+`
     return resultTemp;
   `:n+`
     return ${e}(a, b);
-  `}function tc(e,t){switch(e){case 0:return pte;case 1:return ute;case 2:return fte;case 3:return dte;case 4:return t?gte:mte;case 5:return t?yte:bte;case 6:return t?xte:vte;case 7:return t?kte:wte;case 8:return t?Ste:Ite;case 9:return t?Nte:Cte;case 10:return t?Ete:Ate;case 11:return hte;case 12:return t?_te:$te;case 14:return t?Ote:Fte;case 15:return Aw("max",t);case 16:return Aw("min",t);case 13:return t?Dte:Rte;case 17:return lte;case 18:return cte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Pte="return abs(a);",zte="return ceil(a);",Mte="return cos(a);",Lte=`
+  `}function tc(e,t){switch(e){case 0:return kte;case 1:return yte;case 2:return Ste;case 3:return wte;case 4:return t?Nte:Cte;case 5:return t?$te:Tte;case 6:return t?Ate:_te;case 7:return t?Rte:Ete;case 8:return t?Fte:Dte;case 9:return t?Pte:Ote;case 10:return t?Vte:Bte;case 11:return Ite;case 12:return t?Lte:Mte;case 14:return t?Hte:Gte;case 15:return Ew("max",t);case 16:return Ew("min",t);case 13:return t?Ute:Wte;case 17:return vte;case 18:return xte;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var qte="return abs(a);",jte="return ceil(a);",Kte="return cos(a);",Xte=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
-`,Bte="return exp(a) - 1.0;",Vte="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Wte=`
+`,Yte="return exp(a) - 1.0;",Qte="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Zte=`
   var resFloat = exp(a) - vec4<f32>(1.0);
   if (a.r >= 0.0) {
     resFloat.r = a.r;
@@ -4772,11 +4772,11 @@ return a / b;`,b7=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,Ute="return exp(a);",Gte="return floor(a);",Hte="return a;",qte=`if (a < 0.0) { return 1.0/0.0; }
-  return log(a);`,jte="return f32(!(a >= 1.0));",Kte="return -a;",Xte="if (a < 0.0) { return uniforms.alpha * a; } return a;",Yte=`
+`,Jte="return exp(a);",ene="return floor(a);",tne="return a;",nne=`if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`,sne="return f32(!(a >= 1.0));",rne="return -a;",ane="if (a < 0.0) { return uniforms.alpha * a; } return a;",ine=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`,Qte="if(a < 0.0) { return 0.0; } return a;",Zte="return clamp(a, 0.0, 6.0);",Jte="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",ene=`
+`,one="if(a < 0.0) { return 0.0; } return a;",une="return clamp(a, 0.0, 6.0);",lne="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",cne=`
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
   let isNaN = isnanVec4(a);
 
@@ -4793,16 +4793,16 @@ return a / b;`,b7=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,tne="return 1.0/sqrt(a);",nne="return 1.0 / (1.0 + exp(-1.0 * a));",sne="return sin(a);",rne=`
+`,dne="return 1.0/sqrt(a);",pne="return 1.0 / (1.0 + exp(-1.0 * a));",hne="return sin(a);",fne=`
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
-`,ane="return sqrt(a);",ine="return a * a;",one=`
+`,mne="return sqrt(a);",gne="return a * a;",bne=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,une="return f32(i32((a)));";function Hr(e,t){switch(e){case 0:return Pte;case 2:return Mte;case 3:return Lte;case 1:return zte;case 4:return t?Wte:Vte;case 5:return Ute;case 6:return Bte;case 7:return Gte;case 8:return Hte;case 9:return qte;case 10:return jte;case 11:return Kte;case 14:return t?Yte:Xte;case 12:return t?ene:Qte;case 13:return t?Jte:Zte;case 15:return tne;case 18:return nne;case 16:return sne;case 17:return rne;case 19:return ane;case 20:return ine;case 21:return one;case 22:return une;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e==="linear")return Hr(8);if(e==="relu")return Hr(12,t);if(e==="elu")return Hr(4,t);if(e==="relu6")return Hr(13,t);if(e==="prelu")return tc(14,t);if(e==="sigmoid")return Hr(18,t);if(e==="leakyrelu")return Hr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function lne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Sv(){return`
+`,yne="return f32(i32((a)));";function qr(e,t){switch(e){case 0:return qte;case 2:return Kte;case 3:return Xte;case 1:return jte;case 4:return t?Zte:Qte;case 5:return Jte;case 6:return Yte;case 7:return ene;case 8:return tne;case 9:return nne;case 10:return sne;case 11:return rne;case 14:return t?ine:ane;case 12:return t?cne:one;case 13:return t?lne:une;case 15:return dne;case 18:return pne;case 16:return hne;case 17:return fne;case 19:return mne;case 20:return gne;case 21:return bne;case 22:return yne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Js(e,t=!1){if(e===null)return null;if(e==="linear")return qr(8);if(e==="relu")return qr(12,t);if(e==="elu")return qr(4,t);if(e==="relu6")return qr(13,t);if(e==="prelu")return tc(14,t);if(e==="sigmoid")return qr(18,t);if(e==="leakyrelu")return qr(14,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function vne(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Nv(){return`
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
-`}function Dr(){return`
-  ${Sv()}
+`}function Fr(){return`
+  ${Nv()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
@@ -4810,9 +4810,9 @@ return a / b;`,b7=`
     globalId = GlobalId;
     numWorkgroups = NumWorkgroups;
 `}function Ue(){return`
-    ${Dr()}
+    ${Fr()}
       let index = getGlobalIndex();
-`}function cne(e,t,n,s=!1){let r=[];if(r.push(`
+`}function xne(e,t,n,s=!1){let r=[];if(r.push(`
     let workGroupSizeX = ${n.workGroupSize[0]}u;
     let workGroupSizeY = ${n.workGroupSize[1]}u;
     let workGroupSizeZ = ${n.workGroupSize[2]}u;
@@ -4847,8 +4847,8 @@ return a / b;`,b7=`
 
       @group(0) @binding(0) var<storage, write> result: array<${cd(t.dtype,n.isVec4)}>;
       @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `),[Ew,r.join(`
-`),Rw(t.shape),n.getUserCode()].join(`
+    `),[Rw,r.join(`
+`),Dw(t.shape),n.getUserCode()].join(`
 `);let a="struct Uniforms { NAN : f32, ";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}, `}),a+=`outShape : ${Wt(t.shape.length)}, `;let i=t.shape.length-1;a+=`
        outShapeStrides: ${Wt(i)}, `,n.size&&(a+="size : i32, "),n.uniforms&&(a+=n.uniforms),a+="};",r.push(a),n.atomic?r.push(`
     @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
@@ -4858,10 +4858,10 @@ return a / b;`,b7=`
     @group(0) @binding(${1+d}) var<storage, read> ${p}: array<${cd(e[d].dtype,n.isVec4)}>;
     `)}),a!==""&&r.push(`
     @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
-    `);let[o,u]=gne(t.shape,n.dispatchLayout),l=[Ew,r.join(`
-`),Rw(t.shape),o,dne(t.shape.length)];if(n.atomic||l.push(pne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>hne(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
+    `);let[o,u]=Nne(t.shape,n.dispatchLayout),l=[Rw,r.join(`
+`),Dw(t.shape),o,wne(t.shape.length)];if(n.atomic||l.push(kne(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>Ine(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
 `);l.push(p)}return l.push(n.getUserCode()),l.join(`
-`)}var Ew=`
+`)}var Rw=`
   // Checks whether coordinates lie within the bounds of the shape.
   fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
     return all(coord >= vec2<i32>(0)) && all(coord < shape);
@@ -4908,7 +4908,7 @@ return a / b;`,b7=`
   fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
-`;function dne(e){let t="";switch(e){case 0:case 1:t+=`
+`;function wne(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
           return coords;
         }
@@ -4925,7 +4925,7 @@ return a / b;`,b7=`
           return dot(coords, vec4<i32>(
             uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
         }
-        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function pne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function kne(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4953,7 +4953,7 @@ return a / b;`,b7=`
         let flatIndex = getOutputIndexFromCoords(${o}(${i.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return a}function hne(e,t,n,s){let r=fne(e,n);return e.shape.length<=t.length&&(r+=mne(e,t,n,s)),r}function fne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),i=["d0","d1","d2","d3"].slice(0,s),o=i.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
+    `}return a}function Ine(e,t,n,s){let r=Sne(e,n);return e.shape.length<=t.length&&(r+=Cne(e,t,n,s)),r}function Sne(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),i=["d0","d1","d2","d3"].slice(0,s),o=i.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
         fn ${a}() -> vec4<f32> {
           return vec4<f32>(${n}[0]);
         }
@@ -4971,7 +4971,7 @@ return a / b;`,b7=`
       return f32(${n}[getIndexFromCoords${l}(${r}(${i.join(",")}),
         ${u})]);
     }
-   `}function mne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i="get"+a+"ByOutput",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
+   `}function Cne(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i="get"+a+"ByOutput",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`
         fn ${i}Index(globalIndex : i32) -> vec4<f32> {
           return vec4<f32>(${r}[globalIndex]);
         }
@@ -5028,13 +5028,13 @@ return a / b;`,b7=`
       ${d}
       return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
     }
-  `}function gne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
+  `}function Nne(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{
       let globalIndex = getGlobalIndex();
       return getCoordsFromIndex(globalIndex);
     }
-    `,a];let i="",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=lne(h,"uniforms.outShape");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
+    `,a];let i="",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=vne(h,"uniforms.outShape");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
     ${i}
-  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function Rw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function Dw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
       let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
       return vec2<i32>(d0, d1);
     }`;let a="var index2 = index;"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join("");return`
@@ -5042,7 +5042,7 @@ return a / b;`,b7=`
       ${a}
       return ${s}(${r.join(",")});
     }
-  `}var m2={};Ae(m2,{ArrayBufferToTypedArray:()=>b2,GPUBytesPerElement:()=>jm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Cv,computeWorkGroupSizeForMatMul:()=>g2,computeWorkPerThreadForConv2d:()=>Nv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Tv,tilesFitEvenlyIntoShape:()=>Ks});var na=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(na(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(na(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(na(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Cv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function g2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Nv(e,t){let n=na(e.x.map(r=>t[r])),s=na(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function jm(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function b2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Tv(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function y2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
+  `}var x2={};Ae(x2,{ArrayBufferToTypedArray:()=>k2,GPUBytesPerElement:()=>Xm,computeDispatch:()=>$e,computeWorkGroupSizeForConv2d:()=>Tv,computeWorkGroupSizeForMatMul:()=>w2,computeWorkPerThreadForConv2d:()=>$v,flatDispatchLayout:()=>Le,isWebGPUSupported:()=>_v,tilesFitEvenlyIntoShape:()=>Ks});var sa=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function $e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(sa(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(sa(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(sa(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function Tv(e,t){let n=sa(e.x.map(r=>t[r])),s=sa(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function w2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function $v(e,t){let n=sa(e.x.map(r=>t[r])),s=sa(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Le(e){return{x:e.map((t,n)=>n)}}function Xm(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function k2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function _v(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function I2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
   var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;
   var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
 
@@ -5050,7 +5050,7 @@ return a / b;`,b7=`
   let ColPerThread = ${e[0]};
   let TileInner = ${s};
 
-  ${Dr()}
+  ${Fr()}
 
     let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
     let tileCol = i32(localId.x);
@@ -5109,13 +5109,13 @@ return a / b;`,b7=`
                  globalCol,
                  acc[innerRow], globalId);
     }
-  }`}var bne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  }`}var Tne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
         }
         return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
              return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
         }
-        return vec4<f32>(0.0)`,n="",s="";if(this.activation){let i=Zs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+        return vec4<f32>(0.0)`,n="",s="";if(this.activation){let i=Js(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
                   let b = getPreluActivationWeightsByOutputCoords(outCoord);
                   ${i}
                 }`:n=`
@@ -5146,11 +5146,11 @@ return a / b;`,b7=`
           setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
         }
       }
-      ${y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
-    `}};function $v(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
+      ${I2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
+    `}};function Av(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
     var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
     var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
-    ${Dr()}
+    ${Fr()}
       let tileRow = i32(localId.y) * ${e[1]};
       let tileCol = i32(localId.x) * ${e[0]};
 
@@ -5231,11 +5231,11 @@ return a / b;`,b7=`
         }
       }
     }
-  `}function yne(e){return`
+  `}function $ne(e){return`
     let TileSize = ${e[0]*4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
-    ${Dr()}
+    ${Fr()}
       let tileCol = i32(localId.x);
       let globalCol = i32(globalId.x);
       let globalRow = i32(globalId.y);
@@ -5274,7 +5274,7 @@ return a / b;`,b7=`
         mm_write(globalRow, globalCol, acc, globalId);
       }
     }
-  `}var v2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=g2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var S2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=w2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push("bias"),c&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
              return A[batch * batchASize + row * uniforms.dimInner + col];
            }
            return 0.0;`:e=this.fitA?"return A[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
@@ -5286,7 +5286,7 @@ return a / b;`,b7=`
            return 0.0;`:t=this.fitB?"return B[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
              return B[batch * batchBSize + col * uniforms.dimInner + row];
            }
-           return 0.0;`;let n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+           return 0.0;`;let n="",s="";if(this.activation){let i=Js(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${i}
             }`:n=`
@@ -5316,10 +5316,10 @@ return a / b;`,b7=`
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1]>1?$v([this.workPerThread,this.workPerThread,1],this.workGroupSize):yne(this.workGroupSize)}
-    `}};function vne(){return`
+      ${this.outputShape[1]>1?Av([this.workPerThread,this.workPerThread,1],this.workGroupSize):$ne(this.workGroupSize)}
+    `}};function _ne(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
-    ${Dr()}
+    ${Fr()}
       let coords = getOutputCoords();
       let batch = coords[0];
       let row = coords[1];
@@ -5348,7 +5348,7 @@ return a / b;`,b7=`
         mm_write(batch, row, col, sum);
       }
     }
-  `}var xne=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+  `}var Ane=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A[batch * batchASize + row * uniforms.dimInner + col];":e="return A[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let i=Js(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${i}
             }`:n=`
@@ -5375,8 +5375,8 @@ return a / b;`,b7=`
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${vne()}
-    `}};function wne(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
+      ${_ne()}
+    `}};function Ene(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
   var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
   var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
   var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
@@ -5388,7 +5388,7 @@ return a / b;`,b7=`
   // arithmetic operations and others handle IO operations between barrier api,
   // makes ALUs and load/store units work simultaneously, could improves
   // the performance.
-  ${Dr()}
+  ${Fr()}
     let tileRow = i32(localId.y);
     let tileCol = i32(localId.x);
     let globalRow = i32(globalId.y);
@@ -5463,13 +5463,13 @@ return a / b;`,b7=`
       mm_write(writeCol, globalCol, acc, globalId);
     }
   }
-  `}var kne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push("bias");let o=a!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var Rne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push("bias");let o=a!=null;o&&this.variableNames.push("preluActivationWeights"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
           return A[batch * batchASize + row * uniforms.dimInner + col];
         }
         return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
            return B[batch * batchBSize + row * uniforms.dimBOuter + col];
          }
-         return 0.0;`,n="",s="";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+         return 0.0;`,n="",s="";if(this.activation){let i=Js(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
             let b = getPreluActivationWeightsByOutputCoords(outCoord);
             ${i}
             }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
@@ -5497,8 +5497,8 @@ return a / b;`,b7=`
           setOutputAtCoords(batch, row, col, value);
         }
       }
-      ${wne(this.workGroupSize)}
-    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Ine={kernelName:Eo,backendName:"webgpu",kernelFunc:Me};function _v({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new xne([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new kne(k,T,[P,h,f],a,u,i):R?F=new bne(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new v2(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];u==="leakyrelu"&&(z.push({type:"float32",data:[o]}),F.uniforms+=" alpha : f32,");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Sne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return _v({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var Cne={kernelName:ra,backendName:"webgpu",kernelFunc:Sne},Dw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+      ${Ene(this.workGroupSize)}
+    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Dne={kernelName:Ro,backendName:"webgpu",kernelFunc:Me};function Ev({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=Ko.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],T=s?[y,f,d]:[y,d,f],N=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:T}}),A=[N,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new Ane([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new Rne(k,T,[P,h,f],a,u,i):R?F=new Tne(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new S2(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[N,E];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];u==="leakyrelu"&&(z.push({type:"float32",data:[o]}),F.uniforms+=" alpha : f32,");let W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function Fne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Ev({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var One={kernelName:aa,backendName:"webgpu",kernelFunc:Fne},Fw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
         ${tc(this.op,!1)}
@@ -5513,7 +5513,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
         }
       }
-    `}},Nne=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+    `}},Pne=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
@@ -5540,7 +5540,7 @@ return a / b;`,b7=`
             }
           }
         }
-        `}},Tne=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
+        `}},zne=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
         ${tc(this.op,this.isVec4)}
       }
@@ -5551,7 +5551,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-    `}},x2=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
+    `}},C2=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=S.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
       fn binaryOperation(a : f32, b : f32) -> f32 {
         ${tc(this.op,!1)}
       }
@@ -5562,9 +5562,9 @@ return a / b;`,b7=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function Fw(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new Tne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Nne(e,t,n,a):new x2(e,t,n)}function es(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var $ne={kernelName:Ba,backendName:"webgpu",kernelFunc:es};function lu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=es({inputs:{x:s},backend:n}),u=es({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var _ne={kernelName:jd,backendName:"webgpu",kernelFunc:lu},nc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function Ow(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new zne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Pne(e,t,n,a):new C2(e,t,n)}function Wn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Mne={kernelName:Wa,backendName:"webgpu",kernelFunc:Wn};function cu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=Wn({inputs:{x:s},backend:n}),u=Wn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var Lne={kernelName:Xd,backendName:"webgpu",kernelFunc:cu},nc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
-        ${Hr(this.op,!1)}
+        ${qr(this.op,!1)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -5572,7 +5572,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Dw(17,i.shape,o.shape),b=new Dw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=lu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?S.fromUint8ToStringArray(p):p,f=i.dtype==="string"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Fw(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:Ane,ceilImpl:Ene,concatImpl:Rne,equalImpl:Dne,expImpl:Fne,expm1Impl:One,floorImpl:Pne,gatherNdImpl:zne,gatherV2Impl:Mne,greaterEqualImpl:Lne,greaterImpl:Bne,lessEqualImpl:Vne,lessImpl:Wne,logImpl:Une,maxImpl:Gne,maximumImpl:Hne,minimumImpl:qne,multiplyImpl:jne,negImpl:Kne,notEqualImpl:Xne,prodImpl:Yne,rangeImpl:Qne,rsqrtImpl:Zne,simpleAbsImpl:Jne,sliceImpl:ese,stridedSliceImpl:tse,stringNGramsImpl:nse,subImpl:sse,tileImpl:rse,topKImpl:ase,transposeImpl:ise,uniqueImpl:Mpe}=Zy,ose=Kt({opType:0,cpuKernelImpl:Jne}),use={kernelName:io,backendName:"webgpu",kernelFunc:ose},lse=mn({opSnippet:1,cpuKernelImpl:Ane,supportsComplex:!0}),cse={kernelName:Ir,backendName:"webgpu",kernelFunc:lse},dse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
+      `}};function Kt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function mn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=Ow(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],cn(b.dtype,y.dtype))});else{let g=new Fw(17,i.shape,o.shape),b=new Fw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=cu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||cn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?S.fromUint8ToStringArray(p):p,f=i.dtype==="string"?S.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=Ow(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:Bne,ceilImpl:Vne,concatImpl:Wne,equalImpl:Une,expImpl:Gne,expm1Impl:Hne,floorImpl:qne,gatherNdImpl:jne,gatherV2Impl:Kne,greaterEqualImpl:Xne,greaterImpl:Yne,lessEqualImpl:Qne,lessImpl:Zne,logImpl:Jne,maxImpl:ese,maximumImpl:tse,minimumImpl:nse,multiplyImpl:sse,negImpl:rse,notEqualImpl:ase,prodImpl:ise,rangeImpl:ose,rsqrtImpl:use,simpleAbsImpl:lse,sliceImpl:cse,stridedSliceImpl:dse,stringNGramsImpl:pse,subImpl:hse,tileImpl:fse,topKImpl:mse,transposeImpl:gse,uniqueImpl:Zpe}=ev,bse=Kt({opType:0,cpuKernelImpl:lse}),yse={kernelName:uo,backendName:"webgpu",kernelFunc:bse},vse=mn({opSnippet:1,cpuKernelImpl:Bne,supportsComplex:!0}),xse={kernelName:Sr,backendName:"webgpu",kernelFunc:vse},wse=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
       ${Ue()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5584,7 +5584,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function pse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return es({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new dse(a);return n.runWebGPUProgram(i,s,r)}var hse={kernelName:ka,backendName:"webgpu",kernelFunc:pse},w2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+    `}};function kse(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Wn({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>cn(o,u)),a=s.map(o=>o.shape),i=new wse(a);return n.runWebGPUProgram(i,s,r)}var Ise={kernelName:Ia,backendName:"webgpu",kernelFunc:kse},N2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32, infinityValue : f32,",this.size=!0;let s=[t];S.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=S.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
     `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${r}]`;return`
@@ -5664,10 +5664,10 @@ return a / b;`,b7=`
           setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
         }
       }
-    `}},fse=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+    `}},Sse=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${Sv()}
+      ${Nv()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5686,7 +5686,7 @@ return a / b;`,b7=`
             [localId.y]);
         }
       }
-    `}},mse=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=gse(this.newDim);return`
+    `}},Cse=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=Nse(this.newDim);return`
       ${Ue()}
 
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
@@ -5698,7 +5698,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function gse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function wi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=ise(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new fse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new mse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var bse={kernelName:pi,backendName:"webgpu",kernelFunc:wi};function yse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var vse={kernelName:Ia,backendName:"webgpu",kernelFunc:yse};function xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=wi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new w2(u.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var wse={kernelName:ol,backendName:"webgpu",kernelFunc:xse},k2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+    `}};function Nse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function Xs(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=gse(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new Sse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new Cse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var Tse={kernelName:mi,backendName:"webgpu",kernelFunc:Xs};function $se(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Xs({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new N2(u.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var _se={kernelName:Sa,backendName:"webgpu",kernelFunc:$se};function Ase(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=S.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Xs({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=S.getInnerMostAxes(i.length,u.shape.length)),S.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new N2(u.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var Ese={kernelName:ul,backendName:"webgpu",kernelFunc:Ase},T2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
       ${Ue()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -5731,7 +5731,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, ${t});
         }
       }
-    `}},I2=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+    `}},$2=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -5746,7 +5746,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function kse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new I2(c):(p=new k2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var Ise={kernelName:Sa,backendName:"webgpu",kernelFunc:kse};function Sse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return _v({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Cse={kernelName:Ca,backendName:"webgpu",kernelFunc:Sse},Nse=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=Tse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Km[a]} = uniforms.start[${a}] + coords.${Km[a]};`),`
+    `}};function Rse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new $2(c):(p=new T2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var Dse={kernelName:Ca,backendName:"webgpu",kernelFunc:Rse};function Fse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Ev({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Ose={kernelName:Na,backendName:"webgpu",kernelFunc:Fse},Pse=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=zse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Ym[a]} = uniforms.start[${a}] + coords.${Ym[a]};`),`
       ${Ue()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -5756,7 +5756,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},Km=["x","y","z","w","u","v"];function Tse(e){if(e===1)return"sourceLoc";if(e<=6)return Km.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=ese(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Nse(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var $se={kernelName:Po,backendName:"webgpu",kernelFunc:cu},_se=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},Ase={kernelName:oo,backendName:"webgpu",kernelFunc:_se},S2=mn({opSnippet:10,dtype:"bool",cpuKernelImpl:Xne}),Ese={kernelName:So,backendName:"webgpu",kernelFunc:S2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.real},backend:n})}var Rse={kernelName:np,backendName:"webgpu",kernelFunc:sc};function Dse(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Xm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return es({inputs:{x:r},backend:n});let i=$t(r.shape),o=Xm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=lu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=sc({inputs:{input:r},backend:n}),o=Xm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=es({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return Dse(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=S2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Fse={kernelName:Na,backendName:"webgpu",kernelFunc:Xm},Ose=Kt({opType:1,cpuKernelImpl:Ene}),Pse={kernelName:Ta,backendName:"webgpu",kernelFunc:Ose},zse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+    `}},Ym=["x","y","z","w","u","v"];function zse(e){if(e===1)return"sourceLoc";if(e<=6)return Ym.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function du(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=cse(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new Pse(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var Mse={kernelName:zo,backendName:"webgpu",kernelFunc:du},Lse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=Xs({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=du({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},Bse={kernelName:lo,backendName:"webgpu",kernelFunc:Lse},_2=mn({opSnippet:10,dtype:"bool",cpuKernelImpl:ase}),Vse={kernelName:No,backendName:"webgpu",kernelFunc:_2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.real},backend:n})}var Wse={kernelName:rp,backendName:"webgpu",kernelFunc:sc};function Use(e,t){let n=new nc(e.shape,22),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Qm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Wn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Qm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=cu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=sc({inputs:{input:r},backend:n}),o=Qm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Wn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return Use(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=_2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Gse={kernelName:Ta,backendName:"webgpu",kernelFunc:Qm},Hse=Kt({opType:1,cpuKernelImpl:Vne}),qse={kernelName:$a,backendName:"webgpu",kernelFunc:Hse},jse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5772,7 +5772,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, clampedValue);
         }
       }
-    `}},Mse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+    `}},Kse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
@@ -5783,7 +5783,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
         }
       }
-    `}};function Lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:"float32",data:[a]},{type:"float32",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new zse(r.shape):o=new Mse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Bse={kernelName:Sr,backendName:"webgpu",kernelFunc:Lse},Vse=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+    `}};function Xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:"float32",data:[a]},{type:"float32",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new jse(r.shape):o=new Kse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Yse={kernelName:Cr,backendName:"webgpu",kernelFunc:Xse},Qse=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=S.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
       ${Ue()}
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5797,8 +5797,8 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return es({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Wse={kernelName:Zd,backendName:"webgpu",kernelFunc:Zp};function Ym(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Ym(h,t,n),g=Ym(f,t,n),b=lu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Rne(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Use(e,t,n),o=a.map(h=>h.shape),u=new Vse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Use(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return es({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Ym(o,a,n)}var Gse={kernelName:uo,backendName:"webgpu",kernelFunc:C2},Hse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
-      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
+    `}};function eh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Wn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Zse={kernelName:ep,backendName:"webgpu",kernelFunc:eh};function Zm(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>eh({inputs:{input:y},backend:n})),m=Zm(h,t,n),g=Zm(f,t,n),b=cu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=S.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=Wne(f,m,s,g),y=S.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Jse(e,t,n),o=a.map(h=>h.shape),u=new Qse(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Jse(e,t,n){let s=S.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function A2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=S.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Wn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return S.assertParamsConsistent(u,a),Zm(o,a,n)}var ere={kernelName:co,backendName:"webgpu",kernelFunc:A2},tre=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
     let divBy4Remainder${e} = flatIndex${e} % 4;
     let divBy4Index${e} = flatIndex${e} / 4;
     let curData${e} = x[divBy4Index${e}];
@@ -5817,7 +5817,7 @@ return a / b;`,b7=`
         temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
       }
     }
-    `}getUserCode(){let e=y2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];
+    `}getUserCode(){let e=I2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];
         let outCol = r % uniforms.outShape[2];
         let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
         let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
@@ -5857,7 +5857,7 @@ return a / b;`,b7=`
            return W[row * uniforms.dimBOuter / 4 + col];
          }
          return vec4<f32>(0.0);
-        `,i="",o="";if(this.activation){let c=Zs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+        `,i="",o="";if(this.activation){let c=Js(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${c}
         }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {
@@ -5896,7 +5896,7 @@ return a / b;`,b7=`
           }
         }
         ${e}
-      `}},qse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=$v(this.elementsPerThread,this.workGroupSize),t=`
+      `}},nre=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tv(this.dispatchLayout,this.outputShape),this.elementsPerThread=$v(this.dispatchLayout,this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=Av(this.elementsPerThread,this.workGroupSize),t=`
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -5920,7 +5920,7 @@ return a / b;`,b7=`
            return W[row * uniforms.dimBOuter + col];
 	 }
 	 return 0.0;
-	 `,r="",a="";if(this.activation){let u=Zs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
+	 `,r="",a="";if(this.activation){let u=Js(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                   let b = getPreluActivationWeightsByOutputCoords(outCoord);
                   ${u}
                 }`:r=`
@@ -5951,7 +5951,7 @@ return a / b;`,b7=`
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
     ${e}
-  `}},jse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
+  `}},sre=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Js(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
                let b = getPreluActivationWeightsByOutputCoords(outCoord);
                ${r}
              }`:e=`
@@ -5985,7 +5985,7 @@ return a / b;`,b7=`
         }
       }
 
-      ${Dr()}
+      ${Fr()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let outChannel = coords[3];
@@ -6006,8 +6006,8 @@ return a / b;`,b7=`
 
         writeResult(batch, coords[1], coords[2], outChannel, acc);
       }
-    `}},Kse=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
-      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
+    `}},rre=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, outWidth : i32, itemsPerBlockRow : i32,
+      inChannels : i32,`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
     ${Ue()}
 
       for(var i = 0; i<${this.workPerThread}; i = i + 1) {
@@ -6036,7 +6036,7 @@ return a / b;`,b7=`
         }
       }
     }
-  `}};function Xse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=_v({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function Yse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Kse(T,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new v2(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:"int32",data:[Y]},{type:"int32",data:[te]},{type:"int32",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o==="leakyrelu"&&($.push({type:"float32",data:[i]}),K.uniforms+=" alpha : f32,");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function N2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Xse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return Yse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new jse(n,u,o,l);else{h?c=new Hse(n,u,o,l):c=new qse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o==="leakyrelu"&&(m.push({type:"float32",data:[i]}),c.uniforms+=" alpha : f32,"),s.runWebGPUProgram(c,g,e.dtype,m)}function Qse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return N2({x:r,filter:a,convInfo:d,backend:s})}var Zse={kernelName:$a,backendName:"webgpu",kernelFunc:Qse},Jse=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Cv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Nv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+  `}};function are({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=Ev({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function ire({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,T=[k,x],N=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new rre(T,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,T[0],T[1]]}});A.push(z),A.push(W);let q=[1,T[0],T[1]],K=new S2(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),N,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:"int32",data:[Y]},{type:"int32",data:[te]},{type:"int32",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a),o==="leakyrelu"&&($.push({type:"float32",data:[i]}),K.uniforms+=" alpha : f32,");let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function E2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return are({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return ire({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new sre(n,u,o,l);else{h?c=new tre(n,u,o,l):c=new nre(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),o==="leakyrelu"&&(m.push({type:"float32",data:[i]}),c.uniforms+=" alpha : f32,"),s.runWebGPUProgram(c,g,e.dtype,m)}function ore(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=S.convertConv2DDataFormat(u),d=S.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return E2({x:r,filter:a,convInfo:d,backend:s})}var ure={kernelName:_a,backendName:"webgpu",kernelFunc:ore},lre=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Tv(this.dispatchLayout,this.outputShape),this.elementsPerThread=$v(this.dispatchLayout,this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6089,8 +6089,8 @@ return a / b;`,b7=`
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
 
-    ${$v(this.elementsPerThread,this.workGroupSize)}
-  `}},ere=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
+    ${Av(this.elementsPerThread,this.workGroupSize)}
+  `}},cre=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
     ${Ue()} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6139,7 +6139,7 @@ return a / b;`,b7=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function tre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new ere(d);else{f=new Jse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var nre={kernelName:_a,backendName:"webgpu",kernelFunc:tre},sre=Kt({opType:2}),rre={kernelName:Aa,backendName:"webgpu",kernelFunc:sre},are=Kt({opType:3}),ire={kernelName:Ea,backendName:"webgpu",kernelFunc:are},ore=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
+  `}};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=S.convertConv2DDataFormat(l),d=S.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new cre(d);else{f=new lre(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[b]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var pre={kernelName:Aa,backendName:"webgpu",kernelFunc:dre},hre=Kt({opType:2}),fre={kernelName:Ea,backendName:"webgpu",kernelFunc:hre},mre=Kt({opType:3}),gre={kernelName:Ra,backendName:"webgpu",kernelFunc:mre},bre=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
       ${Ue()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6195,7 +6195,23 @@ return a / b;`,b7=`
         }
       }
     }
-    `}},ure=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new ore(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},lre={kernelName:co,backendName:"webgpu",kernelFunc:ure},cre=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+    `}},yre=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new bre(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},vre={kernelName:ho,backendName:"webgpu",kernelFunc:yre},Pw=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op==="*"?"1.0":"0.0",n=this.exclusive?t:`getX(${zw(e,"coords",this.op)})`,s=this.outputShape[this.outputShape.length-1],r="",a="";return this.exclusive?(r=this.reverse?`end != ${s-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${s}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
+      ${Ue()}
+       if (index < uniforms.size) {
+         var coords = getCoordsFromIndex(index);
+
+         let end = ${Mw(e,"coords",this.op)};
+         var val = ${n};
+         let pow2 = i32(pow(2.0, uniforms.index));
+         if (${r}) {
+           let idx = ${a};
+           ${Mw(e,"coords",this.op)} = idx;
+           val ${this.op}= getX(${zw(e,"coords",this.op)});
+         }
+         setOutputAtIndex(index, val);
+       }
+      }
+    `}};function zw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function Mw(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function R2(e,t,n,s,r,a){let i=t.shape.length,o=S.getAxesPermutation([s],i),u=t;o!=null&&(u=Xs({inputs:{x:t},backend:n,attrs:{perm:o}}));let l=S.getInnerMostAxes(1,i)[0];if(l!==i-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=u.shape[l],p=Wn({inputs:{x:u},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Pw(e,u.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new Pw(e,u.shape,r,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(o!=null){let d=S.getUndoAxesPermutation(o),h=Xs({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(u.dataId),h}return p}function xre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return R2("*",r,n,a,i,o)}var wre={kernelName:po,backendName:"webgpu",kernelFunc:xre};function kre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;return R2("+",r,n,a,i,o)}var Ire={kernelName:Da,backendName:"webgpu",kernelFunc:kre},Sre=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6215,7 +6231,7 @@ return a / b;`,b7=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function dre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new cre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var pre={kernelName:po,backendName:"webgpu",kernelFunc:dre},T2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new Sre(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Nre={kernelName:fo,backendName:"webgpu",kernelFunc:Cre},D2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Js(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
         }`:e=`
@@ -6225,7 +6241,7 @@ return a / b;`,b7=`
         `,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
       ${e}
 
-      ${Sv()}
+      ${Nv()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -6287,9 +6303,9 @@ return a / b;`,b7=`
           }
         }
       }
-    `}},$2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+    `}},F2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
       inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
-      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=Js(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
         }`:e=`
@@ -6307,7 +6323,7 @@ return a / b;`,b7=`
         }
       }
 
-      ${Dr()}
+      ${Fr()}
         let coords = getOutputCoords();
         let batch = coords[0];
         let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
@@ -6369,7 +6385,7 @@ return a / b;`,b7=`
         ${t}
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
-    `}};function hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new T2(p):(h=new $2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var fre={kernelName:Ra,backendName:"webgpu",kernelFunc:hre},_2=mn({opSnippet:0,cpuKernelImpl:jne,supportsComplex:!0}),mre={kernelName:Ya,backendName:"webgpu",kernelFunc:_2},gre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+    `}};function Tre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=S.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new D2(p):(h=new F2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var $re={kernelName:Fa,backendName:"webgpu",kernelFunc:Tre},O2=mn({opSnippet:0,cpuKernelImpl:sse,supportsComplex:!0}),_re={kernelName:Za,backendName:"webgpu",kernelFunc:O2},Are=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=S.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
          if (isnan(candidate)) {
           bestValue = uniforms.NAN;
          } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
@@ -6417,13 +6433,13 @@ return a / b;`,b7=`
           ${n}
         }
        }
-     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=wi({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Gne(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=Yne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":cp(e.dtype),x=[{type:"int32",data:[m]}],k=new gre(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Av(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"sum",n)}var bre={kernelName:oi,backendName:"webgpu",kernelFunc:Av};function yre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=wi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=_2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Av({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var vre={kernelName:Qd,backendName:"webgpu",kernelFunc:yre},xre=Kt({opType:4}),wre={kernelName:Fa,backendName:"webgpu",kernelFunc:xre},kre=mn({opSnippet:4,dtype:"bool",cpuKernelImpl:Dne}),Ire={kernelName:ho,backendName:"webgpu",kernelFunc:kre},A2=Kt({opType:5,cpuKernelImpl:Fne,dtype:"float32"}),Sre={kernelName:Oa,backendName:"webgpu",kernelFunc:A2};function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Cre={kernelName:fo,backendName:"webgpu",kernelFunc:Qm},Nre=Kt({opType:6,cpuKernelImpl:One}),Tre={kernelName:mo,backendName:"webgpu",kernelFunc:Nre},$re=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=S.getAxesPermutation(u,a),c=e;l!=null&&(c=Xs({inputs:{x:e},attrs:{perm:l},backend:r}),u=S.getInnerMostAxes(u.length,a),i.push(c)),S.assertAxesAreInnerMostDims(s,u,a);let[p,d]=S.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=S.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=ese(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=ise(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":pp(e.dtype),x=[{type:"int32",data:[m]}],k=new Are(y,s),T=r.runWebGPUProgram(k,[c],v,x);i.push(T),f=Me({inputs:{x:T},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Rv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"sum",n)}var Ere={kernelName:ci,backendName:"webgpu",kernelFunc:Rv};function Rre(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=S.decodeEinsumEquation(r,a.length);S.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=S.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=S.getEinsumPermutation(h,u[g]),v;S.isIdentityPermutation(b)?v=a[g]:(v=Xs({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=O2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Rv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var Dre={kernelName:Jd,backendName:"webgpu",kernelFunc:Rre},Fre=Kt({opType:4}),Ore={kernelName:Pa,backendName:"webgpu",kernelFunc:Fre},Pre=mn({opSnippet:4,dtype:"bool",cpuKernelImpl:Une}),zre={kernelName:mo,backendName:"webgpu",kernelFunc:Pre},P2=Kt({opType:5,cpuKernelImpl:Gne,dtype:"float32"}),Mre={kernelName:za,backendName:"webgpu",kernelFunc:P2};function Jm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var Lre={kernelName:go,backendName:"webgpu",kernelFunc:Jm},Bre=Kt({opType:6,cpuKernelImpl:Hne}),Vre={kernelName:bo,backendName:"webgpu",kernelFunc:Bre},Wre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
     ${Ue()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function du(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new $re(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var _re={kernelName:ml,backendName:"webgpu",kernelFunc:du},Are=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function pu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Wre(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var Ure={kernelName:ml,backendName:"webgpu",kernelFunc:pu},Gre=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6432,7 +6448,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},Ere={kernelName:go,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Are(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Rre=Kt({opType:7,cpuKernelImpl:Pne}),Dre={kernelName:Pa,backendName:"webgpu",kernelFunc:Rre},Fre=mn({opSnippet:12,dtype:"int32"}),Ore={kernelName:za,backendName:"webgpu",kernelFunc:Fre},Pre=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},E2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=cne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function R2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function Ow(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?"import":"copyExternal"],h=R2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>E2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var zre={kernelName:fd,backendName:"webgpu",kernelFunc:Mre},Bi;function Mre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Bi==null&&(Bi=document.createElement("canvas").getContext("2d")),Bi.canvas.width=c,Bi.canvas.height=p,Bi.drawImage(r,0,0,c,p),r=Bi.canvas),l||u||i||o)return Ow({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Lre=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+    `}},Hre={kernelName:yo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Gre(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},qre=Kt({opType:7,cpuKernelImpl:qne}),jre={kernelName:Ma,backendName:"webgpu",kernelFunc:qre},Kre=mn({opSnippet:12,dtype:"int32"}),Xre={kernelName:La,backendName:"webgpu",kernelFunc:Kre},Yre=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},z2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=xne(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function M2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function Lw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?"import":"copyExternal"],h=M2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>z2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var Qre={kernelName:fd,backendName:"webgpu",kernelFunc:Zre},Wi;function Zre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return Lw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Wi==null&&(Wi=document.createElement("canvas").getContext("2d")),Wi.canvas.width=c,Wi.canvas.height=p,Wi.drawImage(r,0,0,c,p),r=Wi.canvas),l||u||i||o)return Lw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Jre=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],S.assertAndGetBroadcastShape(e,t),S.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(S.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(S.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
       ${Ue()}
         if (index < uniforms.size)
         {
@@ -6445,7 +6461,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},Bre={kernelName:Ma,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Lre(s.shape,i.shape,o.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function Vre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return N2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var Wre={kernelName:aa,backendName:"webgpu",kernelFunc:Vre};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new T2(m,b,d,y):(x=new $2(m,b,d,y),v.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(v.push({type:"float32",data:[h]}),x.uniforms+=" alpha : f32,"),n.runWebGPUProgram(x,g,"float32",v)}var Gre={kernelName:ia,backendName:"webgpu",kernelFunc:Ure},Hre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+  `}},eae={kernelName:Ba,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Jre(s.shape,i.shape,o.shape,p,d),f=[{type:"float32",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=S.convertConv2DDataFormat(c),g=S.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return E2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var nae={kernelName:ia,backendName:"webgpu",kernelFunc:tae};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),w.assert(S.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let m=S.computeConv2DInfo(r.shape,a.shape,u,f,l,p,!0),g=[r,a],b=i!=null,y=o!=null;b&&g.push(i),y&&g.push(o);let v=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],x;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?x=new D2(m,b,d,y):(x=new F2(m,b,d,y),v.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(v.push({type:"float32",data:[h]}),x.uniforms+=" alpha : f32,"),n.runWebGPUProgram(x,g,"float32",v)}var rae={kernelName:oa,backendName:"webgpu",kernelFunc:sae},aae=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Wt(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
         ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6459,7 +6475,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function qre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=zne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Hre(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var jre={kernelName:yo,backendName:"webgpu",kernelFunc:qre},Kre=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Xre(this.aShape);return`
+      `}};function iae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=S.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=jne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new aae(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var oae={kernelName:xo,backendName:"webgpu",kernelFunc:iae},uae=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=lae(this.aShape);return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6468,7 +6484,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function Xre(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function D2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=Mne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Kre(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var Yre={kernelName:bo,backendName:"webgpu",kernelFunc:D2},Qre=mn({opSnippet:5,cpuKernelImpl:Bne,dtype:"bool"}),Zre={kernelName:vo,backendName:"webgpu",kernelFunc:Qre},Jre=mn({opSnippet:6,dtype:"bool",cpuKernelImpl:Lne}),eae={kernelName:La,backendName:"webgpu",kernelFunc:Jre};function tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new nc(r.shape,14);return o.uniforms="alpha : f32,",n.runWebGPUProgram(o,[r],"float32",i)}var nae={kernelName:Va,backendName:"webgpu",kernelFunc:tae},sae=mn({opSnippet:7,dtype:"bool",cpuKernelImpl:Wne}),rae={kernelName:xo,backendName:"webgpu",kernelFunc:sae},aae=mn({opSnippet:8,dtype:"bool",cpuKernelImpl:Vne}),iae={kernelName:wo,backendName:"webgpu",kernelFunc:aae},oae=Kt({opType:9,cpuKernelImpl:Une}),uae={kernelName:Wa,backendName:"webgpu",kernelFunc:oae},lae=mn({opSnippet:9,dtype:"bool"}),cae={kernelName:ko,backendName:"webgpu",kernelFunc:lae},dae=Kt({opType:10}),pae={kernelName:xl,backendName:"webgpu",kernelFunc:dae};function F2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,"max",n)}var hae={kernelName:Ua,backendName:"webgpu",kernelFunc:F2},fae=mn({opSnippet:15,cpuKernelImpl:Hne}),mae={kernelName:Ga,backendName:"webgpu",kernelFunc:fae};function gae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return es({inputs:{x:r},backend:n});p=new I2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new k2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var bae={kernelName:Ha,backendName:"webgpu",kernelFunc:gae};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,"mean",n)}var vae={kernelName:qa,backendName:"webgpu",kernelFunc:yae};function xae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"min",n)}var wae={kernelName:ja,backendName:"webgpu",kernelFunc:xae},kae=mn({opSnippet:16,cpuKernelImpl:qne}),Iae={kernelName:Ka,backendName:"webgpu",kernelFunc:kae},Sae=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}};function lae(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function L2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),T=n.tensorMap.get(d.dataId).values,N=De(d.shape,d.dtype,T),E=Kne(N,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new uae(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var cae={kernelName:vo,backendName:"webgpu",kernelFunc:L2},dae=mn({opSnippet:5,cpuKernelImpl:Yne,dtype:"bool"}),pae={kernelName:wo,backendName:"webgpu",kernelFunc:dae},hae=mn({opSnippet:6,dtype:"bool",cpuKernelImpl:Xne}),fae={kernelName:Va,backendName:"webgpu",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new nc(r.shape,14);return o.uniforms="alpha : f32,",n.runWebGPUProgram(o,[r],"float32",i)}var gae={kernelName:Ua,backendName:"webgpu",kernelFunc:mae},bae=mn({opSnippet:7,dtype:"bool",cpuKernelImpl:Zne}),yae={kernelName:ko,backendName:"webgpu",kernelFunc:bae},vae=mn({opSnippet:8,dtype:"bool",cpuKernelImpl:Qne}),xae={kernelName:Io,backendName:"webgpu",kernelFunc:vae},wae=Kt({opType:9,cpuKernelImpl:Jne}),kae={kernelName:Ga,backendName:"webgpu",kernelFunc:wae},Iae=mn({opSnippet:9,dtype:"bool"}),Sae={kernelName:So,backendName:"webgpu",kernelFunc:Iae},Cae=Kt({opType:10}),Nae={kernelName:xl,backendName:"webgpu",kernelFunc:Cae};function B2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,"max",n)}var Tae={kernelName:Ha,backendName:"webgpu",kernelFunc:B2},$ae=mn({opSnippet:15,cpuKernelImpl:tse}),_ae={kernelName:qa,backendName:"webgpu",kernelFunc:$ae};function Aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=S.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Wn({inputs:{x:r},backend:n});p=new $2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new T2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var Eae={kernelName:ja,backendName:"webgpu",kernelFunc:Aae};function Rae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,"mean",n)}var Dae={kernelName:Ka,backendName:"webgpu",kernelFunc:Rae};function Fae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"min",n)}var Oae={kernelName:Xa,backendName:"webgpu",kernelFunc:Fae},Pae=mn({opSnippet:16,cpuKernelImpl:nse}),zae={kernelName:Ya,backendName:"webgpu",kernelFunc:Pae},Mae=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
           let start = ${i}(${t});
@@ -6485,7 +6501,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, getX(${o}));
         }
       }
-    `}},Cae={kernelName:Xa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new Sae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Nae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=Kne(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Tae={kernelName:Io,backendName:"webgpu",kernelFunc:Nae};function $ae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var _ae={kernelName:Co,backendName:"webgpu",kernelFunc:$ae};function Aae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Eae={kernelName:No,backendName:"webgpu",kernelFunc:Aae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Rae={kernelName:Ho,backendName:"webgpu",kernelFunc:Ld};function O2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=O2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Dae={kernelName:To,backendName:"webgpu",kernelFunc:O2};function Fae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=C2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Oae={kernelName:_o,backendName:"webgpu",kernelFunc:Fae},Pae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}},Lae={kernelName:Qa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new Mae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function Bae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=rse(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var Vae={kernelName:Co,backendName:"webgpu",kernelFunc:Bae};function Wae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Uae={kernelName:To,backendName:"webgpu",kernelFunc:Wae};function Gae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var Hae={kernelName:$o,backendName:"webgpu",kernelFunc:Gae};function Vd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=Vd({inputs:{x:r},backend:n}),i=eh({inputs:{input:s},backend:n}),o=Vd({inputs:{x:i},backend:n}),u=cu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return pu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var qae={kernelName:qo,backendName:"webgpu",kernelFunc:Vd};function V2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=V2({inputs:{x:r},backend:n}),i=eh({inputs:{input:s},backend:n}),o=Vd({inputs:{x:i},backend:n}),u=cu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return pu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var jae={kernelName:_o,backendName:"webgpu",kernelFunc:V2};function Kae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Jm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Jm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=A2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Xae={kernelName:Eo,backendName:"webgpu",kernelFunc:Kae},Yae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
           let start = ${r};
@@ -6500,7 +6516,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}},P2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return es({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return du({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new Pae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},zae={kernelName:Qa,backendName:"webgpu",kernelFunc:P2},Mae=mn({opSnippet:13}),Lae={kernelName:Za,backendName:"webgpu",kernelFunc:Mae};function Bae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new x2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Vae={kernelName:Ja,backendName:"webgpu",kernelFunc:Bae};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"prod",n)}var Uae={kernelName:Ao,backendName:"webgpu",kernelFunc:Wae},Gae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Qne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Hae={kernelName:Il,backendName:"webgpu",kernelFunc:Gae},z2=mn({opSnippet:3}),qae={kernelName:Da,backendName:"webgpu",kernelFunc:z2},jae=Kt({opType:12}),Kae={kernelName:ei,backendName:"webgpu",kernelFunc:jae},Xae=Kt({opType:13}),Yae={kernelName:ni,backendName:"webgpu",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},W2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Wn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return pu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new Yae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Qae={kernelName:Ja,backendName:"webgpu",kernelFunc:W2},Zae=mn({opSnippet:13}),Jae={kernelName:ei,backendName:"webgpu",kernelFunc:Zae};function eie(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new C2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var tie={kernelName:ti,backendName:"webgpu",kernelFunc:eie};function nie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"prod",n)}var sie={kernelName:ni,backendName:"webgpu",kernelFunc:nie},rie=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=ose(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},aie={kernelName:Il,backendName:"webgpu",kernelFunc:rie},U2=mn({opSnippet:3}),iie={kernelName:Oa,backendName:"webgpu",kernelFunc:U2},oie=Kt({opType:12}),uie={kernelName:si,backendName:"webgpu",kernelFunc:oie},lie=Kt({opType:13}),cie={kernelName:ai,backendName:"webgpu",kernelFunc:lie},die=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6543,7 +6559,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Zae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[o?.5:0]}],f=new Qae(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var Jae={kernelName:ti,backendName:"webgpu",kernelFunc:Zae},eie=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
+    `}};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[o?.5:0]}],f=new die(r.shape,u,l);return n.runWebGPUProgram(f,[r],"float32",h)}var hie={kernelName:ri,backendName:"webgpu",kernelFunc:pie},fie=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6574,7 +6590,7 @@ return a / b;`,b7=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function tie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new eie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var nie={kernelName:Cl,backendName:"webgpu",kernelFunc:tie},sie=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+    `}};function mie(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new fie(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var gie={kernelName:Cl,backendName:"webgpu",kernelFunc:mie},bie=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
         ${Ue()}
 
@@ -6596,7 +6612,7 @@ return a / b;`,b7=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},rie={kernelName:qo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new sie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},aie=Kt({opType:15,cpuKernelImpl:Zne}),iie={kernelName:si,backendName:"webgpu",kernelFunc:aie},oie=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
+      `}},yie={kernelName:jo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new bie(s.shape,a),[l,c]=S.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},vie=Kt({opType:15,cpuKernelImpl:use}),xie={kernelName:ii,backendName:"webgpu",kernelFunc:vie},wie=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Le(e),this.dispatch=$e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32, strides: ${o}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6633,7 +6649,7 @@ return a / b;`,b7=`
 
          ${o}
         }
-      }`}};function uie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=du({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new oie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var lie={kernelName:Fo,backendName:"webgpu",kernelFunc:uie},cie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
+      }`}};function kie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=S.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=pu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new wie(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Iie={kernelName:Oo,backendName:"webgpu",kernelFunc:kie},Sie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6645,7 +6661,7 @@ return a / b;`,b7=`
           }
         }
       }
-    `}};function die(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new cie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var pie={kernelName:Oo,backendName:"webgpu",kernelFunc:die},hie=Kt({opType:18}),fie={kernelName:ai,backendName:"webgpu",kernelFunc:hie},mie=Kt({opType:16}),gie={kernelName:ri,backendName:"webgpu",kernelFunc:mie},bie=Kt({opType:17}),yie={kernelName:zo,backendName:"webgpu",kernelFunc:bie},M2=mn({opSnippet:2,cpuKernelImpl:sse,supportsComplex:!0}),vie={kernelName:ci,backendName:"webgpu",kernelFunc:M2};function xie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=F2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=M2({inputs:{a:r,b:l},backend:n}),p=A2({inputs:{x:c},backend:n}),d=Av({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=z2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var wie={kernelName:ui,backendName:"webgpu",kernelFunc:xie},kie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=P2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=wi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},Iie={kernelName:Mo,backendName:"webgpu",kernelFunc:kie},Sie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+    `}};function Cie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Sie(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],cn(r.dtype,a.dtype))}var Nie={kernelName:Po,backendName:"webgpu",kernelFunc:Cie},Tie=Kt({opType:18}),$ie={kernelName:ui,backendName:"webgpu",kernelFunc:Tie},_ie=Kt({opType:16}),Aie={kernelName:oi,backendName:"webgpu",kernelFunc:_ie},Eie=Kt({opType:17}),Rie={kernelName:Mo,backendName:"webgpu",kernelFunc:Eie},G2=mn({opSnippet:2,cpuKernelImpl:hse,supportsComplex:!0}),Die={kernelName:hi,backendName:"webgpu",kernelFunc:G2};function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=B2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=S.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=G2({inputs:{a:r,b:l},backend:n}),p=P2({inputs:{x:c},backend:n}),d=Rv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=U2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var Oie={kernelName:di,backendName:"webgpu",kernelFunc:Fie},Pie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=W2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=S.getReshaped(c.shape,a,o,!1),d=S.getPermuted(p.length,a.length,!1),h=S.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Xs({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},zie={kernelName:Lo,backendName:"webgpu",kernelFunc:Pie},Mie=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${u},`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
       ${Ue()}
 
         let globalIndex = index * ${this.workPerThread};
@@ -6675,21 +6691,21 @@ return a / b;`,b7=`
             }
           }
         }
-      }`}};function Cie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new Sie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Nie={kernelName:ip,backendName:"webgpu",kernelFunc:Cie};function Tie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var $ie={kernelName:Lo,backendName:"webgpu",kernelFunc:Tie},_ie=Kt({opType:19}),Aie={kernelName:ii,backendName:"webgpu",kernelFunc:_ie},Eie={kernelName:Al,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},Rie=mn({opSnippet:11}),Die={kernelName:li,backendName:"webgpu",kernelFunc:Rie},Fie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+      }`}};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=S.calculateShapes(a,r,o),d=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new Mie(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var Bie={kernelName:up,backendName:"webgpu",kernelFunc:Lie};function Vie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=S.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=du({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var Wie={kernelName:Bo,backendName:"webgpu",kernelFunc:Vie},Uie=Kt({opType:19}),Gie={kernelName:li,backendName:"webgpu",kernelFunc:Uie},Hie={kernelName:Al,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,20);return s.runWebGPUProgram(r,[n],n.dtype)}},qie=mn({opSnippet:11}),jie={kernelName:pi,backendName:"webgpu",kernelFunc:qie},Kie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
        ${Ue()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=tse(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Fie(h),E=[{type:"int32",data:y},{type:"int32",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Pie={kernelName:Bo,backendName:"webgpu",kernelFunc:Oie};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=nse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Mie={kernelName:op,backendName:"webgpu",kernelFunc:zie},Lie=Kt({opType:21}),Bie={kernelName:di,backendName:"webgpu",kernelFunc:Lie},Vie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Wie(this.rank,"uniforms.");return`
+     `}};function Xie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),N=du({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=Me({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeData(N.dataId)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),E=De(r.shape,r.dtype,N),A=dse(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let N=new Kie(h),E=[{type:"int32",data:y},{type:"int32",data:x}],A=n.runWebGPUProgram(N,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Yie={kernelName:Vo,backendName:"webgpu",kernelFunc:Xie};function Qie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=pse(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Zie={kernelName:lp,backendName:"webgpu",kernelFunc:Qie},Jie=Kt({opType:21}),eoe={kernelName:fi,backendName:"webgpu",kernelFunc:Jie},toe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=noe(this.rank,"uniforms.");return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function Wie(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Uie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=rse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Vie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Gie={kernelName:Cr,backendName:"webgpu",kernelFunc:Uie},Hie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+    `}};function noe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function soe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype==="string"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=fse(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new toe(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var roe={kernelName:Nr,backendName:"webgpu",kernelFunc:soe},aoe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
         ${Ue()}
           if (index < uniforms.size) {
@@ -6757,7 +6773,7 @@ return a / b;`,b7=`
             }
           }
         }
-      `}},qie=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+      `}},ioe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
         ${Ue()}
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
@@ -6816,7 +6832,7 @@ return a / b;`,b7=`
             }
           }
         }
-      `}};function Vi(e,t){t!==null&&e.disposeData(t.dataId)}function Pw(e){let t=1;for(;t<e;)t*=2;return t}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=ase(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,du({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Pw(a),h=Pw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new Hie(N),R=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,"int32",R),Vi(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new qie([c,k/2]),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,"int32",A),Vi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=cu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Vi(n,b);let y=D2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Vi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Vi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Vi(n,x),[y,f]}var Kie={kernelName:Wo,backendName:"webgpu",kernelFunc:jie},Xie=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function Ui(e,t){t!==null&&e.disposeData(t.dataId)}function Bw(e){let t=1;for(;t<e;)t*=2;return t}function ooe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[T,N]=mse(k,o,r.dtype,a,i);return[n.makeTensorInfo(T.shape,T.dtype,T.values),n.makeTensorInfo(N.shape,N.dtype,N.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,pu({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Bw(a),h=Bw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,T,N)=>{let E=m(),A=new aoe(N),R=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[T]}],F=f;f=n.runWebGPUProgram(A,E,"int32",R),Ui(n,F)};for(let k=1;k<d;k*=2){let T=k*2;for(let N=k;N>=1;N/=2)g(T,N,[c,h])}for(let k=h;k>d;k/=2){let T=m(),N=new ioe([c,k/2]),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(N,T,"int32",A),Ui(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=du({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Ui(n,b);let y=L2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Ui(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Ui(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Ui(n,x),[y,f]}var uoe={kernelName:Uo,backendName:"webgpu",kernelFunc:ooe},loe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6932,7 +6948,7 @@ return a / b;`,b7=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function Yie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Xie(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Qie={kernelName:Uo,backendName:"webgpu",kernelFunc:Yie};function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Jie={kernelName:Go,backendName:"webgpu",kernelFunc:Zie},eoe=[Cne,use,cse,hse,vse,wse,Ise,Cse,Ase,Fse,Pse,Bse,_ne,Gse,Zse,nre,rre,ire,lre,pre,fre,vre,wre,Ire,Sre,Cre,Tre,_re,Ere,zre,Dre,Ore,Bre,Wre,Gre,jre,Yre,Zre,eae,$ne,Wse,nae,rae,iae,uae,cae,pae,hae,mae,bae,vae,wae,Iae,Cae,mre,Tae,_ae,Eae,Ese,Dae,Oae,zae,Lae,Vae,Uae,Hae,Rse,qae,Kae,Yae,Ine,Jae,nie,rie,iie,lie,pie,fie,gie,yie,$se,Pie,Mie,wie,Iie,Nie,$ie,Aie,Eie,Die,vie,bre,Bie,Gie,Kie,Qie,bse,Jie,Rae];for(let e of eoe)El(e);var toe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=zw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=zw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function zw(e,t){return`${e}_${t}`}var L2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+        `}};function coe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new loe(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var doe={kernelName:Go,backendName:"webgpu",kernelFunc:coe};function poe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=du({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var hoe={kernelName:Ho,backendName:"webgpu",kernelFunc:poe},foe=[One,yse,xse,Ise,_se,Ese,Dse,Ose,Bse,Gse,qse,Yse,Lne,ere,ure,pre,fre,gre,vre,wre,Ire,Nre,$re,Dre,Ore,zre,Mre,Lre,Vre,Ure,Hre,Qre,jre,Xre,eae,nae,rae,oae,cae,pae,fae,Mne,Zse,gae,yae,xae,kae,Sae,Nae,Tae,_ae,Eae,Dae,Oae,zae,Lae,_re,Vae,Uae,Hae,Vse,jae,Xae,Qae,Jae,tie,sie,aie,Wse,iie,uie,cie,Dne,hie,gie,yie,xie,Iie,Nie,$ie,Aie,Rie,Mse,Yie,Zie,Oie,zie,Bie,Wie,Gie,Hie,jie,Die,Ere,eoe,roe,uoe,doe,Tse,hoe,qae];for(let e of foe)El(e);var moe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Vw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Vw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Vw(e,t){return`${e}_${t}`}var H2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Le(this.outputShape),this.dispatch=$e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
       ${Ue()}
@@ -6946,10 +6962,10 @@ return a / b;`,b7=`
           }
         }
       }
-  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},noe=class extends L2{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},soe=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Mw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},B2=class extends nl{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Tv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new toe(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*jm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*jm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new L2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new noe),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=b2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Mw(this.device,e);let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o="int32";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join("_"),f=d.map(N=>N.join("_")).join(";"),m=R2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>E2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Pre(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Mw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=soe){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Ev=B2;Ev.nextDataId=0;var roe={};Ae(roe,{WebGPUBackend:()=>Ev,webgpu_util:()=>m2});Tv()&&pp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new Ev(a,r)},3);var It=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(It||{}),Jp=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(Jp||{}),V2;function aoe(e){V2=e.wasm.cwrap(ra,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function ioe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return V2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var ooe={kernelName:ra,backendName:"wasm",setupFunc:aoe,kernelFunc:ioe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var uoe=Xt(io);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var loe=!0,coe=gn(Ir,loe),W2;function doe(e){W2=e.wasm.cwrap(ka,null,["array","number","number","number"])}function poe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return W2(a,r.length,It[s.dtype],i),s}var hoe={kernelName:ka,backendName:"wasm",setupFunc:doe,kernelFunc:poe};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var foe={kernelName:Ba,backendName:"wasm",kernelFunc:eh},U2;function moe(e){U2=e.wasm.cwrap(pi,null,["number","array","number","number","number","array","number"])}function wr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=boe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=goe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return U2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function goe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function boe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var yoe={kernelName:pi,backendName:"wasm",kernelFunc:wr,setupFunc:moe};function Fr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=wr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var G2;function voe(e){G2=e.wasm.cwrap(al,null,["number, number, number"])}function xoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("all",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;G2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var woe={kernelName:al,backendName:"wasm",setupFunc:voe,kernelFunc:xoe},H2;function koe(e){H2=e.wasm.cwrap(il,null,["number, number, number"])}function Ioe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("any",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;H2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Soe={kernelName:il,backendName:"wasm",setupFunc:koe,kernelFunc:Ioe},q2;function Coe(e){q2=e.wasm.cwrap(Ia,null,["number","number","number","number","number"])}function Noe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Fr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return q2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Toe={kernelName:Ia,backendName:"wasm",kernelFunc:Noe,setupFunc:Coe},j2;function $oe(e){j2=e.wasm.cwrap(Sa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function _oe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return j2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var Aoe={kernelName:Sa,backendName:"wasm",setupFunc:$oe,kernelFunc:_oe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Eoe={kernelName:Eo,backendName:"wasm",kernelFunc:yn},K2;function Roe(e){K2=e.wasm.cwrap(Ca,null,["number","array","number","number","array","number","number","number","number"])}function Doe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),N=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,A=n.dataIdMap.get(N.dataId).id,P=i?T.shape[2]:T.shape[1],R=o?N.shape[1]:N.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return K2(E,W,T.shape.length,A,q,N.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(N.dataId),$.shape=v,$}var Foe={kernelName:Ca,backendName:"wasm",setupFunc:Roe,kernelFunc:Doe};function va(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Ooe(u,c[0],d,a,i);else if(h===3)Poe(u,c[0],c[1],d,a,i);else if(h===4)zoe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Ooe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Poe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function zoe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Moe={kernelName:Po,backendName:"wasm",kernelFunc:va};function Loe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=wr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=va({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Boe={kernelName:oo,backendName:"wasm",kernelFunc:Loe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Voe={kernelName:Na,backendName:"wasm",kernelFunc:ac},Woe=Xt(Ta),X2;function Uoe(e){X2=e.wasm.cwrap(Sr,null,["number","number","number","number"])}function 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y.stringBytes=S.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var qoe={kernelName:uo,backendName:"wasm",kernelFunc:Y2},Q2;function joe(e){Q2=e.wasm.cwrap($a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Koe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=S.convertConv2DDataFormat(d),f=S.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,T=f.dilationWidth,N=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Qoe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=S.convertConv2DDataFormat(u),h=S.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:T,strideHeight:N,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,"float32"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,ke=t.dataIdMap.get(a.dataId).id;return Z2(me,ke,f,m,g,y,v,b,k,T,x,N,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Zoe={kernelName:_a,backendName:"wasm",setupFunc:Yoe,kernelFunc:Qoe},Joe=Xt(Aa),eue=Xt(Ea),J2=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(J2||{}),eN;function tue(e){eN=e.wasm.cwrap(co,null,["number","number","number","number","array","number","number","number","number","number"])}function nue(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return eN(g,b,y,c,k,p,d,J2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var sue={kernelName:co,backendName:"wasm",setupFunc:tue,kernelFunc:nue},tN;function rue(e){tN=e.wasm.cwrap(hl,null,["number","number","number","number","number","number"])}function aue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumprod",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;tN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var iue={kernelName:hl,backendName:"wasm",setupFunc:rue,kernelFunc:aue},nN;function oue(e){nN=e.wasm.cwrap(lo,null,["number","number","number","number","number","number"])}function uue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=S.getAxesPermutation([a],u),c=r;l!==null&&(c=wr({inputs:{x:r},attrs:{perm:l},backend:n}));let p=S.getInnerMostAxes(1,u)[0];S.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;nN(f,i?1:0,o?1:0,h,m,It[r.dtype]);let g=d;if(l!==null){let b=S.getUndoAxesPermutation(l);g=wr({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var lue={kernelName:lo,backendName:"wasm",setupFunc:oue,kernelFunc:uue},sN;function cue(e){sN=e.wasm.cwrap(po,null,["number","number","number","array","number","array","array","number","number"])}function due(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return sN(b,a,i==="NHWC"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var pue={kernelName:po,backendName:"wasm",setupFunc:cue,kernelFunc:due},rN;function hue(e){rN=e.wasm.cwrap(Ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=S.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,T=h.strideHeight,N=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,"float32"),F=s.dataIdMap.get(R.dataId).id;return rN(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,T,N,E,A,F),R}var mue={kernelName:Ra,backendName:"wasm",setupFunc:hue,kernelFunc:fue},gue=Xt(Fa),bue=!1,yue=gn(ho,bue,"bool"),vue=Xt(Oa,"float32");function Zm(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),yn({inputs:{x:r},backend:s,attrs:{shape:o}})}var xue={kernelName:fo,backendName:"wasm",kernelFunc:Zm};function aN(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var wue={kernelName:ml,backendName:"wasm",kernelFunc:aN},iN;function kue(e){iN=e.wasm.cwrap(go,null,["number","number","number","number","number","number"])}function Iue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return iN(a,o,u,l,c,i),r}var Sue={kernelName:go,backendName:"wasm",kernelFunc:Iue,setupFunc:kue},Cue=Xt(Pa),Nue=!1,Tue=gn(za,Nue),oN;function $ue(e){oN=e.wasm.cwrap(Ma,null,["number","number","number","number","number","number","number"])}function _ue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return oN(c,p,d,h,f,r,g),m}var Aue={kernelName:Ma,backendName:"wasm",setupFunc:$ue,kernelFunc:_ue},uN;function Eue(e){uN=e.wasm.cwrap(aa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Rue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=S.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,T=m.filterWidth,N=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type==="SAME"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. 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x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var hle={kernelName:qa,backendName:"wasm",setupFunc:dle,kernelFunc:ple},gN;function fle(e){gN=e.wasm.cwrap(ja,null,["number","number","number","number"])}function mle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims("min",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;gN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var gle={kernelName:ja,backendName:"wasm",setupFunc:fle,kernelFunc:mle},ble=!1,yle=gn(Ka,ble),bN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(bN||{}),yN;function vle(e){yN=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function xle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return yN(i,l,t.shape.length,It[t.dtype],d,h,bN[r],u),o}var wle={kernelName:Xa,backendName:"wasm",kernelFunc:xle,setupFunc:vle},kle=!0,Ile=gn(Ya,kle),Sle=Xt(Io);function Rv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var vN;function Cle(e){vN=e.wasm.cwrap(Co,"number",["number","number","number","number","number"])}function Nle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=vN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Rv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Tle={kernelName:Co,backendName:"wasm",setupFunc:Cle,kernelFunc:Nle},xN;function $le(e){xN=e.wasm.cwrap(kl,"number",["number","number","number","number","number","bool"])}function _le(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=xN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var Ale={kernelName:kl,backendName:"wasm",setupFunc:$le,kernelFunc:_le},wN;function Ele(e){wN=e.wasm.cwrap(No,"number",["number","number","number","number","number","number"])}function Rle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=wN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Rv(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var Dle={kernelName:No,backendName:"wasm",setupFunc:Ele,kernelFunc:Rle},Fle=!1,Ole=gn(So,Fle,"bool"),kN;function Ple(e){kN=e.wasm.cwrap($o,null,["number","number","number","number","number"])}function zle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return kN(p,a,i,o,l),u}var Mle={kernelName:$o,backendName:"wasm",setupFunc:Ple,kernelFunc:zle};function Lle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Ble={kernelName:To,backendName:"wasm",kernelFunc:Lle};function Vle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Zm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Zm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Y2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Wle={kernelName:_o,backendName:"wasm",kernelFunc:Vle},IN;function Ule(e){IN=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function Gle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return aN({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return IN(i,c,t.shape.length,It[t.dtype],h,f,r,l),o}var SN={kernelName:Qa,backendName:"wasm",kernelFunc:Gle,setupFunc:Ule},Hle=!1,qle=gn(Za,Hle),CN;function jle(e){CN=e.wasm.cwrap(Ja,null,["number","number","number"])}function Kle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!=="float32"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,"float32"),p=n.dataIdMap.get(c.dataId).id;return CN(o,i,p),u.dtype!=="float32"&&n.disposeData(l.dataId),c}var Xle={kernelName:Ja,backendName:"wasm",setupFunc:jle,kernelFunc:Kle},NN;function Yle(e){NN=e.wasm.cwrap(Ao,null,["number","number","number","number"])}function Qle(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Fr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=S.getInnerMostAxes(f.length,l.shape.length))}S.assertAxesAreInnerMostDims("prod",f,l.shape.length);let[m,g]=S.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;NN(u,b,It[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Zle={kernelName:Ao,backendName:"wasm",setupFunc:Yle,kernelFunc:Qle},Jle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=rv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},ece={kernelName:Il,backendName:"wasm",kernelFunc:Jle},tce=!0,nce=gn(Da,tce),sce=Xt(ei),rce=Xt(ni),TN;function ace(e){TN=e.wasm.cwrap(ti,null,["number","number","number","number","number","number","number","number","number","number"])}function ice(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,[c,p,d,h]=r.shape,f=[c,u,l,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=ac({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let b=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let v=t.dataIdMap.get(y.dataId).id;return TN(b,c,p,d,h,u,l,a?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),y}var oce={kernelName:ti,backendName:"wasm",setupFunc:ace,kernelFunc:ice},$N;function uce(e){$N=e.wasm.cwrap(Ro,null,["number","array","number","array","number","number"])}function lce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return eh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);$N(u,c,i.length,p,r.shape.length,l);let d=yn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var cce={kernelName:Ro,backendName:"wasm",kernelFunc:lce,setupFunc:uce},_N;function dce(e){_N=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function pce(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:i,center:o}=s,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(u.dataId).id,[p,d,h,f]=r.shape,[m,g]=S.getImageCenter(o,d,h),b=i===0,y=255,v=typeof i=="number"?[i,i,i,b?0:y]:[...i,y],x=new Uint8Array(new Int32Array(v).buffer);return _N(l,p,d,h,f,a,m,g,x,v.length,c),u}var hce={kernelName:qo,backendName:"wasm",kernelFunc:pce,setupFunc:dce},fce=Xt(Do),mce=Xt(si),AN;function gce(e){AN=e.wasm.cwrap(Fo,null,["number","number","number","number","number","number","array","number","number"])}function bce(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:i}=s,o=t.makeOutput(i,a.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=Ak.calculateShapes(a,r,i),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=new Uint8Array(new Int32Array(p).buffer),y=t.dataIdMap.get(o.dataId).id;return AN(f,g,It[a.dtype],u,l,c,b,d,y),o}var yce={kernelName:Fo,backendName:"wasm",setupFunc:gce,kernelFunc:bce},EN;function vce(e){EN=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function xce(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id,p=s.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:w.sizeFromShape(r.shape.slice(1));return EN(i,o,u,h,c),l}var wce={kernelName:Oo,backendName:"wasm",kernelFunc:xce,setupFunc:vce},RN;function kce(e){RN=e.wasm.cwrap(ai,null,["number","number"])}function Ice(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return w.sizeFromShape(r.shape)===0||RN(s,a),r}var Sce={kernelName:"Sigmoid",backendName:"wasm",setupFunc:kce,kernelFunc:Ice},Cce=Xt(ri),DN;function Nce(e){DN=e.wasm.cwrap(ui,null,["number","number","number","number"])}function Tce(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(a.dataId).id,o=n.shape[s],u=w.sizeFromShape(n.shape)/o;return w.sizeFromShape(a.shape)===0||DN(r,i,o,u),a}var $ce={kernelName:ui,backendName:"wasm",setupFunc:Nce,kernelFunc:Tce};function _ce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s,o=w.sizeFromShape(a),u=[[0,0]];u.push(...i);for(let T=1+a.length;T<r.shape.length;++T)u.push([0,0]);let l=SN.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),c=S.getReshaped(l.shape,a,o,!1),p=S.getPermuted(c.length,a.length,!1),d=S.getReshapedPermuted(l.shape,a,o,!1),m=yn({inputs:{x:l},backend:n,attrs:{shape:c}}),y=wr({inputs:{x:m},backend:n,attrs:{perm:p}}),k=yn({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeData(l.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),k}var Ace={kernelName:Mo,backendName:"wasm",kernelFunc:_ce},FN;function Ece(e){FN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Rce(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=n,o=s.shape[0],u=s.shape[1],l=t.readSync(a.dataId)[0],c=[o+l,u],p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(c,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([l],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([o],s.dtype),k=t.dataIdMap.get(x.dataId).id,T=t.makeOutput([4],"int32"),N=t.dataIdMap.get(T.dataId).id,E=FN(p,d,It[r.dtype],o,l,u,h,m,b,v,k,N),A=t.readSync(T.dataId),P;switch(A[0]){case 1:{P=S.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);break}case 2:{P=S.getSparseFillEmptyRowsNegativeIndexErrorMessage(A[1],A[2]);break}case 3:P=S.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(A[1],A[2],A[3]);break;default:P=""}if(t.disposeData(T.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(y.dataId),t.disposeData(x.dataId),new Error(P);let R=f,F=g;return E!==c[0]&&(R=va({inputs:{x:f},attrs:{begin:0,size:[E,u]},backend:t}),F=va({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[R,F,y,x]}var Dce={kernelName:sp,backendName:"wasm",setupFunc:Ece,kernelFunc:Rce},ON;function Fce(e){ON=e.wasm.cwrap(_l,null,["number","number","number","number","number","number","number"])}function Oce(e){let{backend:t,inputs:n}=e,{inputIndices:s,inputShape:r,newShape:a}=n;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},goe=class extends H2{constructor(){super(...arguments),this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},boe=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Ww=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},q2=class extends sl{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!_v())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new moe(this.device),this.tensorMap=new Hd(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return q2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*Xm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*Xm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new H2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new goe),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=S.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=k2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let N=this.tensorMap.get(r.dataId);return N.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Ww(this.device,e);let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(N=>N.shape),o="int32";i.map(N=>{a.push({type:o,data:N})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let N=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((N,E)=>{if(N.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[E]}}),p=c.map(N=>N.dtype).concat(r.dtype),d=c.map(N=>S.getBroadcastDims(N.shape,r.shape)),h=c.map(N=>w.arraysEqual(N.shape,r.shape)).join("_"),f=d.map(N=>N.join("_")).join(";"),m=M2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>z2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Yre(this.device,g,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let T={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(T),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Ww(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=boe){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Dv=q2;Dv.nextDataId=0;var yoe={};Ae(yoe,{WebGPUBackend:()=>Dv,webgpu_util:()=>x2});_v()&&fp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new Dv(a,r)},3);var It=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(It||{}),th=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(th||{}),j2;function voe(e){j2=e.wasm.cwrap(aa,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function xoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=th[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=Ko.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,T=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(a.shape).buffer);return j2(d,T,r.shape.length,h,N,a.shape.length,u,l,g,f,m,p||0,k),x}var woe={kernelName:aa,backendName:"wasm",setupFunc:voe,kernelFunc:xoe};function Xt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,It[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var koe=Xt(uo);function gn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=S.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,It[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Ioe=!0,Soe=gn(Sr,Ioe),K2;function Coe(e){K2=e.wasm.cwrap(Ia,null,["array","number","number","number"])}function Noe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return K2(a,r.length,It[s.dtype],i),s}var Toe={kernelName:Ia,backendName:"wasm",setupFunc:Coe,kernelFunc:Noe};function nh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var $oe={kernelName:Wa,backendName:"wasm",kernelFunc:nh},X2;function _oe(e){X2=e.wasm.cwrap(mi,null,["number","array","number","number","number","array","number"])}function kr(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Eoe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=Aoe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=nh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return X2(c,h,u.shape.length,It[u.dtype],p,d,a.length),l}function Aoe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function Eoe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var Roe={kernelName:mi,backendName:"wasm",kernelFunc:kr,setupFunc:_oe};function Or(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=S.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=S.getInnerMostAxes(i.length,r),u=kr({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var Y2;function Doe(e){Y2=e.wasm.cwrap(il,null,["number, number, number"])}function Foe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("all",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;Y2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ooe={kernelName:il,backendName:"wasm",setupFunc:Doe,kernelFunc:Foe},Q2;function Poe(e){Q2=e.wasm.cwrap(ol,null,["number, number, number"])}function zoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("any",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;Q2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Moe={kernelName:ol,backendName:"wasm",setupFunc:Poe,kernelFunc:zoe},Z2;function Loe(e){Z2=e.wasm.cwrap(Sa,null,["number","number","number","number","number"])}function Boe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Or(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return Z2(o,It[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var Voe={kernelName:Sa,backendName:"wasm",kernelFunc:Boe,setupFunc:Loe},J2;function Woe(e){J2=e.wasm.cwrap(Ca,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Uoe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return J2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var Goe={kernelName:Ca,backendName:"wasm",setupFunc:Woe,kernelFunc:Uoe};function yn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var Hoe={kernelName:Ro,backendName:"wasm",kernelFunc:yn},eN;function qoe(e){eN=e.wasm.cwrap(Na,null,["number","array","number","number","array","number","number","number","number"])}function joe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=Ko.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],T=yn({inputs:{x:r},backend:n,attrs:{shape:x}}),N=yn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(T.dataId).id,A=n.dataIdMap.get(N.dataId).id,P=i?T.shape[2]:T.shape[1],R=o?N.shape[1]:N.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],T.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(T.shape).buffer),q=new Uint8Array(new Int32Array(N.shape).buffer);return eN(E,W,T.shape.length,A,q,N.shape.length,i,o,z),n.disposeData(T.dataId),n.disposeData(N.dataId),$.shape=v,$}var Koe={kernelName:Na,backendName:"wasm",setupFunc:qoe,kernelFunc:joe};function xa(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Pd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)Xoe(u,c[0],d,a,i);else if(h===3)Yoe(u,c[0],c[1],d,a,i);else if(h===4)Qoe(u,c[0],c[1],c[2],d,a,i);else{let f=Pd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function Xoe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Yoe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Qoe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Zoe={kernelName:zo,backendName:"wasm",kernelFunc:xa};function Joe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=S.getReshaped(r.shape,a,o),l=S.getPermuted(u.length,a.length),c=S.getReshapedPermuted(r.shape,a,o),p=S.getSliceBeginCoords(i,a.length),d=S.getSliceSize(c,i,a.length),h=yn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=kr({inputs:{x:h},backend:n,attrs:{perm:l}}),m=yn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=xa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var eue={kernelName:lo,backendName:"wasm",kernelFunc:Joe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var tue={kernelName:Ta,backendName:"wasm",kernelFunc:ac},nue=Xt($a),tN;function sue(e){tN=e.wasm.cwrap(Cr,null,["number","number","number","number"])}function 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Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return mN(b,K,Y,Z,y,k,T,x,N,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var Yue={kernelName:oa,backendName:"wasm",setupFunc:Kue,kernelFunc:Xue},gN;function Que(e){gN=e.wasm.cwrap(xo,null,["number","number","number","number","number","number","array","number"])}function Zue(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=Rk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return gN(h,It[s.dtype],m,i,p,o,g,b),l}var Jue={kernelName:xo,backendName:"wasm",setupFunc:Que,kernelFunc:Zue},bN;function ele(e){bN=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function tle(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let A=0;A<l.length;++A){let P=l[A];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=S.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=yn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=yn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,T=t.dataIdMap.get(g.dataId).id,N=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return bN(v,It[r.dtype],N,b,k,p.batchSize,E,T),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var nle={kernelName:vo,backendName:"wasm",setupFunc:ele,kernelFunc:tle},sle=!1,rle=gn(wo,sle,"bool"),ale=!1,ile=gn(Va,ale,"bool"),yN;function ole(e){yN=e.wasm.cwrap(Ua,null,["number","number","number","number"])}function ule(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;yN(r,It[t.dtype],n,i)}return a}var lle={kernelName:Ua,backendName:"wasm",setupFunc:ole,kernelFunc:ule},cle=!1,dle=gn(ko,cle,"bool"),ple=!1,hle=gn(Io,ple,"bool"),fle=Xt(Ga),mle=!1,gle=gn(So,mle,"bool"),vN;function ble(e){vN=e.wasm.cwrap(Ha,null,["number","number","number","number"])}function yle(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;S.assertAxesAreInnerMostDims("max",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;vN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var vle={kernelName:Ha,backendName:"wasm",setupFunc:ble,kernelFunc:yle},xle=!1,wle=gn(qa,xle),xN;function kle(e){xN=e.wasm.cwrap(ja,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ile(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=S.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.dilationHeight,y=c.dilationWidth,v=c.strideHeight,x=c.strideWidth,k=c.inChannels,T=c.outChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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x=S.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!=="float32"&&t.disposeData(y.dataId),v}var Tle={kernelName:Ka,backendName:"wasm",setupFunc:Cle,kernelFunc:Nle},kN;function $le(e){kN=e.wasm.cwrap(Xa,null,["number","number","number","number"])}function _le(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Or(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;S.assertAxesAreInnerMostDims("min",p,f);let[m,g]=S.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;kN(u,It[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=S.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ale={kernelName:Xa,backendName:"wasm",setupFunc:$le,kernelFunc:_le},Ele=!1,Rle=gn(Ya,Ele),IN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(IN||{}),SN;function Dle(e){SN=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function Fle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return SN(i,l,t.shape.length,It[t.dtype],d,h,IN[r],u),o}var Ole={kernelName:Qa,backendName:"wasm",kernelFunc:Fle,setupFunc:Dle},Ple=!0,zle=gn(Za,Ple),Mle=Xt(Co);function Fv(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var CN;function Lle(e){CN=e.wasm.cwrap(To,"number",["number","number","number","number","number"])}function Ble(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=CN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Fv(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Vle={kernelName:To,backendName:"wasm",setupFunc:Lle,kernelFunc:Ble},NN;function Wle(e){NN=e.wasm.cwrap(kl,"number",["number","number","number","number","number","bool"])}function Ule(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=NN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Fv(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var Gle={kernelName:kl,backendName:"wasm",setupFunc:Wle,kernelFunc:Ule},TN;function Hle(e){TN=e.wasm.cwrap($o,"number",["number","number","number","number","number","number"])}function qle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=TN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Fv(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var jle={kernelName:$o,backendName:"wasm",setupFunc:Hle,kernelFunc:qle},Kle=!1,Xle=gn(No,Kle,"bool"),$N;function Yle(e){$N=e.wasm.cwrap(Ao,null,["number","number","number","number","number"])}function Qle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return $N(p,a,i,o,l),u}var Zle={kernelName:Ao,backendName:"wasm",setupFunc:Yle,kernelFunc:Qle};function Jle(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var ece={kernelName:_o,backendName:"wasm",kernelFunc:Jle};function tce(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return eg({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=eg({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=nN({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var nce={kernelName:Eo,backendName:"wasm",kernelFunc:tce},_N;function sce(e){_N=e.wasm.cwrap(Ja,null,["number","array","number","number","array","array","number","number"])}function rce(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return dN({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return _N(i,c,t.shape.length,It[t.dtype],h,f,r,l),o}var AN={kernelName:Ja,backendName:"wasm",kernelFunc:rce,setupFunc:sce},ace=!1,ice=gn(ei,ace),EN;function oce(e){EN=e.wasm.cwrap(ti,null,["number","number","number"])}function 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logicalXor,Cpe as losses,We as matMul,iA as math,As as max,eb as maxPool,dI as maxPool3d,fD as maxPoolWithArgmax,$r as maximum,St as mean,dm as memory,spe as meshgrid,rW as metrics,fm as min,bp as minimum,xD as mirrorPad,kD as mod,Npe as model,xW as models,tb as moments,wpe as movingAverage,V as mul,rpe as multiRNNCell,TD as multinomial,kt as neg,OI as nextFrame,CI as norm,Xu as notEqual,vd as oneHot,Mn as ones,Qn as onesLike,L as op,ape as outerProduct,fi as pad,ipe as pad1d,ope as pad2d,upe as pad3d,lpe as pad4d,cpe as pool,pa as pow,sb as prelu,U_ as print,pI as prod,zde as profile,dpe as rand,ppe as randomGamma,KD as randomNormal,Ml as randomUniform,Yu as range,Bde as ready,wd as real,ZD as reciprocal,pp as registerBackend,_pe as registerCallbackConstructor,D$ as registerGradient,El as registerKernel,Epe as registerOp,wW as regularizers,Xs as relu,hI as relu6,Wde as removeBackend,G as reshape,Zn as reverse,hpe as reverse1d,fpe as reverse2d,mpe as reverse3d,gpe as reverse4d,cb as 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IFFT,Wa as Identity,ep as Imag,Dt as InputSpec,gl as IsFinite,bl as IsInf,yl as IsNan,sl as KernelBackend,np as LRN,Ig as LRNGrad,pz as LayerVariable,hr as LayersModel,Ua as LeakyRelu,ko as Less,Io as LessEqual,kg as LinSpace,Ga as Log,vl as Log1p,z$ as LogSoftmax,So as LogicalAnd,xl as LogicalNot,tp as LogicalOr,G0 as MathBackendCPU,U1 as MathBackendWebGL,Ha as Max,ja as MaxPool,sp as MaxPool3D,Cg as MaxPool3DGrad,Sg as MaxPoolGrad,Ng as MaxPoolWithArgmax,qa as Maximum,Ka as Mean,Xa as Min,Ya as Minimum,Qa as MirrorPad,wl as Mod,xb as MomentumOptimizer,Tg as Multinomial,Za as Multiply,Co as Neg,To as NonMaxSuppressionV3,kl as NonMaxSuppressionV4,$o as NonMaxSuppressionV5,No as NotEqual,m_ as OP_SCOPE_SUFFIX,Ao as OneHot,_o as OnesLike,Ar as Optimizer,Ur as OptimizerConstructors,Eo as Pack,Ja as PadV2,Wde as Pool,ei as Pow,ti as Prelu,ni as Prod,wb as RMSPropOptimizer,Er as RNN,Il as Range,i_ as Rank,rp as Real,Oa as RealDiv,Sl as Reciprocal,pO as Reduction,si as Relu,ai as Relu6,Ro as Reshape,ri as ResizeBilinear,_g as ResizeBilinearGrad,Cl as ResizeNearestNeighbor,$g as ResizeNearestNeighborGrad,Do as Reverse,jo as RotateWithOffset,Fo as Round,ii as Rsqrt,Cp as SGDOptimizer,Oo as ScatterNd,Po as Select,Nl as Selu,Hb as Sequential,ui as Sigmoid,Tl as Sign,oi as Sin,Mo as Sinh,zo as Slice,di as Softmax,$l as Softplus,Lo as SpaceToBatchND,ap as SparseFillEmptyRows,_l as SparseReshape,ip as SparseSegmentMean,op as SparseSegmentSum,up as SparseToDense,Bo as SplitV,li as Sqrt,Al as Square,pi as SquaredDifference,gi as Step,Vo as StridedSlice,lp as StringNGrams,Ag as StringSplit,Eg as StringToHashBucketFast,hi as Sub,ci as Sum,$s as SymbolicTensor,Wo as Tan,fi as Tanh,et as Tensor,Vt as TensorBuffer,Nr as Tile,Uo as TopK,Go as Transform,mi as Transpose,Rg as Unique,Ho as Unpack,cp as UnsortedSegmentSum,gd as Variable,qo as ZerosLike,aa as _FusedMatMul,Mt as abs,YA as acos,ZA as acosh,ie as add,eE as addN,Yk as all,hm as any,qu as argMax,aE as argMin,oE as asin,lE as asinh,dE as atan,hE as atan2,mE as atanh,qg as avgPool,eI as avgPool3d,UA as backend,S as backend_util,ipe as basicLSTMCell,Ku as batchNorm,FE as batchNorm2d,PE as batchNorm3d,ME as batchNorm4d,jg as batchToSpaceND,tI as bincount,Fpe as booleanMaskAsync,VE as broadcastArgs,nd as broadcastTo,Ko as broadcast_util,Ak as browser,De as buffer,Gpe as callbacks,ce as cast,GE as ceil,Vn as clipByValue,cr as clone,ua as complex,Ft as concat,jE as concat1d,XE as concat2d,QE as concat3d,JE as concat4d,$L as constraints,nI as conv1d,da as conv2d,sI as conv2dTranspose,rI as conv3d,oR as conv3dTranspose,Hde as copyRegisteredKernels,Xg as cos,iI as cosh,EI as cosineWindow,mm as cumprod,oI as cumsum,js as customGrad,W4 as data,hR as denseBincount,Kk as deprecationWarn,mR as depthToSpace,mp as depthwiseConv2d,qpe as deregisterOp,hp as device_util,ope as diag,vR as dilation2d,Xde as disableDeprecationWarnings,Re as dispose,Yde as disposeVariables,xe as div,SR as divNoNan,upe as dot,oF as dropout,TR as einsum,gp as elu,Kde as enableDebugMode,jde as enableProdMode,uF as enclosingPowerOfTwo,Ss as engine,X as env,Xn as equal,AR as erf,Yn as exp,Pn as expandDims,FR as expm1,uI as eye,db as fft,Pl as fill,spe as findBackend,rpe as findBackendFactory,bp as floor,Xk as floorDiv,e8 as forceHalfFloat,fa as fused,Xu as gather,rF as gatherND,Rk as gather_util,tpe as getBackend,rx as getGradient,Jf as getKernel,em as getKernelsForBackend,nhe as getThreadsCount,WK as gpgpu_util,dpe as grad,ppe as grads,Un as greater,Xo as greaterEqual,Id as ifft,Yg as imag,jn as image,Ppe as inTopKAsync,DL as initializers,HB as input,En as io,SI as irfft,lpe as isFinite,cpe as isInf,HR as isNaN,Ht as keep,xs as kernel_impls,XL as layers,Qg as leakyRelu,lI as less,Yo as lessEqual,qO as linalg,XR as linspace,jpe as loadGraphModel,Wpe as loadLayersModel,QR as localResponseNormalization,Qn as log,Zg as log1p,mpe as logSigmoid,cI as logSoftmax,dD as logSumExp,Ds as logicalAnd,tb as logicalNot,fI as logicalOr,gpe as logicalXor,Lpe as losses,We as matMul,pA as math,As as max,nb as maxPool,mI as maxPool3d,vD as maxPoolWithArgmax,_r as maximum,St as mean,pm as memory,bpe as meshgrid,fW as metrics,gm as min,vp as minimum,CD as mirrorPad,TD as mod,Bpe as model,AW as models,sb as moments,Ope as movingAverage,V as mul,ype as multiRNNCell,RD as multinomial,kt as neg,LI as nextFrame,_I as norm,Yu as notEqual,vd as oneHot,Mn as ones,Zn as onesLike,L as op,vpe as outerProduct,bi as pad,xpe as pad1d,wpe as pad2d,kpe as pad3d,Ipe as pad4d,Spe as pool,ha as pow,ab as prelu,X_ as print,gI as prod,Qde as profile,Cpe as rand,Npe as randomGamma,JD as randomNormal,Ml as randomUniform,Qu as range,epe as ready,wd as real,s3 as reciprocal,fp as registerBackend,Upe as registerCallbackConstructor,L$ as registerGradient,El as registerKernel,Hpe as registerOp,EW as regularizers,Ys as relu,bI as relu6,npe as removeBackend,G as reshape,Jn as reverse,Tpe as reverse1d,$pe as reverse2d,_pe as reverse3d,Ape as reverse4d,pb as rfft,yI as round,vI as rsqrt,we as scalar,eF as scatterND,Fk as scatter_util,xI as selu,m3 as separableConv2d,Vpe as sequential,ae as serialization,Jde as setBackend,ape as setPlatform,the as setThreadsCount,Jpe as setWasmPath,ehe as setWasmPaths,F5 as setWebGLContext,b3 as setdiff1dAsync,ev as shared,qs as sigmoid,v3 as sign,Mpe as signal,wI as sin,kI as sinh,qe as slice,ub as slice1d,II as slice2d,lb as slice3d,kd as slice4d,wt as slice_util,cb as softmax,zl as softplus,rb as spaceToBatchND,Wc as sparse,AI as sparseToDense,zpe as spectral,Bn as split,dn as sqrt,ct as square,CI as squaredDifference,gr as squeeze,es as stack,xp as step,M3 as stridedSlice,Bf as string,ge as sub,ve as sum,pp as sumOutType,B3 as tan,ju as tanh,fs as tensor,Zt as tensor1d,Yi as tensor2d,yA as tensor3d,Epe as tensor4d,Rpe as tensor5d,Dpe as tensor6d,_s as tensor_util,OA as test_util,j as tidy,ps as tile,Zde as time,W3 as topk,Mi as train,Ge as transpose,hb as truncatedNormal,mx as unique,Gde as unregisterGradient,Ude as unregisterKernel,q3 as unsortedSegmentSum,Fs as unstack,cn as upcastType,w as util,hpe as valueAndGrad,fpe as valueAndGrads,K3 as variable,eD as variableGrads,rhe as version,Kpe as version_converter,qde as version_core,Xpe as version_cpu,hS as version_layers,she as version_wasm,Ype as version_webgl,Qpe as webgl,D5 as webgl_util,yoe as webgpu,vn as where,TI as whereAsync,$t as zeros,je as zerosLike};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
@@ -7171,6 +7187,31 @@ return a / b;`,b7=`
  * limitations under the License.
  * =============================================================================
  */
+/**
+ * @license
+ * Copyright 2022 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
 /**
  * @license
  * Copyright 2022 Google LLC. All Rights Reserved.
diff --git a/package.json b/package.json
index 07d7d345..b687829f 100644
--- a/package.json
+++ b/package.json
@@ -53,7 +53,7 @@
     "tensorflow"
   ],
   "devDependencies": {
-    "@microsoft/api-extractor": "^7.21.2",
+    "@microsoft/api-extractor": "^7.22.1",
     "@tensorflow/tfjs": "^3.15.0",
     "@tensorflow/tfjs-backend-cpu": "^3.15.0",
     "@tensorflow/tfjs-backend-wasm": "^3.15.0",
@@ -65,16 +65,16 @@
     "@tensorflow/tfjs-layers": "^3.15.0",
     "@tensorflow/tfjs-node": "^3.15.0",
     "@tensorflow/tfjs-node-gpu": "^3.15.0",
-    "@types/node": "^17.0.23",
+    "@types/node": "^17.0.24",
     "@types/offscreencanvas": "^2019.6.4",
-    "@typescript-eslint/eslint-plugin": "^5.18.0",
-    "@typescript-eslint/parser": "^5.18.0",
+    "@typescript-eslint/eslint-plugin": "^5.19.0",
+    "@typescript-eslint/parser": "^5.19.0",
     "@vladmandic/build": "^0.7.2",
     "@vladmandic/pilogger": "^0.4.4",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
     "canvas": "^2.9.1",
     "dayjs": "^1.11.0",
-    "esbuild": "^0.14.34",
+    "esbuild": "^0.14.36",
     "eslint": "8.13.0",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-html": "^6.2.0",
diff --git a/test/build.log b/test/build.log
index fe806b6e..226a8fe7 100644
--- a/test/build.log
+++ b/test/build.log
@@ -1,24 +1,24 @@
-2022-04-14 11:39:30 INFO:  Application: {"name":"@vladmandic/human","version":"2.7.0"} 
-2022-04-14 11:39:30 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2022-04-14 11:39:30 INFO:  Toolchain: {"build":"0.7.2","esbuild":"0.14.34","typescript":"4.6.3","typedoc":"0.22.15","eslint":"8.13.0"} 
-2022-04-14 11:39:30 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2022-04-14 11:39:30 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":595} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":72,"inputBytes":572717,"outputBytes":295399} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":599} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":72,"inputBytes":572721,"outputBytes":295403} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":651} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":72,"inputBytes":572773,"outputBytes":295453} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":394} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1068,"outputBytes":615} 
-2022-04-14 11:39:30 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":72,"inputBytes":572737,"outputBytes":294360} 
-2022-04-14 11:39:31 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1344714} 
-2022-04-14 11:39:31 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":72,"inputBytes":1916836,"outputBytes":1638088} 
-2022-04-14 11:39:31 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":72,"inputBytes":1916836,"outputBytes":2117565} 
-2022-04-14 11:39:37 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":114} 
-2022-04-14 11:39:39 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":73,"generated":true} 
-2022-04-14 11:39:39 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5916,"outputBytes":2957} 
-2022-04-14 11:39:39 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
-2022-04-14 11:39:47 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":104,"errors":0,"warnings":0} 
-2022-04-14 11:39:48 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2022-04-14 11:39:48 INFO:  Done... 
+2022-04-14 11:54:20 INFO:  Application: {"name":"@vladmandic/human","version":"2.7.0"} 
+2022-04-14 11:54:20 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2022-04-14 11:54:20 INFO:  Toolchain: {"build":"0.7.2","esbuild":"0.14.36","typescript":"4.6.3","typedoc":"0.22.15","eslint":"8.13.0"} 
+2022-04-14 11:54:20 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2022-04-14 11:54:20 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
+2022-04-14 11:54:20 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":595} 
+2022-04-14 11:54:20 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":72,"inputBytes":572717,"outputBytes":295399} 
+2022-04-14 11:54:20 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":599} 
+2022-04-14 11:54:20 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":72,"inputBytes":572721,"outputBytes":295403} 
+2022-04-14 11:54:20 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":651} 
+2022-04-14 11:54:21 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":72,"inputBytes":572773,"outputBytes":295453} 
+2022-04-14 11:54:21 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":394} 
+2022-04-14 11:54:21 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1068,"outputBytes":615} 
+2022-04-14 11:54:21 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":72,"inputBytes":572737,"outputBytes":294360} 
+2022-04-14 11:54:21 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1349864} 
+2022-04-14 11:54:21 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":72,"inputBytes":1921986,"outputBytes":1643238} 
+2022-04-14 11:54:21 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":72,"inputBytes":1921986,"outputBytes":2123758} 
+2022-04-14 11:54:27 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":114} 
+2022-04-14 11:54:28 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":73,"generated":true} 
+2022-04-14 11:54:28 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5916,"outputBytes":2957} 
+2022-04-14 11:54:28 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
+2022-04-14 11:54:37 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":104,"errors":0,"warnings":0} 
+2022-04-14 11:54:37 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2022-04-14 11:54:37 INFO:  Done... 
diff --git a/test/test.log b/test/test.log
index 741066bc..91bbbc2e 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,684 +1,684 @@
-2022-04-14 11:46:16 INFO:  @vladmandic/human version 2.7.0 
-2022-04-14 11:46:16 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.4.0 
-2022-04-14 11:46:16 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
-2022-04-14 11:46:16 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
-2022-04-14 11:46:16 INFO:  
-2022-04-14 11:46:16 INFO:  test-node.js start 
-2022-04-14 11:46:16 INFO:  test-node.js test: configuration validation 
-2022-04-14 11:46:16 STATE: test-node.js passed: configuration default validation [] 
-2022-04-14 11:46:16 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-04-14 11:46:16 INFO:  test-node.js test: model load 
-2022-04-14 11:46:17 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-04-14 11:46:17 INFO:  test-node.js test: warmup 
-2022-04-14 11:46:17 STATE: test-node.js passed: create human 
-2022-04-14 11:46:17 INFO:  test-node.js human version: 2.7.0 
-2022-04-14 11:46:17 INFO:  test-node.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-04-14 11:46:17 INFO:  test-node.js tfjs version: 3.15.0 
-2022-04-14 11:46:17 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
-2022-04-14 11:46:17 STATE: test-node.js passed: set backend: tensorflow 
-2022-04-14 11:46:17 STATE: test-node.js tensors 1919 
-2022-04-14 11:46:17 STATE: test-node.js passed: load models 
-2022-04-14 11:46:17 STATE: test-node.js  result: defined models: 22 loaded models: 12 
-2022-04-14 11:46:17 STATE: test-node.js passed: warmup: none default 
-2022-04-14 11:46:17 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-04-14 11:46:17 DATA:  test-node.js  result: performance: load: null total: null 
-2022-04-14 11:46:17 STATE: test-node.js passed: warmup none result match 
-2022-04-14 11:46:17 STATE: test-node.js event: image 
-2022-04-14 11:46:17 STATE: test-node.js event: detect 
-2022-04-14 11:46:17 STATE: test-node.js event: warmup 
-2022-04-14 11:46:17 STATE: test-node.js passed: warmup: face default 
-2022-04-14 11:46:17 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-04-14 11:46:17 DATA:  test-node.js  result: performance: load: null total: 355 
-2022-04-14 11:46:17 STATE: test-node.js passed: warmup face result match 
-2022-04-14 11:46:17 STATE: test-node.js event: image 
-2022-04-14 11:46:17 STATE: test-node.js event: detect 
-2022-04-14 11:46:17 STATE: test-node.js event: warmup 
-2022-04-14 11:46:17 STATE: test-node.js passed: warmup: body default 
-2022-04-14 11:46:17 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:17 DATA:  test-node.js  result: performance: load: null total: 275 
-2022-04-14 11:46:17 STATE: test-node.js passed: warmup body result match 
-2022-04-14 11:46:17 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking right"},{"iris":0,"gesture":"looking up"}]} 
-2022-04-14 11:46:17 INFO:  test-node.js test: details verification 
-2022-04-14 11:46:18 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:18 STATE: test-node.js event: image 
-2022-04-14 11:46:18 STATE: test-node.js event: detect 
-2022-04-14 11:46:18 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:18 DATA:  test-node.js  result: performance: load: null total: 253 
-2022-04-14 11:46:18 STATE: test-node.js passed: details face length 1 
-2022-04-14 11:46:18 STATE: test-node.js passed: details face score 1 0.93 1 
-2022-04-14 11:46:18 STATE: test-node.js passed: details face age/gender 23.7 female 0.97 73.26 
-2022-04-14 11:46:18 STATE: test-node.js passed: details face arrays 4 478 1024 
-2022-04-14 11:46:18 STATE: test-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-04-14 11:46:18 STATE: test-node.js passed: details face anti-spoofing 0.79 
-2022-04-14 11:46:18 STATE: test-node.js passed: details face liveness 0.83 
-2022-04-14 11:46:18 STATE: test-node.js passed: details body length 1 
-2022-04-14 11:46:18 STATE: test-node.js passed: details body 0.92 17 6 
-2022-04-14 11:46:18 STATE: test-node.js passed: details hand length 1 
-2022-04-14 11:46:18 STATE: test-node.js passed: details hand 0.51 0.73 point 
-2022-04-14 11:46:18 STATE: test-node.js passed: details hand arrays 21 5 7 
-2022-04-14 11:46:18 STATE: test-node.js passed: details gesture length 6 
-2022-04-14 11:46:18 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-04-14 11:46:18 STATE: test-node.js passed: details object length 1 
-2022-04-14 11:46:18 STATE: test-node.js passed: details object 0.72 person 
-2022-04-14 11:46:18 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-04-14 11:46:18 STATE: test-node.js event: image 
-2022-04-14 11:46:18 STATE: test-node.js event: detect 
-2022-04-14 11:46:18 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-04-14 11:46:18 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-04-14 11:46:18 STATE: test-node.js event: image 
-2022-04-14 11:46:19 STATE: test-node.js event: detect 
-2022-04-14 11:46:19 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-04-14 11:46:19 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:19 STATE: test-node.js event: image 
-2022-04-14 11:46:19 STATE: test-node.js event: detect 
-2022-04-14 11:46:19 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-04-14 11:46:19 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:19 STATE: test-node.js event: image 
-2022-04-14 11:46:20 STATE: test-node.js event: detect 
-2022-04-14 11:46:20 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-04-14 11:46:20 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-04-14 11:46:20 STATE: test-node.js event: image 
-2022-04-14 11:46:20 STATE: test-node.js event: detect 
-2022-04-14 11:46:20 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-04-14 11:46:20 INFO:  test-node.js test default 
-2022-04-14 11:46:20 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:20 STATE: test-node.js event: image 
-2022-04-14 11:46:20 STATE: test-node.js event: detect 
-2022-04-14 11:46:20 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:20 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:20 DATA:  test-node.js  result: performance: load: null total: 247 
-2022-04-14 11:46:20 STATE: test-node.js passed: default result face match 1 female 0.97 
-2022-04-14 11:46:20 INFO:  test-node.js test sync 
-2022-04-14 11:46:21 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:21 STATE: test-node.js event: image 
-2022-04-14 11:46:21 STATE: test-node.js event: detect 
-2022-04-14 11:46:21 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:21 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:21 DATA:  test-node.js  result: performance: load: null total: 220 
-2022-04-14 11:46:21 STATE: test-node.js passed: default sync 1 female 0.97 
-2022-04-14 11:46:21 INFO:  test-node.js test: image process 
-2022-04-14 11:46:21 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:21 STATE: test-node.js passed: image input null [1,256,256,3] 
-2022-04-14 11:46:21 INFO:  test-node.js test: image null 
-2022-04-14 11:46:21 STATE: test-node.js passed: invalid input could not convert input to tensor 
-2022-04-14 11:46:21 INFO:  test-node.js test face similarity 
-2022-04-14 11:46:21 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:21 STATE: test-node.js event: image 
-2022-04-14 11:46:21 STATE: test-node.js event: detect 
-2022-04-14 11:46:21 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:21 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-14 11:46:21 DATA:  test-node.js  result: performance: load: null total: 211 
-2022-04-14 11:46:21 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:21 STATE: test-node.js event: image 
-2022-04-14 11:46:21 STATE: test-node.js event: detect 
-2022-04-14 11:46:21 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:21 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:21 DATA:  test-node.js  result: performance: load: null total: 226 
-2022-04-14 11:46:21 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-14 11:46:21 STATE: test-node.js event: image 
-2022-04-14 11:46:22 STATE: test-node.js event: detect 
-2022-04-14 11:46:22 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:22 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 3 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-04-14 11:46:22 DATA:  test-node.js  result: performance: load: null total: 202 
-2022-04-14 11:46:22 STATE: test-node.js passed: face descriptor 
-2022-04-14 11:46:22 STATE: test-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
-2022-04-14 11:46:22 INFO:  test-node.js test face matching 
-2022-04-14 11:46:22 STATE: test-node.js passed: face database 40 
-2022-04-14 11:46:22 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
-2022-04-14 11:46:22 INFO:  test-node.js test object 
-2022-04-14 11:46:22 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:22 STATE: test-node.js event: image 
-2022-04-14 11:46:22 STATE: test-node.js event: detect 
-2022-04-14 11:46:22 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:22 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:22 DATA:  test-node.js  result: performance: load: null total: 234 
-2022-04-14 11:46:22 STATE: test-node.js passed: object result match 
-2022-04-14 11:46:22 INFO:  test-node.js test sensitive 
-2022-04-14 11:46:22 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:22 STATE: test-node.js event: image 
-2022-04-14 11:46:23 STATE: test-node.js event: detect 
-2022-04-14 11:46:23 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:23 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:23 DATA:  test-node.js  result: performance: load: null total: 247 
-2022-04-14 11:46:23 STATE: test-node.js passed: sensitive result match 
-2022-04-14 11:46:23 STATE: test-node.js passed: sensitive face result match 
-2022-04-14 11:46:23 STATE: test-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-04-14 11:46:23 STATE: test-node.js passed: sensitive body result match 
-2022-04-14 11:46:23 STATE: test-node.js passed: sensitive hand result match 
-2022-04-14 11:46:23 INFO:  test-node.js test detectors 
-2022-04-14 11:46:23 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:23 STATE: test-node.js event: image 
-2022-04-14 11:46:23 STATE: test-node.js event: detect 
-2022-04-14 11:46:23 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:23 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:23 DATA:  test-node.js  result: performance: load: null total: 148 
-2022-04-14 11:46:23 STATE: test-node.js passed: detector result face match 
-2022-04-14 11:46:23 STATE: test-node.js passed: detector result hand match 
-2022-04-14 11:46:23 INFO:  test-node.js test: multi-instance 
-2022-04-14 11:46:23 STATE: test-node.js event: image 
-2022-04-14 11:46:23 STATE: test-node.js event: detect 
-2022-04-14 11:46:23 STATE: test-node.js passed: detect: random default 
-2022-04-14 11:46:23 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.08,"keypoints":15} 
-2022-04-14 11:46:23 DATA:  test-node.js  result: performance: load: null total: 150 
-2022-04-14 11:46:23 INFO:  test-node.js test: first instance 
-2022-04-14 11:46:23 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-14 11:46:23 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:23 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:23 DATA:  test-node.js  result: performance: load: null total: 144 
-2022-04-14 11:46:23 INFO:  test-node.js test: second instance 
-2022-04-14 11:46:23 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-14 11:46:24 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:24 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:24 DATA:  test-node.js  result: performance: load: null total: 142 
-2022-04-14 11:46:24 INFO:  test-node.js test: concurrent 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:24 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-04-14 11:46:24 STATE: test-node.js event: image 
-2022-04-14 11:46:24 STATE: test-node.js event: image 
-2022-04-14 11:46:24 STATE: test-node.js event: image 
-2022-04-14 11:46:25 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:25 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:25 DATA:  test-node.js  result: performance: load: null total: 1431 
-2022-04-14 11:46:25 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:25 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:25 DATA:  test-node.js  result: performance: load: null total: 1431 
-2022-04-14 11:46:26 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:26 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:26 DATA:  test-node.js  result: performance: load: null total: 1557 
-2022-04-14 11:46:26 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:26 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:26 DATA:  test-node.js  result: performance: load: null total: 1557 
-2022-04-14 11:46:26 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:26 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:26 DATA:  test-node.js  result: performance: load: null total: 1557 
-2022-04-14 11:46:26 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:26 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:26 DATA:  test-node.js  result: performance: load: null total: 1557 
-2022-04-14 11:46:26 STATE: test-node.js event: detect 
-2022-04-14 11:46:26 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:26 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:26 DATA:  test-node.js  result: performance: load: null total: 1298 
-2022-04-14 11:46:26 STATE: test-node.js event: detect 
-2022-04-14 11:46:26 STATE: test-node.js event: detect 
-2022-04-14 11:46:26 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:26 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:26 DATA:  test-node.js  result: performance: load: null total: 1298 
-2022-04-14 11:46:26 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:26 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:26 DATA:  test-node.js  result: performance: load: null total: 1298 
-2022-04-14 11:46:26 INFO:  test-node.js test: monkey-patch 
-2022-04-14 11:46:26 STATE: test-node.js event: image 
-2022-04-14 11:46:26 STATE: test-node.js event: detect 
-2022-04-14 11:46:26 STATE: test-node.js passed: monkey patch 
-2022-04-14 11:46:26 STATE: test-node.js passed: segmentation [65536] 
-2022-04-14 11:46:26 STATE: test-node.js passeed: equal usage 
-2022-04-14 11:46:26 INFO:  test-node.js test: input compare 
-2022-04-14 11:46:26 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:26 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-04-14 11:46:26 STATE: test-node.js passed: image compare 0 23.275441687091504 
-2022-04-14 11:46:26 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
-2022-04-14 11:46:26 INFO:  test-node.js tensors 1925 
-2022-04-14 11:46:26 INFO:  test-node.js test complete: 9574 ms 
-2022-04-14 11:46:26 INFO:  
-2022-04-14 11:46:26 INFO:  test-node-gpu.js start 
-2022-04-14 11:46:27 INFO:  test-node-gpu.js test: configuration validation 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: configuration default validation [] 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-04-14 11:46:27 INFO:  test-node-gpu.js test: model load 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-04-14 11:46:27 INFO:  test-node-gpu.js test: warmup 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: create human 
-2022-04-14 11:46:27 INFO:  test-node-gpu.js human version: 2.7.0 
-2022-04-14 11:46:27 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-04-14 11:46:27 INFO:  test-node-gpu.js tfjs version: 3.15.0 
-2022-04-14 11:46:27 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: set backend: tensorflow 
-2022-04-14 11:46:27 STATE: test-node-gpu.js tensors 1919 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: load models 
-2022-04-14 11:46:27 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: warmup: none default 
-2022-04-14 11:46:27 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-04-14 11:46:27 DATA:  test-node-gpu.js  result: performance: load: null total: null 
-2022-04-14 11:46:27 STATE: test-node-gpu.js passed: warmup none result match 
-2022-04-14 11:46:27 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:30 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:30 STATE: test-node-gpu.js event: warmup 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: warmup: face default 
-2022-04-14 11:46:30 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-04-14 11:46:30 DATA:  test-node-gpu.js  result: performance: load: null total: 2399 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: warmup face result match 
-2022-04-14 11:46:30 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:30 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:30 STATE: test-node-gpu.js event: warmup 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: warmup: body default 
-2022-04-14 11:46:30 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:30 DATA:  test-node-gpu.js  result: performance: load: null total: 205 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: warmup body result match 
-2022-04-14 11:46:30 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking right"},{"iris":0,"gesture":"looking up"}]} 
-2022-04-14 11:46:30 INFO:  test-node-gpu.js test: details verification 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:30 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:30 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:30 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:30 DATA:  test-node-gpu.js  result: performance: load: null total: 161 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details face length 1 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details face age/gender 23.7 female 0.97 73.26 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details face anti-spoofing 0.79 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details face liveness 0.83 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details body length 1 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details body 0.92 17 6 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details hand length 1 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details gesture length 6 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details object length 1 
-2022-04-14 11:46:30 STATE: test-node-gpu.js passed: details object 0.72 person 
-2022-04-14 11:46:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-04-14 11:46:31 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:31 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:31 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-04-14 11:46:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-04-14 11:46:31 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:31 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:31 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-04-14 11:46:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:31 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:31 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:31 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-04-14 11:46:32 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:32 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:32 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:32 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-04-14 11:46:32 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-04-14 11:46:32 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:32 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:32 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-04-14 11:46:32 INFO:  test-node-gpu.js test default 
-2022-04-14 11:46:32 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:32 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:32 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:32 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:32 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:32 DATA:  test-node-gpu.js  result: performance: load: null total: 144 
-2022-04-14 11:46:32 STATE: test-node-gpu.js passed: default result face match 1 female 0.97 
-2022-04-14 11:46:32 INFO:  test-node-gpu.js test sync 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:33 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:33 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:33 DATA:  test-node-gpu.js  result: performance: load: null total: 162 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: default sync 1 female 0.97 
-2022-04-14 11:46:33 INFO:  test-node-gpu.js test: image process 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
-2022-04-14 11:46:33 INFO:  test-node-gpu.js test: image null 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
-2022-04-14 11:46:33 INFO:  test-node-gpu.js test face similarity 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:33 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:33 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-14 11:46:33 DATA:  test-node-gpu.js  result: performance: load: null total: 147 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:33 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:33 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:33 DATA:  test-node-gpu.js  result: performance: load: null total: 199 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-14 11:46:34 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:34 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:34 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 3 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-04-14 11:46:34 DATA:  test-node-gpu.js  result: performance: load: null total: 140 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: face descriptor 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
-2022-04-14 11:46:34 INFO:  test-node-gpu.js test face matching 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: face database 40 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7828184453007331}} {"second":{"index":4,"similarity":0.5001334216773398}} {"third":{"index":4,"similarity":0.5403054967489764}} 
-2022-04-14 11:46:34 INFO:  test-node-gpu.js test object 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:34 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:34 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:34 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:34 DATA:  test-node-gpu.js  result: performance: load: null total: 151 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: object result match 
-2022-04-14 11:46:34 INFO:  test-node-gpu.js test sensitive 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:34 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:34 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:34 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:34 DATA:  test-node-gpu.js  result: performance: load: null total: 180 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: sensitive result match 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: sensitive face result match 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: sensitive body result match 
-2022-04-14 11:46:34 STATE: test-node-gpu.js passed: sensitive hand result match 
-2022-04-14 11:46:34 INFO:  test-node-gpu.js test detectors 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:35 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:35 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: performance: load: null total: 79 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: detector result face match 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: detector result hand match 
-2022-04-14 11:46:35 INFO:  test-node-gpu.js test: multi-instance 
-2022-04-14 11:46:35 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:35 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: detect: random default 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: performance: load: null total: 80 
-2022-04-14 11:46:35 INFO:  test-node-gpu.js test: first instance 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: performance: load: null total: 87 
-2022-04-14 11:46:35 INFO:  test-node-gpu.js test: second instance 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:35 DATA:  test-node-gpu.js  result: performance: load: null total: 92 
-2022-04-14 11:46:35 INFO:  test-node-gpu.js test: concurrent 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:35 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:36 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:36 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-14 11:46:36 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-14 11:46:36 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:36 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:36 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-04-14 11:46:36 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:36 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:36 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 990 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 990 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 1048 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 1048 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 1048 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 1048 
-2022-04-14 11:46:37 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 771 
-2022-04-14 11:46:37 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:37 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 771 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:37 DATA:  test-node-gpu.js  result: performance: load: null total: 771 
-2022-04-14 11:46:37 INFO:  test-node-gpu.js test: monkey-patch 
-2022-04-14 11:46:37 STATE: test-node-gpu.js event: image 
-2022-04-14 11:46:37 STATE: test-node-gpu.js event: detect 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: monkey patch 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: segmentation [65536] 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passeed: equal usage 
-2022-04-14 11:46:37 INFO:  test-node-gpu.js test: input compare 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-04-14 11:46:37 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
-2022-04-14 11:46:37 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
-2022-04-14 11:46:37 INFO:  test-node-gpu.js tensors 1925 
-2022-04-14 11:46:37 INFO:  test-node-gpu.js test complete: 10573 ms 
-2022-04-14 11:46:38 INFO:  
-2022-04-14 11:46:38 INFO:  test-node-wasm.js start 
-2022-04-14 11:46:39 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
-2022-04-14 11:46:39 INFO:  test-node-wasm.js test: configuration validation 
-2022-04-14 11:46:39 STATE: test-node-wasm.js passed: configuration default validation [] 
-2022-04-14 11:46:39 STATE: test-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-04-14 11:46:39 INFO:  test-node-wasm.js test: model load 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-04-14 11:46:40 INFO:  test-node-wasm.js test: warmup 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: create human 
-2022-04-14 11:46:40 INFO:  test-node-wasm.js human version: 2.7.0 
-2022-04-14 11:46:40 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-04-14 11:46:40 INFO:  test-node-wasm.js tfjs version: 3.15.0 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: set backend: wasm 
-2022-04-14 11:46:40 STATE: test-node-wasm.js tensors 1919 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: load models 
-2022-04-14 11:46:40 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: warmup: none default 
-2022-04-14 11:46:40 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-04-14 11:46:40 DATA:  test-node-wasm.js  result: performance: load: null total: null 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: warmup none result match 
-2022-04-14 11:46:40 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:40 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:40 STATE: test-node-wasm.js event: warmup 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: warmup: face default 
-2022-04-14 11:46:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-14 11:46:40 DATA:  test-node-wasm.js  result: performance: load: null total: 616 
-2022-04-14 11:46:40 STATE: test-node-wasm.js passed: warmup face result match 
-2022-04-14 11:46:40 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:41 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:41 STATE: test-node-wasm.js event: warmup 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: warmup: body default 
-2022-04-14 11:46:41 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:41 DATA:  test-node-wasm.js  result: performance: load: null total: 451 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: warmup body result match 
-2022-04-14 11:46:41 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-04-14 11:46:41 INFO:  test-node-wasm.js test: details verification 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:41 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:41 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:41 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:41 DATA:  test-node-wasm.js  result: performance: load: null total: 386 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details face length 1 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details face age/gender 23.7 female 0.97 73.26 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details face anti-spoofing 0.79 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details face liveness 0.83 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details body length 1 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details body 0.92 17 6 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details hand length 1 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details gesture length 6 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details object length 1 
-2022-04-14 11:46:41 STATE: test-node-wasm.js passed: details object 0.72 person 
-2022-04-14 11:46:42 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
-2022-04-14 11:46:42 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:42 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:42 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-04-14 11:46:42 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
-2022-04-14 11:46:42 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:43 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:43 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-04-14 11:46:43 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:43 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:43 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:43 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-04-14 11:46:44 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:44 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:44 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:44 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-04-14 11:46:44 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-04-14 11:46:44 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:45 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:45 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-04-14 11:46:45 INFO:  test-node-wasm.js test default 
-2022-04-14 11:46:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:45 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:45 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:45 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:45 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:45 DATA:  test-node-wasm.js  result: performance: load: null total: 388 
-2022-04-14 11:46:45 STATE: test-node-wasm.js passed: default result face match 1 female 0.97 
-2022-04-14 11:46:45 INFO:  test-node-wasm.js test sync 
-2022-04-14 11:46:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:45 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:46 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:46 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:46 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:46 DATA:  test-node-wasm.js  result: performance: load: null total: 383 
-2022-04-14 11:46:46 STATE: test-node-wasm.js passed: default sync 1 female 0.97 
-2022-04-14 11:46:46 INFO:  test-node-wasm.js test: image process 
-2022-04-14 11:46:46 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-14 11:46:46 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
-2022-04-14 11:46:46 INFO:  test-node-wasm.js test: image null 
-2022-04-14 11:46:46 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
-2022-04-14 11:46:46 INFO:  test-node-wasm.js test face similarity 
-2022-04-14 11:46:46 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-14 11:46:46 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:46 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:46 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:46 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-04-14 11:46:46 DATA:  test-node-wasm.js  result: performance: load: null total: 367 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:47 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:47 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:47 DATA:  test-node-wasm.js  result: performance: load: null total: 390 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-14 11:46:47 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:47 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 3 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-04-14 11:46:47 DATA:  test-node-wasm.js  result: performance: load: null total: 346 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: face descriptor 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
-2022-04-14 11:46:47 INFO:  test-node-wasm.js test face matching 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: face database 40 
-2022-04-14 11:46:47 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
-2022-04-14 11:46:47 INFO:  test-node-wasm.js test object 
-2022-04-14 11:46:48 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:48 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:48 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:48 DATA:  test-node-wasm.js  result: performance: load: null total: 358 
-2022-04-14 11:46:48 STATE: test-node-wasm.js passed: object result match 
-2022-04-14 11:46:48 INFO:  test-node-wasm.js test sensitive 
-2022-04-14 11:46:48 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:48 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:49 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:49 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:49 DATA:  test-node-wasm.js  result: performance: load: null total: 424 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: sensitive result match 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: sensitive face result match 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: sensitive body result match 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: sensitive hand result match 
-2022-04-14 11:46:49 INFO:  test-node-wasm.js test detectors 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:49 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:49 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:49 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:49 DATA:  test-node-wasm.js  result: performance: load: null total: 279 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: detector result face match 
-2022-04-14 11:46:49 STATE: test-node-wasm.js passed: detector result hand match 
-2022-04-14 11:46:49 INFO:  test-node-wasm.js test: multi-instance 
-2022-04-14 11:46:49 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:50 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:50 STATE: test-node-wasm.js passed: detect: random default 
-2022-04-14 11:46:50 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-04-14 11:46:50 DATA:  test-node-wasm.js  result: performance: load: null total: 244 
-2022-04-14 11:46:50 INFO:  test-node-wasm.js test: first instance 
-2022-04-14 11:46:50 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-14 11:46:50 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:50 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:50 DATA:  test-node-wasm.js  result: performance: load: null total: 269 
-2022-04-14 11:46:50 INFO:  test-node-wasm.js test: second instance 
-2022-04-14 11:46:50 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-14 11:46:50 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:50 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:50 DATA:  test-node-wasm.js  result: performance: load: null total: 261 
-2022-04-14 11:46:50 INFO:  test-node-wasm.js test: concurrent 
-2022-04-14 11:46:50 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-14 11:46:50 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-04-14 11:46:51 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:51 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:51 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:53 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:53 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:53 DATA:  test-node-wasm.js  result: performance: load: null total: 2324 
-2022-04-14 11:46:53 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:53 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:53 DATA:  test-node-wasm.js  result: performance: load: null total: 2324 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: performance: load: null total: 2584 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: performance: load: null total: 2584 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: performance: load: null total: 2584 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: performance: load: null total: 2584 
-2022-04-14 11:46:54 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: performance: load: null total: 2247 
-2022-04-14 11:46:54 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:54 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: performance: load: null total: 2247 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-04-14 11:46:54 DATA:  test-node-wasm.js  result: performance: load: null total: 2247 
-2022-04-14 11:46:54 INFO:  test-node-wasm.js test: monkey-patch 
-2022-04-14 11:46:54 STATE: test-node-wasm.js event: image 
-2022-04-14 11:46:54 STATE: test-node-wasm.js event: detect 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: monkey patch 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: segmentation [65536] 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passeed: equal usage 
-2022-04-14 11:46:54 INFO:  test-node-wasm.js test: input compare 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-04-14 11:46:54 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
-2022-04-14 11:46:54 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
-2022-04-14 11:46:54 INFO:  test-node-wasm.js tensors 1927 
-2022-04-14 11:46:54 INFO:  test-node-wasm.js test complete: 15539 ms 
-2022-04-14 11:46:54 INFO:  all tests complete 
-2022-04-14 11:46:54 INFO:  failed: {"count":0,"messages":[]} 
-2022-04-14 11:46:54 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
-2022-04-14 11:46:54 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
-2022-04-14 11:46:54 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
+2022-04-14 11:54:51 INFO:  @vladmandic/human version 2.7.0 
+2022-04-14 11:54:51 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.4.0 
+2022-04-14 11:54:51 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
+2022-04-14 11:54:51 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
+2022-04-14 11:54:51 INFO:  
+2022-04-14 11:54:51 INFO:  test-node.js start 
+2022-04-14 11:54:51 INFO:  test-node.js test: configuration validation 
+2022-04-14 11:54:51 STATE: test-node.js passed: configuration default validation [] 
+2022-04-14 11:54:51 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-04-14 11:54:51 INFO:  test-node.js test: model load 
+2022-04-14 11:54:51 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-04-14 11:54:51 INFO:  test-node.js test: warmup 
+2022-04-14 11:54:51 STATE: test-node.js passed: create human 
+2022-04-14 11:54:51 INFO:  test-node.js human version: 2.7.0 
+2022-04-14 11:54:51 INFO:  test-node.js platform: linux x64 agent: NodeJS v17.4.0 
+2022-04-14 11:54:51 INFO:  test-node.js tfjs version: 3.15.0 
+2022-04-14 11:54:51 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
+2022-04-14 11:54:51 STATE: test-node.js passed: set backend: tensorflow 
+2022-04-14 11:54:51 STATE: test-node.js tensors 1919 
+2022-04-14 11:54:51 STATE: test-node.js passed: load models 
+2022-04-14 11:54:51 STATE: test-node.js  result: defined models: 22 loaded models: 12 
+2022-04-14 11:54:51 STATE: test-node.js passed: warmup: none default 
+2022-04-14 11:54:51 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-04-14 11:54:51 DATA:  test-node.js  result: performance: load: null total: null 
+2022-04-14 11:54:51 STATE: test-node.js passed: warmup none result match 
+2022-04-14 11:54:51 STATE: test-node.js event: image 
+2022-04-14 11:54:52 STATE: test-node.js event: detect 
+2022-04-14 11:54:52 STATE: test-node.js event: warmup 
+2022-04-14 11:54:52 STATE: test-node.js passed: warmup: face default 
+2022-04-14 11:54:52 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-04-14 11:54:52 DATA:  test-node.js  result: performance: load: null total: 347 
+2022-04-14 11:54:52 STATE: test-node.js passed: warmup face result match 
+2022-04-14 11:54:52 STATE: test-node.js event: image 
+2022-04-14 11:54:52 STATE: test-node.js event: detect 
+2022-04-14 11:54:52 STATE: test-node.js event: warmup 
+2022-04-14 11:54:52 STATE: test-node.js passed: warmup: body default 
+2022-04-14 11:54:52 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:52 DATA:  test-node.js  result: performance: load: null total: 268 
+2022-04-14 11:54:52 STATE: test-node.js passed: warmup body result match 
+2022-04-14 11:54:52 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking right"},{"iris":0,"gesture":"looking up"}]} 
+2022-04-14 11:54:52 INFO:  test-node.js test: details verification 
+2022-04-14 11:54:52 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:52 STATE: test-node.js event: image 
+2022-04-14 11:54:53 STATE: test-node.js event: detect 
+2022-04-14 11:54:53 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:54:53 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:53 DATA:  test-node.js  result: performance: load: null total: 252 
+2022-04-14 11:54:53 STATE: test-node.js passed: details face length 1 
+2022-04-14 11:54:53 STATE: test-node.js passed: details face score 1 0.93 1 
+2022-04-14 11:54:53 STATE: test-node.js passed: details face age/gender 23.7 female 0.97 73.26 
+2022-04-14 11:54:53 STATE: test-node.js passed: details face arrays 4 478 1024 
+2022-04-14 11:54:53 STATE: test-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-04-14 11:54:53 STATE: test-node.js passed: details face anti-spoofing 0.79 
+2022-04-14 11:54:53 STATE: test-node.js passed: details face liveness 0.83 
+2022-04-14 11:54:53 STATE: test-node.js passed: details body length 1 
+2022-04-14 11:54:53 STATE: test-node.js passed: details body 0.92 17 6 
+2022-04-14 11:54:53 STATE: test-node.js passed: details hand length 1 
+2022-04-14 11:54:53 STATE: test-node.js passed: details hand 0.51 0.73 point 
+2022-04-14 11:54:53 STATE: test-node.js passed: details hand arrays 21 5 7 
+2022-04-14 11:54:53 STATE: test-node.js passed: details gesture length 6 
+2022-04-14 11:54:53 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-04-14 11:54:53 STATE: test-node.js passed: details object length 1 
+2022-04-14 11:54:53 STATE: test-node.js passed: details object 0.72 person 
+2022-04-14 11:54:53 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-04-14 11:54:53 STATE: test-node.js event: image 
+2022-04-14 11:54:53 STATE: test-node.js event: detect 
+2022-04-14 11:54:53 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-04-14 11:54:53 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-04-14 11:54:53 STATE: test-node.js event: image 
+2022-04-14 11:54:54 STATE: test-node.js event: detect 
+2022-04-14 11:54:54 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-04-14 11:54:54 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:54 STATE: test-node.js event: image 
+2022-04-14 11:54:54 STATE: test-node.js event: detect 
+2022-04-14 11:54:54 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-04-14 11:54:54 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:54 STATE: test-node.js event: image 
+2022-04-14 11:54:54 STATE: test-node.js event: detect 
+2022-04-14 11:54:54 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-04-14 11:54:55 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-04-14 11:54:55 STATE: test-node.js event: image 
+2022-04-14 11:54:55 STATE: test-node.js event: detect 
+2022-04-14 11:54:55 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-04-14 11:54:55 INFO:  test-node.js test default 
+2022-04-14 11:54:55 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:55 STATE: test-node.js event: image 
+2022-04-14 11:54:55 STATE: test-node.js event: detect 
+2022-04-14 11:54:55 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:54:55 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:55 DATA:  test-node.js  result: performance: load: null total: 254 
+2022-04-14 11:54:55 STATE: test-node.js passed: default result face match 1 female 0.97 
+2022-04-14 11:54:55 INFO:  test-node.js test sync 
+2022-04-14 11:54:56 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:56 STATE: test-node.js event: image 
+2022-04-14 11:54:56 STATE: test-node.js event: detect 
+2022-04-14 11:54:56 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:54:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:56 DATA:  test-node.js  result: performance: load: null total: 237 
+2022-04-14 11:54:56 STATE: test-node.js passed: default sync 1 female 0.97 
+2022-04-14 11:54:56 INFO:  test-node.js test: image process 
+2022-04-14 11:54:56 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:54:56 STATE: test-node.js passed: image input null [1,256,256,3] 
+2022-04-14 11:54:56 INFO:  test-node.js test: image null 
+2022-04-14 11:54:56 STATE: test-node.js passed: invalid input could not convert input to tensor 
+2022-04-14 11:54:56 INFO:  test-node.js test face similarity 
+2022-04-14 11:54:56 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:54:56 STATE: test-node.js event: image 
+2022-04-14 11:54:56 STATE: test-node.js event: detect 
+2022-04-14 11:54:56 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:54:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-04-14 11:54:56 DATA:  test-node.js  result: performance: load: null total: 217 
+2022-04-14 11:54:56 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:56 STATE: test-node.js event: image 
+2022-04-14 11:54:56 STATE: test-node.js event: detect 
+2022-04-14 11:54:56 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:54:56 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:56 DATA:  test-node.js  result: performance: load: null total: 223 
+2022-04-14 11:54:56 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-14 11:54:56 STATE: test-node.js event: image 
+2022-04-14 11:54:57 STATE: test-node.js event: detect 
+2022-04-14 11:54:57 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:54:57 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 3 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-04-14 11:54:57 DATA:  test-node.js  result: performance: load: null total: 197 
+2022-04-14 11:54:57 STATE: test-node.js passed: face descriptor 
+2022-04-14 11:54:57 STATE: test-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
+2022-04-14 11:54:57 INFO:  test-node.js test face matching 
+2022-04-14 11:54:57 STATE: test-node.js passed: face database 40 
+2022-04-14 11:54:57 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
+2022-04-14 11:54:57 INFO:  test-node.js test object 
+2022-04-14 11:54:57 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:57 STATE: test-node.js event: image 
+2022-04-14 11:54:57 STATE: test-node.js event: detect 
+2022-04-14 11:54:57 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:54:57 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:57 DATA:  test-node.js  result: performance: load: null total: 218 
+2022-04-14 11:54:57 STATE: test-node.js passed: object result match 
+2022-04-14 11:54:57 INFO:  test-node.js test sensitive 
+2022-04-14 11:54:57 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:57 STATE: test-node.js event: image 
+2022-04-14 11:54:57 STATE: test-node.js event: detect 
+2022-04-14 11:54:57 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:54:57 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:57 DATA:  test-node.js  result: performance: load: null total: 242 
+2022-04-14 11:54:57 STATE: test-node.js passed: sensitive result match 
+2022-04-14 11:54:57 STATE: test-node.js passed: sensitive face result match 
+2022-04-14 11:54:57 STATE: test-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2022-04-14 11:54:57 STATE: test-node.js passed: sensitive body result match 
+2022-04-14 11:54:57 STATE: test-node.js passed: sensitive hand result match 
+2022-04-14 11:54:57 INFO:  test-node.js test detectors 
+2022-04-14 11:54:58 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:58 STATE: test-node.js event: image 
+2022-04-14 11:54:58 STATE: test-node.js event: detect 
+2022-04-14 11:54:58 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:54:58 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:54:58 DATA:  test-node.js  result: performance: load: null total: 154 
+2022-04-14 11:54:58 STATE: test-node.js passed: detector result face match 
+2022-04-14 11:54:58 STATE: test-node.js passed: detector result hand match 
+2022-04-14 11:54:58 INFO:  test-node.js test: multi-instance 
+2022-04-14 11:54:58 STATE: test-node.js event: image 
+2022-04-14 11:54:58 STATE: test-node.js event: detect 
+2022-04-14 11:54:58 STATE: test-node.js passed: detect: random default 
+2022-04-14 11:54:58 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-04-14 11:54:58 DATA:  test-node.js  result: performance: load: null total: 150 
+2022-04-14 11:54:58 INFO:  test-node.js test: first instance 
+2022-04-14 11:54:58 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-14 11:54:58 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:54:58 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:54:58 DATA:  test-node.js  result: performance: load: null total: 146 
+2022-04-14 11:54:58 INFO:  test-node.js test: second instance 
+2022-04-14 11:54:58 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-14 11:54:58 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:54:58 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:54:58 DATA:  test-node.js  result: performance: load: null total: 148 
+2022-04-14 11:54:58 INFO:  test-node.js test: concurrent 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:54:59 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-04-14 11:54:59 STATE: test-node.js event: image 
+2022-04-14 11:54:59 STATE: test-node.js event: image 
+2022-04-14 11:54:59 STATE: test-node.js event: image 
+2022-04-14 11:55:00 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:00 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:00 DATA:  test-node.js  result: performance: load: null total: 1456 
+2022-04-14 11:55:00 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:00 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:00 DATA:  test-node.js  result: performance: load: null total: 1456 
+2022-04-14 11:55:01 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:01 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-14 11:55:01 DATA:  test-node.js  result: performance: load: null total: 1577 
+2022-04-14 11:55:01 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:01 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-14 11:55:01 DATA:  test-node.js  result: performance: load: null total: 1577 
+2022-04-14 11:55:01 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:01 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:01 DATA:  test-node.js  result: performance: load: null total: 1577 
+2022-04-14 11:55:01 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:01 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:01 DATA:  test-node.js  result: performance: load: null total: 1577 
+2022-04-14 11:55:01 STATE: test-node.js event: detect 
+2022-04-14 11:55:01 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:01 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:01 DATA:  test-node.js  result: performance: load: null total: 1299 
+2022-04-14 11:55:01 STATE: test-node.js event: detect 
+2022-04-14 11:55:01 STATE: test-node.js event: detect 
+2022-04-14 11:55:01 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:01 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-14 11:55:01 DATA:  test-node.js  result: performance: load: null total: 1299 
+2022-04-14 11:55:01 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:01 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:01 DATA:  test-node.js  result: performance: load: null total: 1299 
+2022-04-14 11:55:01 INFO:  test-node.js test: monkey-patch 
+2022-04-14 11:55:01 STATE: test-node.js event: image 
+2022-04-14 11:55:01 STATE: test-node.js event: detect 
+2022-04-14 11:55:01 STATE: test-node.js passed: monkey patch 
+2022-04-14 11:55:01 STATE: test-node.js passed: segmentation [65536] 
+2022-04-14 11:55:01 STATE: test-node.js passeed: equal usage 
+2022-04-14 11:55:01 INFO:  test-node.js test: input compare 
+2022-04-14 11:55:01 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:55:01 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-04-14 11:55:01 STATE: test-node.js passed: image compare 0 23.275441687091504 
+2022-04-14 11:55:01 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
+2022-04-14 11:55:01 INFO:  test-node.js tensors 1925 
+2022-04-14 11:55:01 INFO:  test-node.js test complete: 9683 ms 
+2022-04-14 11:55:01 INFO:  
+2022-04-14 11:55:01 INFO:  test-node-gpu.js start 
+2022-04-14 11:55:02 INFO:  test-node-gpu.js test: configuration validation 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: configuration default validation [] 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-04-14 11:55:02 INFO:  test-node-gpu.js test: model load 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-04-14 11:55:02 INFO:  test-node-gpu.js test: warmup 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: create human 
+2022-04-14 11:55:02 INFO:  test-node-gpu.js human version: 2.7.0 
+2022-04-14 11:55:02 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v17.4.0 
+2022-04-14 11:55:02 INFO:  test-node-gpu.js tfjs version: 3.15.0 
+2022-04-14 11:55:02 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: set backend: tensorflow 
+2022-04-14 11:55:02 STATE: test-node-gpu.js tensors 1919 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: load models 
+2022-04-14 11:55:02 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: warmup: none default 
+2022-04-14 11:55:02 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-04-14 11:55:02 DATA:  test-node-gpu.js  result: performance: load: null total: null 
+2022-04-14 11:55:02 STATE: test-node-gpu.js passed: warmup none result match 
+2022-04-14 11:55:03 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:06 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:06 STATE: test-node-gpu.js event: warmup 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: warmup: face default 
+2022-04-14 11:55:06 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-04-14 11:55:06 DATA:  test-node-gpu.js  result: performance: load: null total: 2965 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: warmup face result match 
+2022-04-14 11:55:06 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:06 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:06 STATE: test-node-gpu.js event: warmup 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: warmup: body default 
+2022-04-14 11:55:06 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:06 DATA:  test-node-gpu.js  result: performance: load: null total: 247 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: warmup body result match 
+2022-04-14 11:55:06 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking right"},{"iris":0,"gesture":"looking up"}]} 
+2022-04-14 11:55:06 INFO:  test-node-gpu.js test: details verification 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-14 11:55:06 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:06 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:06 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:06 DATA:  test-node-gpu.js  result: performance: load: null total: 202 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details face length 1 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details face age/gender 23.7 female 0.97 73.26 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details face anti-spoofing 0.79 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details face liveness 0.83 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details body length 1 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details body 0.92 17 6 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details hand length 1 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details gesture length 6 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details object length 1 
+2022-04-14 11:55:06 STATE: test-node-gpu.js passed: details object 0.72 person 
+2022-04-14 11:55:07 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-04-14 11:55:07 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:07 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:07 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-04-14 11:55:07 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-04-14 11:55:07 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:07 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:07 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-04-14 11:55:07 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-14 11:55:07 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:08 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:08 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-04-14 11:55:08 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
+2022-04-14 11:55:08 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:08 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:08 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-04-14 11:55:08 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-04-14 11:55:08 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:08 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:08 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-04-14 11:55:08 INFO:  test-node-gpu.js test default 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-14 11:55:09 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:09 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:09 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:09 DATA:  test-node-gpu.js  result: performance: load: null total: 223 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: default result face match 1 female 0.97 
+2022-04-14 11:55:09 INFO:  test-node-gpu.js test sync 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-14 11:55:09 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:09 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:09 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:09 DATA:  test-node-gpu.js  result: performance: load: null total: 182 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: default sync 1 female 0.97 
+2022-04-14 11:55:09 INFO:  test-node-gpu.js test: image process 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
+2022-04-14 11:55:09 INFO:  test-node-gpu.js test: image null 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
+2022-04-14 11:55:09 INFO:  test-node-gpu.js test face similarity 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-04-14 11:55:09 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:09 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:09 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:09 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-04-14 11:55:09 DATA:  test-node-gpu.js  result: performance: load: null total: 175 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-14 11:55:10 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:10 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:10 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:10 DATA:  test-node-gpu.js  result: performance: load: null total: 182 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-04-14 11:55:10 STATE: test-node-gpu.js event: image 
+2022-04-14 11:55:10 STATE: test-node-gpu.js event: detect 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:10 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 3 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-04-14 11:55:10 DATA:  test-node-gpu.js  result: performance: load: null total: 175 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: face descriptor 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
+2022-04-14 11:55:10 INFO:  test-node-gpu.js test face matching 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: face database 40 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7828184453007331}} {"second":{"index":4,"similarity":0.5001334216773398}} {"third":{"index":4,"similarity":0.5403054967489764}} 
+2022-04-14 11:55:10 INFO:  test-node-gpu.js test object 
+2022-04-14 11:55:10 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-04-14 11:55:10 STATE: test-node-gpu.js event: image 
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+2022-04-14 11:55:11 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
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+2022-04-14 11:55:15 INFO:  
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+2022-04-14 11:55:16 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
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+2022-04-14 11:55:17 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
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+2022-04-14 11:55:17 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-04-14 11:55:17 INFO:  test-node-wasm.js test: details verification 
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+2022-04-14 11:55:18 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:18 DATA:  test-node-wasm.js  result: performance: load: null total: 392 
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+2022-04-14 11:55:18 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
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+2022-04-14 11:55:18 STATE: test-node-wasm.js passed: details body 0.92 17 6 
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+2022-04-14 11:55:20 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
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+2022-04-14 11:55:22 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:22 DATA:  test-node-wasm.js  result: performance: load: null total: 361 
+2022-04-14 11:55:22 STATE: test-node-wasm.js passed: default result face match 1 female 0.97 
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+2022-04-14 11:55:22 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
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+2022-04-14 11:55:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:23 DATA:  test-node-wasm.js  result: performance: load: null total: 383 
+2022-04-14 11:55:23 STATE: test-node-wasm.js passed: default sync 1 female 0.97 
+2022-04-14 11:55:23 INFO:  test-node-wasm.js test: image process 
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+2022-04-14 11:55:23 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
+2022-04-14 11:55:23 INFO:  test-node-wasm.js test: image null 
+2022-04-14 11:55:23 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
+2022-04-14 11:55:23 INFO:  test-node-wasm.js test face similarity 
+2022-04-14 11:55:23 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-14 11:55:23 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:23 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:23 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:23 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-04-14 11:55:23 DATA:  test-node-wasm.js  result: performance: load: null total: 348 
+2022-04-14 11:55:23 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:23 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:24 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:24 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:24 DATA:  test-node-wasm.js  result: performance: load: null total: 377 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-14 11:55:24 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:24 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:24 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 3 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-04-14 11:55:24 DATA:  test-node-wasm.js  result: performance: load: null total: 326 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: face descriptor 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
+2022-04-14 11:55:24 INFO:  test-node-wasm.js test face matching 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: face database 40 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
+2022-04-14 11:55:24 INFO:  test-node-wasm.js test object 
+2022-04-14 11:55:24 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:24 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:25 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:25 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:25 DATA:  test-node-wasm.js  result: performance: load: null total: 370 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: object result match 
+2022-04-14 11:55:25 INFO:  test-node-wasm.js test sensitive 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:25 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:25 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:25 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:25 DATA:  test-node-wasm.js  result: performance: load: null total: 390 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: sensitive result match 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: sensitive face result match 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: sensitive body result match 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: sensitive hand result match 
+2022-04-14 11:55:25 INFO:  test-node-wasm.js test detectors 
+2022-04-14 11:55:25 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:25 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:26 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:26 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:26 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:26 DATA:  test-node-wasm.js  result: performance: load: null total: 259 
+2022-04-14 11:55:26 STATE: test-node-wasm.js passed: detector result face match 
+2022-04-14 11:55:26 STATE: test-node-wasm.js passed: detector result hand match 
+2022-04-14 11:55:26 INFO:  test-node-wasm.js test: multi-instance 
+2022-04-14 11:55:26 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:26 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:26 STATE: test-node-wasm.js passed: detect: random default 
+2022-04-14 11:55:26 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-04-14 11:55:26 DATA:  test-node-wasm.js  result: performance: load: null total: 222 
+2022-04-14 11:55:26 INFO:  test-node-wasm.js test: first instance 
+2022-04-14 11:55:26 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-14 11:55:26 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:26 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:26 DATA:  test-node-wasm.js  result: performance: load: null total: 248 
+2022-04-14 11:55:26 INFO:  test-node-wasm.js test: second instance 
+2022-04-14 11:55:26 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:27 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:27 DATA:  test-node-wasm.js  result: performance: load: null total: 266 
+2022-04-14 11:55:27 INFO:  test-node-wasm.js test: concurrent 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-14 11:55:27 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-14 11:55:28 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:28 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-04-14 11:55:28 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:28 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:28 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2408 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2408 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2650 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2650 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2650 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2650 
+2022-04-14 11:55:30 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2312 
+2022-04-14 11:55:30 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:30 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2312 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-04-14 11:55:30 DATA:  test-node-wasm.js  result: performance: load: null total: 2312 
+2022-04-14 11:55:30 INFO:  test-node-wasm.js test: monkey-patch 
+2022-04-14 11:55:30 STATE: test-node-wasm.js event: image 
+2022-04-14 11:55:30 STATE: test-node-wasm.js event: detect 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: monkey patch 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: segmentation [65536] 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passeed: equal usage 
+2022-04-14 11:55:30 INFO:  test-node-wasm.js test: input compare 
+2022-04-14 11:55:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-04-14 11:55:31 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-04-14 11:55:31 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
+2022-04-14 11:55:31 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
+2022-04-14 11:55:31 INFO:  test-node-wasm.js tensors 1927 
+2022-04-14 11:55:31 INFO:  test-node-wasm.js test complete: 15281 ms 
+2022-04-14 11:55:31 INFO:  all tests complete 
+2022-04-14 11:55:31 INFO:  failed: {"count":0,"messages":[]} 
+2022-04-14 11:55:31 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
+2022-04-14 11:55:31 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
+2022-04-14 11:55:31 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0}